Consolidated Mosaic

Consolidated Mosaic is a working name for a new mosaic paradigm that will be introduced in ERDAS IMAGINE 2010. Consider the different tools ERDAS, Inc. has for mosaicking images:

• ERDAS IMAGINE's MosaicTool (original mosaic tools, good defaults options)
• ERDAS MosaicPro (advanced MosaicTool, easy cutlines, ortho-correct from block files )
• ERDAS ER Mapper Mosaic (fast virtual mosaic, a lot of capacity, limited capability)
• ERDAS ER Mapper Color Balance (fast color balance, limited to true color)
• ERDAS Image Compressor (fast ECW and JPEG 2000 compression)
• ERDAS IMAGINE's MosaicDirect (Wizard to feed to MosaicPro and batch)
• ERDAS IMAGINE's MosaicWizard (Wizard to process mosaic)
• ERDAS IMAGINE's Virtual Mosaic (more capability than ER Mapper Mosaic, less capacity)

What if we combined these products in a single product? What if you could mosaic >2.5 terapixels of data straight into a single >2.5 terapixel IMG, or to a 20:1 compressed ECW, or to a lossless compressed JPEG2000 image. What if you could break that >2.5 terapixel mosaic into tiles with your shapefiles (and it has no temp files)? What if you could do all this within a 32-bit operating system environment?

If you think this can help you, keep your eyes open for a WebEx or an erdas labs discussion on this topic very soon.

I gave you a hint of where we were going when I asked Hammad to post to The Field Guide in: http://field-guide.blogspot.com/2009/02/benefits-of-64-bit-architecture-in.html

Web Demo of ERDAS Enterprise Server Products

Recently, ERDAS placed web demos of ERDAS Enterprise Server Products on the web for the world to play with. The data are from Cherokee County, Georgia. I believe the speed is incredible. Give it a look at: http://demo.erdas.com/

If you wish, compare to the online mapping of: http://www.richlandmaps.com/#mapping

Preview the Future of ERDAS IMAGINE and more on ERDAS Labs

This is going to be fun....

ERDAS Inc. announces the launch of ERDAS Labs, an informative new site highlighting technology currently being developed.

“ERDAS Labs provides the market and our customers a window into our product development activities; whether it’s a concept or idea we’re exploring, or a new feature under development for a product,” said Bruce Chaplin, Senior Vice President, Product Development, ERDAS. “We’ll showcase projects under active development, engaging our customers in conversations about these projects and soliciting their feedback.”

Whether it is an innovative new idea being explored or a major new feature being implemented for the next version of a product, ERDAS Labs provides a forum for discussion with the development team.

Visit http://labs.erdas.com/.

Vote Early and Vote Often

Please cast your vote for the image file formats you use.


Пожалуйста отдайте свой голос за формат или форматы, которые Вы чаще всего используете.

Para favor lance seu voto para os formatos de arquivo de imagem que você usa.

Vote para favor para los formatos de archivo de imagen que usted utiliza.

S'il vous plaît voter pour les formats de dossier d'image vous utilisez.

使用されている画像フォーマットの投票に参加してください。

请为你使用的图像文件格式投票。

Stem alstublieft op de raster formaten die u het meest gebruikt.

Bitte wählen Sie die Bildformate, mit welchen Sie arbeiten.

Per favore di lanciare il suo voto per i formati di file di immagine lei usa.

Behag støp din stemme for avbildene arkivene formatene som du bruker.


The "Vote Early and Vote Often" phrase in the US is a humorous way of saying, "Make sure you vote, and vote in each election." It appears I am saying, "Vote early in the morning and vote many times during the same election." That is illegal and morally wrong. I would be disappointed in someone who would do that.

Scanned Aerial Photo Pixel Size Determination

Many GIS people are collecting historical aerial photos to understand the changes in their areas of responsibility. I became interested in historical aerial photos when a student of Drs. John Jensen and Dave Cowan at the University of South Carolina (Dept. of Geography). One of Jensen’s graduate teaching assistants gave us a stereo pair of black and white aerial photos for us to use in our aerial photo interpretation lab. Among the tasks we had to perform was to decide what part of the US the photos covered. It was a trick question.

The stereo pairs were from the late 1930s, had smoothly rolling terrain, and were mostly covered by hay, corn and other crops. Trees were only located to provide shade for homes and along the larger streams. All but one student guessed Kansas was the area. The one who guessed differently said Nebraska (he was from Nebraska). We were all wrong. It was from Laurens County, South Carolina about 60 miles north of the university. Look on Google today.

We missed it because in 1989 (and today) when we drove through the area, there was little farming and the area was mostly covered by trees. But if we had looked carefully, we would have noticed that most of the trees were less than 50 years old.

So, thus began an interest in historical aerial imagery. The chart below was born at that time when I was a graduate student and working at South Carolina Department of Natural Resources.

