LIDAR Data and Processing softwares

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http://gis.mapsofworld.com/lidar/lidar-software.html

LiDAR Softwares have added a new dimension in the arena of Airborne Laser Swath Mapping (ALSM) and laser altimetry. One of the major developments in Remote Sensing technology is the emergence of LiDAR (Light Detection And Ranging) technology. LiDAR is the Remote Sensing technology that can find the range and other information about a particular distant object by the means of measuring the properties of scattered lights.

There are various types of LiDAR Softwares available in the market. Some worth mentioning softwares are:

LiDAR XLR8R: This software is developed by Canadian Software developer, Ambercore Software Inc. This is basically for LiDAR (Light Detection And Ranging) data management, analysis and visualization tool. This LiDAR Software is characterized with the following features:

This facilitates the user batch processing and feature merger capability.

This software is also featured with enhanced pyramiding, 3-D visualization and Geo-referencing functionality for raster data.

This LiDAR Software allows unlimited LiDAR (Light Detection And Ranging) point data to get uploaded and processed.

This can able to generate Digital Elevation Model (DEM) very quickly from the acquired LiDAR (Light Detection And Ranging) data and allow its users to build, view, analyze and disseminate 3-D surfaces.

This software has an innovative algorithms for fast data processing and regularizing, user defined and optimized surface building parameters that helps to control the surface accuracy and precision.

This software is capable if both 2-D and 3-D modelling which facilitates in transparent modelling, multilayering, draping and seamless stitching.

Its can import and export data in various formats like ESRI & USGS.

LP360: This LiDAR Software is developed by Qcoherent Software. This software is largely used as the plug in for the ArcGIS environment. This LP360 LiDAR (Light Detection And Ranging) extension is a platform of integration of LiDAR data with the whole GIS (Geographical Information System) system. This LiDAR data can be viewed in format that the user like to. These formats are supported by ArcGIS™, including vector data, rasters and imagery.

The LP360 LiDAR Software exhibits the following features:

It provides an integrated extension for ArcMAPTM or ArcCatalogTM.

It has unlimited LiDAR data architecture i.e it allows limitless LiDAR data points to get uploaded and processed.

It only need a ArcViewTM license.

It can create LiDAR data layer in ArcMapTM.

It has a ArcMAPTM enabled LiDAR data properties.

It has inbuilt 3-D data viewer with the TIN facility.

It has an advanced LAS file management and has ASCII to LAS import wizard.

TopPIT (TopoSys Processing and Imaging Tools):

This software is mainly for the analysis and reprocessing of LiDAR and RGB/NIR data. LINUX is the best operating system for this application.

Apart from these softwares there are various other LiDAR Softwares that are commercially available in the market. They are as follows:

IDL by Research Systems Inc.
MATLAB by MathWorks
Terrascan by Terrasolid
These softwares are very important since the raw LiDAR data i.e. x, y, z points can be imported and spatially rendered by these softwares.

As far as the integration of LiDAR dataand GIS (Geographical Information System) is concerned, the following LiDAR Softwares are important:

ArcView by ESRI
ARC/INFO by ESRI
Surfer by Golden Software Inc.
MapInfo by MapInfo Corp.
ERDAS Imagine by Earth Resource Mapping Inc.
The demand for LiDAR Softwares are increasing day by day due to its several advantages. This is widely used for high resolution topographic mapping.

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Current capabilities and community needs for software tools and educational resources for use with LiDAR high resolution topography data
August 8, 2008 (original date–will be included in a workshop report being prepared by Dorothy Merritts)

J R. Arrowsmith (Arizona State University; ramon.arrowsmith@asu.edu)

Nancy Glenn (Idaho State University)

Christopher J. Crosby (San Diego SuperComputer Center)

Eric Cowgill (UC Davis)

Introduction

LiDAR (Light Detection and Ranging) data are transforming diverse facets of earth surface studies, including geomorphology, hazard assessments, forestry, and fish ecology. The major increase in spatial resolution (1 to 2 orders of magnitude better than what is currently typically available—e.g., USGS National Elevation Dataset) LiDAR data enable users to identify, measure, and quantitatively characterize features in the landscape such as earthquake offsets, ungullied hillslopes, or tree canopy height/density at the scales necessary for analysis. Although the LiDAR data make unprecedented opportunities, efficient quantitative analysis of these data is challenging because of their size and heterogeneity. Current Airborne Laser Scanning (ALS) methods typically yield several point measurements per square meter over areas encompassing 10s to 1000s of km2. Although Terrestrial Laser Scanning (TLS) typically covers smaller areas (0.1 to 1 km2), current approaches sample the surface at 1 or 2 orders of magnitude higher spatial resolution than airborne LiDAR. Thus both ALS and TLS produce data sets containing 100s of millions to billions of individual measurements to be processed and analyzed. The use of repeat LiDAR scans in time-series analysis for change detection and the coupling of imagery with the scanning processes further magnifies these computational needs.