			1:40000		1:9600		1:4800		1:2400		1:1200
DPI	Microns	Feet	Meters	Feet	Meters	Feet	C'meters	Inches	C'meters	Inches	C'meters
508	50	6.56	2.00	1.57	0.48	0.79	24.00	4.72	12.00	2.36	6.00
635	40	5.25	1.60	1.26	0.38	0.63	19.20	3.78	9.60	1.89	4.80
847	30	3.94	1.20	0.94	0.29	0.47	14.40	2.83	7.20	1.42	3.60
1016	25	3.28	1.00	0.79	0.24	0.39	12.00	2.36	6.00	1.18	3.00
1270	20	2.62	0.80	0.63	0.19	0.31	9.60	1.89	4.80	0.94	2.40
1411	18	2.36	0.72	0.57	0.17	0.28	8.64	1.70	4.32	0.85	2.16
1814	14	1.84	0.56	0.44	0.13	0.22	6.72	1.32	3.36	0.66	1.68
2540	10	1.31	0.40	0.31	0.10	0.16	4.80	0.94	2.40	0.47	1.20
3629	7	0.92	0.28	0.22	0.07	0.11	3.36	0.66	1.68	0.33	0.84

1. These are in photo scales; not in map scales.
2. Diapositive or negative transparencies provide the best results. The original scan resolution should be at least 20% smaller than the final pixel size. Scanning images above 50 microns will make it difficult to measure fiducials correctly, and is discouraged when doing ortho-correction.
3. The best available resolution is determined from the "Camera Calibration Report" in the "Lens Resolving Power" section. Depending on the quality of the camera, lens and film; resolution quality will vary across the image. 1000 / Tangential Line value will calculate the available resolution of the image in microns. As an example, cameras used to capture US Geological Survey (USGS) National Aerial Photography Program (NAPP) imagery typically had a maximum resolving ability from 8.85 to 15.38 microns. Using this information, the USGS typically scanned CIR NAPP imagery at 14 microns.
4. Below is a graphic Spatial Model to convert scanned negatives to positives. It is simply each digital number minus 255 (if the data are 8-bit). You may wish to add another step to the model to the model eliminate all zero and 255 values. Remote sensing software (including ERDAS IMAGINE) like zero as the background values (black). ESRI's ArcGIS likes 255 as the background value (white). The difference comes from image analysts wanting a black background to ease eye strain, while GIS analysts wanting a white background for the map composition. Although the ArcGIS user could make the 255 values transparent, many are not familiar with this option. Thus ESRI made it simple for their customers.

PAGESIZE 6, 8 INCHES;
CELLSIZE MINIMUM;
PRINTERPAGESIZE 8.5, 11;
MARGINS 0.5, 0.5, 0.5, 0.5;
ORIENTATION PORTRAIT;
PRINTSCALE 100;
WINDOW UNION;
PROJECTION DEFAULT;
AOI NONE;
OPTIMIZE NO;
RASTER {
ID 1;
TITLE "n1_memory";
POSITION 0.833329, 0.666667;
TEMPFILE;
INTERPOLATION NEAREST;
ATHEMATIC;
DATATYPE FLOAT;
DECLARE "Integer";
COMPRESSION UNCOMPRESSED;
COORDINATES MAP;
RECODE NO;
CHILD 2;
}
FUNCTION {
ID 2;
TITLE "$n1_memory";
POSITION 1.68889, 1.91111;
VALUE "$n1_memory - 255";
AREA UNION;
CHILD 3;
}
RASTER {
ID 3;
TITLE "n3_memory";
POSITION 2.54444, 3.28889;
TEMPFILE;
NEWFILE;
INTERPOLATION NEAREST;
ATHEMATIC;
DATATYPE FLOAT;
DECLARE "Integer";
COMPRESSION UNCOMPRESSED;
COORDINATES MAP;
RECODE NO;
}

Fill Holes and Slivers in Imagery (Table of Values)

Here is a graphic Spatial Model (PaulBeaty_HoleFiller_Table.gmd) I created when I was at Georgia Tech. It fills DEM and other raster data (including imagery) holes and slivers with data calculated values from surrounding pixel values. These 1, 2 and sometimes 3 pixel wide artifacts are common to users who have reprojected butt-matched raster data. This model finds the "to-be-replaced" values from a table of values and determines whether a 3 x 3 or 5 x 5 focal mean should be applied. The model ignores the replacement values when calculating the focal mean.

Copy and paste the text below into a text editor and save as ANSI text file without the text editor's formatting. Save as PaulBeaty_HoleFiller_Table.gmd and open in Model Maker.

PAGESIZE 6.04444, 8.73889 INCHES;
CELLSIZE MINIMUM;
PRINTERPAGESIZE 8.5, 11;
MARGINS 0.5, 0.5, 0.5, 0.5;
ORIENTATION PORTRAIT;
PRINTSCALE 100;
WINDOW INTERSECTION;
PROJECTION DEFAULT;
AOI NONE;
OPTIMIZE YES;
RASTER {
ID 1;
TITLE "n1_PROMPT_USER";
POSITION 0.855556, 1.4;
PROMPT;
IGNORE 0;
INTERPOLATION NEAREST;
ATHEMATIC;
DATATYPE UNSIGNED16;
DECLARE "Integer";
COMPRESSION UNCOMPRESSED;
COORDINATES MAP;
AREA RECT 26, -2.079167 : 26.628333, -5.184167;
AOI NONE;
RECODE NO;
CHILD 2;
}
FUNCTION {
ID 2;
TITLE "EITHER";
POSITION 2.18889, 3.21111;
VALUE "EITHER (FOCAL MEAN ( $n1_PROMPT_USER , $n6_Low_Pass , IGNORE_VALUE $n28_Custom_Integer , APPLY_AT_VALUE $n28_Custom_Integer ) ) IF ( (FOCAL MAJORITY ( $n1_PROMPT_USER , $n3_Low_Pass ) == $n28_Custom_Integer) ) OR (FOCAL MEAN ( $n1_PROMPT_USER , $n3_Low_Pass , IGNORE_VALUE $n28_Custom_Integer , APPLY_AT_VALUE $n28_Custom_Integer ) ) OTHERWISE";
AREA UNION;
CHILD 4;
}
MATRIX {
ID 3;
TITLE "n3_Low_Pass";
POSITION 3.42222, 5.36667;
SIZE 3, 3;
DATATYPE SIGNED32;
BUILTIN LOWPASS;
VALUE 1, 1, 1,
1, 1, 1,
1, 1, 1;
NORMALIZE NO;
CHILD 2;
}
RASTER {
ID 4;
TITLE "n4_PROMPT_USER";
POSITION 0.944445, 5.24444;
PROMPT;
NEWFILE;
IGNORE 0;
INTERPOLATION NEAREST;
ATHEMATIC;
DATATYPE UNSIGNED16;
DECLARE "Integer";
COMPRESSION UNCOMPRESSED;
COORDINATES MAP;
RECODE NO;
}
MATRIX {
ID 6;
TITLE "n6_Low_Pass";
POSITION 3.26667, 1.68889;
SIZE 5, 5;
DATATYPE SIGNED32;
BUILTIN LOWPASS;
VALUE 1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1;
NORMALIZE NO;
CHILD 2;
}
TEXT {
ID 7;
TITLE "Repair Holes and Slivers using a Table of Values";
POSITION 2.67778, 0.344445;
FONT "new century schoolbook";
SIZE 18;
}
TEXT {
ID 8;
TITLE "5 x 5 filter";
POSITION 3.26667, 0.988889;
FONT "new century schoolbook";
SIZE 12;
}
TEXT {
ID 10;
TITLE "Value(s) to Replace";
POSITION 0.788889, 2.57778;
FONT "new century schoolbook";
SIZE 12;
}
TEXT {
ID 11;
TITLE "3 x 3 filter";
POSITION 3.52222, 4.66667;
FONT "new century schoolbook";
SIZE 12;
}
TEXT {
ID 13;
TITLE "Conditional statement to determine";
POSITION 3.8, 2.78889;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 14;
TITLE "which filter is more appropriate. If a majority";
POSITION 4.06667, 2.96667;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 15;
TITLE "of the pixels are the \"Value(s) to Replace,\"";
POSITION 4.01111, 3.18889;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 16;
TITLE "the 5 x 5 filter is used. Otherwise, ";
POSITION 3.8, 3.38889;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 17;
TITLE "the 3 x 3 filter is used.";
POSITION 3.4, 3.58889;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 18;
TITLE "Be sure to define the correct";
POSITION 1.03333, 6.23333;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 19;
TITLE "\"Data Type\" in the output file.";
POSITION 1.03333, 6.43333;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 20;
TITLE "Created in IMAGINE 8.4 - Developed by Paul Beaty";
POSITION 2.72222, 7.08889;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 21;
TITLE "Center for Geographic Information Systems";
POSITION 2.72222, 7.47778;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 22;
TITLE "Georgia Institute of Technology - Atlanta Georgia, USA";
POSITION 2.82222, 7.65556;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 26;
TITLE "Both filters ignore the \"Value(s) to Replace\"";
POSITION 4.06667, 3.77778;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 27;
TITLE "when calculating the mean.";
POSITION 3.58889, 3.98889;
FONT "new century schoolbook";
SIZE 10;
}
TABLE {
ID 28;
TITLE "n28_Custom_Integer";
POSITION 0.744445, 3.21111;
SIZE 4;
DATATYPE SIGNED32;
VALUE 0, 10, 20, 30;
CHILD 2;
}
TEXT {
ID 29;
TITLE "Add and delete values";
POSITION 0.8, 4.08889;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 30;
TITLE "Integer or float?";
POSITION 0.777778, 4.27778;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 31;
TITLE "Modified in IMAGINE 8.7 - Paul Beaty";
POSITION 2.71111, 7.27778;
FONT "new century schoolbook";
SIZE 10;
}

Fill Holes and Slivers in Imagery (Single Value)

Here is a graphic Spatial Model (PaulBeaty_HoleFiller.gmd) I created when I was at Georgia Tech. It fills DEM and other raster data (including imagery) holes and slivers with data calculated values from surrounding pixel values. These 1, 2 and sometimes 3 pixel wide artifacts are common to users who have reprojected butt-matched raster data. This model finds the "to-be-replaced" value and determines whether a 3 x 3 or 5 x 5 focal mean should be applied. The model ignores the replacement value when calculating the focal mean.

Copy and paste the text below into a text editor and save as ANSI text file without the text editor's formatting. Save as PaulBeaty_HoleFiller.gmd and open in Model Maker.

PAGESIZE 6.04444, 8.83889 INCHES;
CELLSIZE MINIMUM;
PRINTERPAGESIZE 8.5, 11;
MARGINS 0.5, 0.5, 0.5, 0.5;
ORIENTATION PORTRAIT;
PRINTSCALE 100;
WINDOW INTERSECTION;
PROJECTION DEFAULT;
AOI NONE;
OPTIMIZE YES;
RASTER {
ID 1;
TITLE "n1_PROMPT_USER";
POSITION 0.8, 1.7;
PROMPT;
INTERPOLATION NEAREST;
ATHEMATIC;
DATATYPE SIGNED16;
DECLARE "Integer";
COMPRESSION UNCOMPRESSED;
COORDINATES MAP;
AOI NONE;
RECODE NO;
CHILD 2;
}
FUNCTION {
ID 2;
TITLE "EITHER";
POSITION 2, 3.3;
VALUE "EITHER (FOCAL MEAN ( $n1_PROMPT_USER , $n6_Low_Pass , IGNORE_VALUE $n5_Integer , APPLY_AT_VALUE $n5_Integer ) ) IF ( (FOCAL MAJORITY ( $n1_PROMPT_USER , $n3_Low_Pass ) == $n5_Integer) ) OR (FOCAL MEAN ( $n1_PROMPT_USER , $n3_Low_Pass , IGNORE_VALUE $n5_Integer , APPLY_AT_VALUE $n5_Integer ) ) OTHERWISE";
AREA UNION;
CHILD 4;
}
MATRIX {
ID 3;
TITLE "n3_Low_Pass";
POSITION 3.3, 5.9;
SIZE 3, 3;
DATATYPE SIGNED32;
BUILTIN LOWPASS;
VALUE 1, 1, 1,
1, 1, 1,
1, 1, 1;
NORMALIZE NO;
CHILD 2;
}
RASTER {
ID 4;
TITLE "n4_PROMPT_USER";
POSITION 0.988889, 5.5;
PROMPT;
NEWFILE;
INTERPOLATION NEAREST;
ATHEMATIC;
DATATYPE SIGNED16;
DECLARE "Integer";
COMPRESSION UNCOMPRESSED;
COORDINATES MAP;
RECODE NO;
}
SCALAR {
ID 5;
TITLE "n5_Integer";
POSITION 0.6, 4;
DATATYPE SIGNED32;
VALUE -32767;
SHOW;
CHILD 2;
}
MATRIX {
ID 6;
TITLE "n6_Low_Pass";
POSITION 3.2, 1.8;
SIZE 5, 5;
DATATYPE SIGNED32;
BUILTIN LOWPASS;
VALUE 1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1;
NORMALIZE NO;
CHILD 2;
}
TEXT {
ID 7;
TITLE "Repair DEM Holes and Slivers";
POSITION 2.4, 0.5;
FONT "new century schoolbook";
SIZE 18;
}
TEXT {
ID 8;
TITLE "5 x 5 filter";
POSITION 3.2, 1.1;
FONT "new century schoolbook";
SIZE 12;
}
TEXT {
ID 10;
TITLE "Value to Replace";
POSITION 0.644444, 3.4;
FONT "new century schoolbook";
SIZE 12;
}
TEXT {
ID 11;
TITLE "3 x 3 filter";
POSITION 3.4, 5.2;
FONT "new century schoolbook";
SIZE 12;
}
TEXT {
ID 13;
TITLE "Conditional statement to determine";
POSITION 3.7, 2.8;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 14;
TITLE "which filter is appropriate. If a majority";
POSITION 3.8, 3;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 15;
TITLE "of the pixels are the \"Value to Replace,\"";
POSITION 3.83333, 3.18889;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 16;
TITLE "the 5 x 5 filter is used. Otherwise, ";
POSITION 3.7, 3.4;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 17;
TITLE "the 3 x 3 filter is used.";
POSITION 3.3, 3.6;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 18;
TITLE "Be sure to define the correct";
POSITION 1.2, 6.4;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 19;
TITLE "\"Data Type\" in the output file.";
POSITION 1.2, 6.6;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 20;
TITLE "Created in IMAGINE 8.4 - Developed by Paul Beaty";
POSITION 2.7, 7.4;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 21;
TITLE "Center for Geographic Information Systems";
POSITION 2.7, 7.6;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 22;
TITLE "Georgia Institute of Technology - Atlanta Georgia, USA";
POSITION 2.8, 7.8;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 26;
TITLE "Both filters ignore the \"Value to Replace\"";
POSITION 3.9, 3.8;
FONT "new century schoolbook";
SIZE 10;
}
TEXT {
ID 27;
TITLE "when calculating the mean.";
POSITION 3.48889, 4;
FONT "new century schoolbook";
SIZE 10;
}

A Brief History of ERDAS IMAGINE

+++While this blog post was originally in April 2009, I have added to it to keep it up-to-date on releases and new information uncovered in my historical research.+++


I thought I would take some time to briefly outline some of the major points in the history of ERDAS IMAGINE. I have rebuilt this history from "What's New" PowerPoints, "What's New" documents, software documentation, release plans, software media, software code, advertisements, and personal interviews.
Since its beginning, ERDAS software was designed to be a blend of remote sensing and GIS analysis capabilities. Combining remote sensing with GIS allowed ERDAS to deliver a product which analyzed existing geospatial information and create updated information for re-analysis rather than use outdated data; allowing the customer to make the most informed decision on the their part of an ever changing world. Combining remote sensing and GIS analysis capabilities made the ERDAS software a true decision support tool as a GIS is intended to be (Cowen, D. PE&RS, Nov 1988).

From the 1984 ERDAS 2400 Users' Manual Page 1-1 we read,

"In early 1979 a group of the multi-disciplinary professionals (including Planners, Landscape Architects, Geologists, Remote Sensing Specialists, and Electrical Engineers) collaborated on the design of a microcomputer-based geographic information system to be used for planning and resource analysis applications. This Earth Resources Data Analysis System (ERDAS) was designed to be used by professionals in application areas without requiring a prior knowledge of computers or computer programming. Some of the original major design issues included:

• The need to integrate LANDSAT remotely sensed imagery into a more comprehensive Geographic Information System (GIS) with other data sources such as soils, topography, cultural features, etc.
• The design of a “user-friendly” system environment through the use of keyword oriented menus and conversational programs.
• The ability to expand both the system hardware and software over time as new data sources (e.g. LANDSAT 4, digital soils and terrain tapes), new techniques, and new applications needs to be identified.

This User’s Manual in many ways reflects the growth and development of the original concept and design objectives. The Manual incorporates a wide variety of ideas which originated through feedback from system users. Several menus for LANDSAT and GIS applications have doubled in the number of features offered. Many programs have been expanded to handle multi-channel data (e.g., new LANDSAT) or extended ranges of data values for more complex GIS layers. The choice of computer hardware options has been expanded to include the new DEC PDP-11 and VAX family of minicomputers, along with new turn-key hardware/software options for polygon digitizing, 9-track tape features, etc. ERDAS systems with these features are now being used around the United States, in Europe, and even in a mobile van-mounted unit. Nonetheless, the original objectives are a continuing challenge to the system design team."

Another page from the first manual may be found here.

Here is an outline of changes in the software from the beginning.
1978, ERDAS 4 introduced (hardware and software turnkey solution)

• Cromemco microcomputers using the 8-bit Z80 CPU and CDOS operating system
• Built into a desk, color monitor (256 x 256),  monitor,
• Two 8" floppy drives (one each for software and data)
• Optional 5MB or 10MB hard disk

1980, ERDAS 400 introduced

• Added CAT 400 display option

1979 - 1981, ERDAS enhancements

• More image processing capabilities
• Large table digitizers to convert existing maps
• Hard disk added (80MB fixed disk and 16MB removable platter), it was the size of a washing machine

1982, ERDAS 2400 released

• Added PDP 11/24 16-bit processor with 10MB removable hard drive option
• Added CAT 800 display option

1982, ERDAS 7.0; first COTS version on PC introduced (Nov 1982)

• IBM XT using Intel CPU and MSDOS operating system
• Turnkey hardware and software solution
• Color monitor (512 x 512), B&W monitor, one 5.25” floppy drive
• Added menu system to command prompts system

1983, ERDAS 7.1 released

• Jan 1984, first PC license sold to Dr. John R. Jensen at the Department of Geography at the University of South Carolina (then Ph.D candidate Michael E. Hodgson and MS candidate Bruce A. Davis drive to Atlanta to take receipt of turnkey system).

1985, ERDAS 7.2 released

• ERDAS 7.2 on IBM-PC/AT, VAX, Data General and Prime
• Dedicated added dual 1024 x 32-bit display to existing 512 x32-bit display
• Hardware roam, zoom and histogram manipulation

1986, ERDAS 7.2 enhancement released

• Over 120 programs (including GIS analysis, topographic & 3D capabilities and classification)
• Menu driven, color scaled hardcopy, 9-track tape handling
• Video digitization of imagery introduced
• Georeferencing of imagery introduced through research on Intelligent Indexing System

1987, ERDAS 7.3 released

• ERDAS – PC ARC/INFO Link (first ERDAS ESRI COTS collaboration) introduced
• Stitch (mosaic) introduced
• Support for CPQ-DOS allowing 32MB partitions
• ERDAS for Sun 3 Workstation introduced

1989, ERDAS 7.4 released

• Classification enhancements, training samples, accuracy assessment, etc.
• Support for large format electrostatic plotters introduced
• ERDAS – PC ARC/INFO Link renamed to ERDAS – ARC/INFO Live Link and added to additional platforms

1990, ERDAS 7.5 released

• GISMO (GIS MOdeling, a script language) introduced

1991, VGA ERDAS 7.5 introduced

• Uses single display toggle mechanism for customers who cannot afford dual screen configuration
• All commands available, excluding dual screen specific commands
• ERDAS Digital Ortho (single frame resection) on ERDAS 7.5 on Sun Workstations introduced
• Beta version of ERDAS IMAGINE introduced in October 1991 at the ERDAS Users Group Meeting in Atlanta, GA

1992, ERDAS IMAGINE 8.0 introduced (Feb 1992)

• Name combined ERDAS brand with IMAGINE, playing off the word image and the concept of creating ideas and data
• First graphical user interface for ERDAS product, replacing former ERDAS menu structure
• Sun Workstation only
• Multiple Viewers, GUI, geographic linking
• Spatial Modeler (GIS script modeling language) replaces GISMO, has over 150 commands
• ERDAS 7.5 delivered much of the processing capability until 8.1 was released in 1994

Late 1992 or early 1993, ERDAS IMAGINE 8.0.1 released

• IMAGINE Digital Ortho released; upgraded user interface and capability from ERDAS Digital Ortho 7.5

1993, ERDAS IMAGINE 8.0.2 released

• Model Maker (graphic flow chart model builder enhancement to Spatial Modeler) introduced
• Map Composer (WYSIWYG map composition tools) introduced
• Vector Module (first COTS user interface for editing ESRI Arc Coverage) introduced
• May 1993, phrase "Intelligent Images Map the Future" introduced; in Sept 1993 trademarked to "The Map of the Future is an Intelligent Image"
• OrthoMAX replaces Digital Ortho introduces block-bundle adjustment and DEM creation and stereo editing

1994, ERDAS IMAGINE 8.1 released

• Image Interpreter introduced (build upon Spatial Modeler)
• Radar Interpreter introduced
• Area of Interest (AOI) processing introduced
• Cell Array introduced
• Raster editing and raster attribute editor introduced
• 'Seed' collection tools added to supervised classification
• Multi-threading in Viewer introduced
• Image Catalog Introduced
• AutoWarp (model based automatic image registration) introduced
• Return to PC from UNIX with release of Windows NT product

1995, ERDAS IMAGINE 8.2 released

• Map Series Tool added to Map Composer
• VirtualGIS and NITF introduced

1996, ERDAS IMAGINE 8.2 Subpixel Classification introduced

1997, ERDAS IMAGINE 8.3 released

• MosaicTool released Subpixel Classification expanded to more platforms
• ERDAS 8.3.1 for UNIX released at the end of the 1997

1998, ERDAS 8.3.1 released on Windows 95 and NT 4.0

• Renamed IMAGINE Vista to IMAGINE Essentials
• Introduced IMAGINE Advantage
• Renamed IMAGINE Production to IMAGINE Professional
• Native editing of ESRI Arc Coverages without a Vector Module license introduced
• Introduced ERDAS ArcView Image Anlaysis 1.0 (based on ERDAS IMAGINE technology)
• Introduced ERDAS MapSheets 1.0 (based on ERDAS IMAGINE technology)

1999, ERDAS IMAGINE 8.4 released

• StereoSAR DEM and OrthoRadar introduced
• OrthoBASE released replacing OrthoMAX
• ESRI 2D Shapefile and SDE support introduced
• Read capability of LizardTech's MrSID compressed image format introduced
• Batch Tools added
• Fuzzy Classifier and Fuzzy Convolution introduced
• Expert Classifier introduced
• Reprojection on the fly fully implemented
• Break 2.1 GB file barrier, introduce .ige and .rde data extension files
• First deliver of Raster Data Objects (RDO) to ESRI; ERDAS IMAGINE technology imbedded into ArcMap

2000, ERDAS Stereo Analyst released

2001, ERDAS IMAGINE 8.5 released

• OrthoBASE Pro Introduced
• IMAGINE MrSID Encoders introduced
• ESRI Geodatabase support introduced
• ESRI 3D Shapefile support introduced
• Anaglyph stereo image creation introduced

2002, ERDAS IMAGINE 8.6 released

• GLT Viewer introduced
• CIB / CADRG (RPF) production introduced
• Virtual Mosaic, Virtual Layer Stack and Virtual Independent Files introduced
• Spectral Analysis tools for hyperspectral image processing introduced
• Vertical Datum support introduced
• Frame Sampling and Class Grouping Tools introduced
• Dodging added to MosaicTool

2004, ERDAS IMAGINE 8.7 released

• LPS released replacing OrthoBASE Pro
• Terrain Editor, PRO600 and ORIMA introduced to LPS product line
• LPS marketed separately from ERDAS IMAGINE (still based on IMAGINE Technology)
• Multi-threaded added to GLT Viewer
• Fuzzy Recode introduced
• Mosaic Wizard and Mosaic Direct, the engines behind Leica MosaicPro released in 8.7.2 (Feb 2005).
• MrSID Generation 3 read and write support added

2005, ERDAS IMAGINE 9.0 released

• AutoSync introduced
• Oracle Spatial 10g support introduced
• Previously introduced in a minor release, Leica MosaicPro Module released.

2006, ERDAS IMAGINE 9.1 released

• EasyTrace introduced

2007, IMAGINE DeltaCue introduced

2008, ERDAS IMAGINE 9.2, 9.3 and 9.3.1 released

• 9.2, Massive improvements to raster roam quality and speed
• 9.3, IMAGINE Subpixel Classifier becomes part of IMAGINE Professional
• 9.3, Massive improvements on vector and annotation display speed
• 9.3, Improvements in MrSID projection support
• 9.3, Initial ERDAS ER Mapper integration efforts released (ECW, joint product licensing)
• 9.3, ECW SDK v3.6 used for ECW and JPEG2000 creation (except for NITF JPEG2000)
• 9.3.1, IMAGINE InSAR replaces IMAGINE IFSAR DEM; Coherence Change Detection introduced

2009, ERDAS IMAGINE 9.3.2 and 2010 released

• 9.3.2, Support for non-Earth map coordinates introduced
• 9.3.2, ECW SDK v 3.6 used for all ECW and JPEG2000 creation (including NITF)
• 2010, Ribbon User Interface introduced
• 2010, ERDAS MosaicPro has massive improvements and is moved into IMAGINE Advantage, no longer a separate module
• 2010, ER Mapper Image Compressor (ECW, JPEG2000 file and mosaic compression) moved into ERDAS IMAGINE
• 2010, LizardTech MrSID metadata added and file handling capacity increased
• 2010, ER Mapper Algorithms moved into ERDAS IMAGINE
• 2010, Parallel Batch Processing introduced to IMAGINE Advantage; 4 per Advantage license
• 2010, IMAGINE SAR Interferometry released (InSAR, CCD and new D-InSAR capabilities in one module)

2010, ERDAS IMAGINE 2010 Versions 10.1, and 2011 released

• 2010 v10.1, ERDAS ECW JPEG2000 SDK v4.1 integrated into ERDAS IMAGINE and ERDAS ER Mapper. Performance improvements for JPEG2000 and ECW are very significant.
• 2010 v10.1, Direct read MrSID DLL performance improved (up to 30% in some applications), direct write to lossless introduced
• 2010 v10.1, 'Large Address Aware' support introduced for Windows 64-bit operating systems
• 2010 v10.1, Image Segmentation memory management introduced for large file handling
• 2010 v10.1 First Spectral Shift Filter from DLR (German Aerospace) introduced in IMAGINE SAR Interferometry
• 2010 v10.1, Transparency masks for ECW and JPEG2000 introduced in ERDAS ER Mapper
• 2011, Transparency masks for ECW and JPEG2000 introduced in ERDAS IMAGINE
• 2011, Further ECW and JPEG2000 performance improvements in Export, MosaicPro and direct write
• 2011, Surface Tool contour generation moved into Terrain Prep Tool
• 2011, Distributed processing added to multi-core processing, 4 per Advantage license or ERDAS Engine
• 2011, Improved Imagery Analysis Workflow within the IMAGINE ribbon replaces GLT
• 2011, LAS Import rasterization incorporates all returns, intensities, classifications and RGB values

2011, ERDAS IMAGINE 2011 Versions 11.0.1, 11.0.2, 11.0.3, and 11.0.4 Released

• Each version addresses maintenance issues and customer enhancement requests
• Each version expands use of Large Address Aware 
• Each version expands map projection support
• 11.0.2, Improved Bing Basemap performance in slower internet areas
• 11.0.2, Added capability to ECW Export to define transparency area via AOI layer
• 11.0.3, Add Spectral Angle and Spectral Correlation Mapper to Supervised Classification
• 11.0.3, Add data re-scaling to MosaicPro
• 11.0.3, Add process distribution of output images tiles in MosaicPro 
• 11.0.4, Add support fro GDAL OVRs 

2012, ERDAS IMAGINE 2011 Version 11.0.5 and 2013 Released  

• 11.0.5, Improved JP2 decoding inherited from  ERDAS ECW/JP2 SDK improvements 
• 11.0.5, Added 'Live Link' with GeoMedia 
• 11.0.5, Added support for GeoMedia Warehouses 
• 2013, Asynchronous raster data engine in ERDAS IMAGINE adds rapid pull architecture
• 2013, Spatial Modeler re-designed with new UI, and pull architecture for real-time processing
• 2013, Point cloud visualization in 2D and 3D introduced
• 2013, Point cloud file to file processing added
• 2013, LPS ribbonized
• 2013, ECW/JP2 SDK upgraded, v3 file format introduced
• 2013, Add support for ECW transparency layer (aka opacity or NODATA layer)
• 2013, Realtime vegetation and other indices with new modeler technology
• 2013, Preference Editor redesigned
• 2013, Geomedia vector warehouse support improved
• 2013, Geomedia vector warehouse processing put into 'new' Spatial Modeler 
• 2013, MosaicPro performance improved
• 2013, Add support for Point Cloud (LAS)
• 2013, Radar Analyst UI introduced

2013, ERDAS IMAGINE 13.00.0001, 13.00.0002, and 2014 Released 

• 13.00.0002, Upgrade ECW/JP2 SDK verison
• 13.00.0002, Stengthen support for ECW NODATA layer
• 13.00.0002, Speeds up Export ECW  
• 2014, Move raster engine, spatial modeler, and MosaicPro to 64-bit applications
• 2014, expand support for Point Cloud (LAS)
• 2014, Upgrade to 64-bit ECW/JP2 SDK

2014, ERDAS IMAGINE 14.00.01, and 2015 Released

• 14.00.01, Add support for LAZ
• 14.00.01, Add Orient to Map and enhance ECW NODATA Layer support in Export ECW 
• 14.00.01, Update to latest ECW/JP2 SDK
• 2015, Significant speed improvements for MosaicPro 
• 2015, Expand point cloud editing 
• 2015, Expand point cloud processing 
• 2015, Add fast point cloud streaming in 2D Viewer from  ERDAS APOLLO Essentials
• 2015, Add point cloud volumetric analysis 
• 2015, Improve DEM editing in Terrain Editor 
• 2015, Add Image Change and Stretch Panel to 2D Viewer 
• 2015, Add more Spatial Modeler operators, including RapidAtmospheric and DodgePlus
• 2015, Add new Zonal Change Detection processing engine and review UI 
• 2015, Upgrade to latest 64-bit ECW/JP2 SDK

2015, ERDAS IMAGINE 15.00.01 Released 

• 15.00.01, Update to latest 64-bit ECW/JP2 SDK 
• 15.00.01, Add more Spatial Modeler operators 
• At HxGN Live in June in Las Vegas, Mladen Stojic introduces the phrase "The Map of the Future is a Smart M.App". This is a natural extension of the 1993 ERDAS, Inc. trademarked phrase, "The Map of the Future is an Intelligent Image."  

The History of ERDAS, by Brad Skelton: http://www.youtube.com/watch?v=5RkYHZy2bfY&feature=player_embedded

ERDAS IMAGINE 9.3.2 Released

ERDAS IMAGINE 9.3.2 is no longer a beta product and has been released to software maintenance (SWM) customers on the ERDAS Support site. The most common errors people have had are as follows:

• Going to the IMAGINE Products webpage rather than the ERDAS Support webpage. Downloads of ERDAS IMAGINE 9.3 are available on the Products page to provide people a way to demo the software. The 9.3.1 and 9.3.2 versions are on the ERDAS Support webpage. I know this is confusing, but until ERDAS completes the webpage transformation, there are a few awkward limitations.
• Not knowing your SWM login name and password. The visitor login will get you to ERDAS IMAGINE 9.3.1, but ERDAS IMAGINE 9.3.2 requires your SWM login.
• Asking about licensing. There are no changes in licensing. ERDAS IMAGINE 9.3.1 and ERDAS IMAGINE 9.3.2 use ERDAS IMAGINE 9.3 license files and brokers. Just install and play.
• Believing you need to install ERDAS IMAGINE 9.3.1 before you install ERDAS IMAGINE 9.3.2. ERDAS IMAGINE 9.3.2 includes all the items found in ERDAS IMAGINE 9.3.1.

Keep your eyes open for Webinars concering ERDAS IMAGINE 9.4. The Webinars will begin in a little over a month, and the product release is due near the end of summer. I think you are going to like ERDAS IMAGINE 9.4.

Create an Output Directory Structure for Batch Processing

I originally created this procedure while at the Georgia Institute of Technology (Georgia Tech) Center for Geographic Information Systems. In response to the following ERDAS-L question:

Q: “I have a lot of ASTER 1A HDF images to import to the .img format using the batch tool. How does one send, by batch procedures, the bands from each HDF into a directory for each ASTER image? The directory must exist before the batch starts. How may I create the output directory inside the batch tool?”

A: This procedure assumes you have an existing directory structure and require an identical one in the output location.

Part A:
1. Open a DOS Command Window
2. Change to the appropriate directory
3. Use "dir" command as follows: dir *.hdf /b /s > file-list.txt
4. Open "file-list.txt" in MS Excel
5. Copy & Paste 1st Column into a 2nd column
6. Find & Replace all "\" with "/", in both columns
7. In the 2nd column, Find & Replace old directory path with new directory path
8. Insert new row at top
9. 1st Column = “Input”; 2nd Column = “Output”
10. Save as tab delimitated text file with "file-list.bls".
Part B:
Part B1:
1. Open original "file-list.txt" in Excel with "\" as delimiter. Insert 1st and 2nd Column
2. Put mkdir in 1st Column, leave 2nd Column empty
3. Delete the file name column
4. Find & Replace Directory Path Columns as needed
5. Save as text file "mkdir.bat"
Part B2:
1. Open "mkdir.bat" in Word
2. Find & Replace: ^t^t with " " (a single space)
3. Find & Replace: ^t with "\"
4. Save as tab-delimited text file as "mkdir.bat".
Part C:
1. Run "mkdir.bat" in DOS window.
Part D:
1. Start the batch tool. Select Modify commands automatically.
2. Change Output "Variable" to "User"
3. Open .bls in Batch tool in file list section
4. Launch program.

A Map of the World as we know it....

Here is a little fun on YouTube....

http://www.youtube.com/watch?v=f1iPe0LtCew

Benefits of 64-bit Architecture in Geospatial Imaging

I asked Hammad Khan, an ERDAS IMAGINE Software Engineer, to write a discussion of 32- and 64-bit processing in ERDAS IMAGINE. Thank you, Hammad.
--

Recently, we have noticed a lot of discussion in our community centered on the benefits of 64-bit computing. Having discussed 64-bit processor architecture with customers and product managers here at ERDAS, I felt it may help to share some thoughts on how 64-bit computing impacts our work in the geospatial imaging world.

As opposed to their more common 32-bit counterparts, 64-bit processors have two major advantages: the ability to access more memory, and an increased number of General Purpose Registers (GPRs). Memory is used by programs to store data needed for calculations. Registers, by contrast, are used to store specific values (a single number, for example) for very quick access in calculations. Data is moved from memory to registers prior to calculations being performed. Another advantage of 64-bit processors worth mentioning is the introduction additional SSE/SSE2 registers, which are registers used for highly-optimized, highly-repeated calculations.

What does this mean for software designed for geospatial analysis? By nature, geospatial analysis algorithms must run on imagery, data which are large, and getting larger. As such, geospatial analysis algorithms rarely hold entire datasets in memory, and are almost always written using techniques such as tiling data access to allow the program to run to completion.

For example, an ADS-40 sensor will frequently generate data that is hundreds of gigabytes in size. Further, we are beginning to see multiple-terabyte images pop up, such as the data used by the Oregon Imagery Explorer project at the Oregon State University. Processing data of this magnitude will require tiled access or another clever data access technique; regardless of how many address bits are available, the entire dataset has a good chance of not fitting in memory.

To illustrate, most traditional classification algorithms may be performed by loading a tile of the image into memory, processing the tile, writing the output, and then loading the next tile. This approach may be (and, in ERDAS IMAGINE, is) optimized by loading the next tile to be worked on while the current tile is being processed. Notable exceptions to this tile-based data-access approach include terrain generation, where generation of a TIN cannot be neatly broken up: features in one tile may, and frequently do, influence features in adjacent tiles. This limitation can be circumvented by using a spatial index (say, using a quad-tree) to intelligently access appropriate tiles; regardless, loading the entire image into memory is not a recommended option.

As computation seems to be our focus above, does this imply that geospatial analysis can then benefit from the computational speedup offered by the additional registers available on 64-bit processors? It is true that the ability to have more data directly ready for access will speed up calculations. However, frequently, the processor is not the bottleneck in executing algorithms. Disk access speeds, and data transport speeds over the associated buses, are frequently seen as bottlenecks. Note that the speed here is primarily getting data read from the disk into memory, not the speed of getting the data from memory to processor registers.

A number of techniques are available to speed up algorithms in the 32-bit world. Examples include pipelining data read and processing, where the next tile is accessed while the current one is being processed; multi-threading processing, where the algorithm is split to be performed in parallel; and using multiple processes wherever possible (say, simultaneously processing multiple independent frames of an RPF dataset) to take advantage of dual- and quad-core architectures. These represent just some of the many tools available to optimize geospatial algorithms. Moving to a 64-bit architecture is another available tool.

This is not to say that moving to a 64-bit architecture will have no benefit whatsoever. Optimization of an algorithm must take a holistic view of the algorithm. The appropriate tools for optimization must then be selected to address bottlenecks specific to the process in question. As such, the benefits of a 64-bit architecture must be kept in perspective.

While there is no doubt that in the near future, we will move to a 64-bit processing environment, and while personally, I look forward to the day where a 32-bit architecture presents primary limitations to our processes, we have found that our bottlenecks lie elsewhere. As such, certainly within our problem space, other optimization techniques offer more bang for the proverbial buck.

We continue to work to make ERDAS IMAGINE the strongest package available for working with geospatial imagery. We aggressively remove bottlenecks as we find them; in addition to the significant performance improvements seen over the previous release, scalability over computing resources will be especially notable in the upcoming release. In fact, of all the releases I have been involved with, and I am particularly excited about the upcoming release. We hope to demo some of the new features at ASPRS in March, and hope you find it as exciting as we do.