Users working with LiDAR data encounter two main data types: 1) the attributed point cloud measured by the laser scanner, and 2) high-resolution (0.1 to 1 m/pixel) DEMs (digital elevation models) derived from the point measurements. Point clouds comprise a set of measured positions in three-dimensional space of point locations on a scanned surface (e.g., bare ground, trees, roads, buildings, etc.). Points are typically attributed with return intensity, time, and number, in the case of multiple returns for a single shot. In addition, they can be classified as ground, structure, vegetation, etc. during post-processing. LiDAR point clouds are three-dimensional data because they can contain multiple height measurements for a given X, Y position. The main sources of uncertainty in the point locations include accuracy of the laser scanner, Inertial Measurement Unit (IMU; in the airborne case) and GPS system, registration of individual scans, and georeferencing of the resulting point cloud. Importantly, point cloud data are scattered (i.e., non-gridded), so that both point spacing and point density (number of points per m2) vary within the scan. Current topographic analysis tools are limited in their ability to work with such scattered data, although some software tools (most commonly used with TLS data), perform 3D tessellation and texture mapping on the point cloud.

DEMs are gridded height fields in which elevations are represented on a 2 dimensional map grid with constant spacing and only one height value at each horizontal grid node. As such, they are “2.5D” representations of true 3D surfaces. High resolution DEMs are generated from LiDAR data by gridding and interpolating the measured point cloud. Thus, in addition to the uncertainties in point locations described above, they contain an additional level of uncertainty because points in the DEM do not necessarily lie on the true surface. Conversion of the point cloud to digital elevation model is a destructive step, in that the original cloud cannot be regenerated from the derived DEM. Many users work with DEMs because current implementations of a number of important topographic analysis tools, such as slope and flow direction, presume gridded data.

Software tools

The software needs of the community will vary due to the differences in data types and the diversity of user needs. The major LiDAR workflow steps of direct interest to most end users begin with a point cloud that has already been merged from individual scans, georeferenced, and attributed with point intensity and return number.

Software tools, tutorials, and test data sets are needed to support both general tasks common to many users (point cloud analysis, DEM generation, and QA/QC) and application-specific tasks (stream-profile analysis, neotectonic mapping, etc.). Depending upon the application, DEM generation may occur at the end of a processing workflow to preserve the accuracy of the point data for transects, height analysis, etc. Regardless of the workflow, software tools should be diverse enough to adapt to user needs.

When working with LiDAR data, most people use multiple pieces of software because each performs certain tasks well. All GIS or remote sensing-type software packages with raster support will allow some amount of LiDAR DEM analysis and visualization. The US Army Corps of Engineers prepared a comprehensive survey of Terrain Visualization Software: http://www.tec.army.mil/TD/tvd/survey/survey_toc.html. As Table 1 indicates, current commonly used tools can vary from expensive commercial packages (e.g., Polyworks, TerraScan), to less costly commercial resources that are often site licensed in academic environments (e.g., ArcGIS, ENVI/IDL, MATLAB) to free extensions or codes for commercial packages (e.g., LiDAR Tools, GeomorphTools), to free open source software (e.g., GRASS, Points2Grid, LViz; LidarViewer, Real-time interactive Mapping System).

ArcGIS is the principal environment for 2.5D-based cartography and data integration for many earth scientists, although its 3D rendering capability is limited with the large files that characterize LiDAR DEMs. Nancy Glenn and colleagues from Idaho State University have developed a free set of LiDAR Tools which is an extension to ENVI (http://geology.isu.edu/BCAL/tools/EnviTools/index.html) (Glenn et al., 2006; Streutker and Glenn, 2006). Kelin Whipple (Arizona State University) and colleagues have developed free extensions to MATLAB and ArcGIS to extract stream profiles from DEMs and analyze their steepness index and concavity (http://www.geomorphtools.org/Tools/StPro/StPro.htm). George Hilley (Stanford) has created and released a large number of MATLAB functions that also enable analysis of DEMs in slope-area space. Members of the UC Davis W.M. Keck Center for Active Visualization in the Earth Sciences (KeckCAVES) (http://www.keckcaves.org/) have created tools to allow real-time interactive visual analysis of massive point cloud (LidarViewer) and DEM (RIMS) data (Bernardin et al., 2006; Bernardin et al., 2008; Kellogg et al., 2008) as the first steps in developing a comprehensive point cloud analysis tool (Kreylos et al., 2007; Gold et al., 2007).

Despite these many tools, there remains a considerable need for expansion of software resources that can handle the challenges posed by LiDAR point cloud and DEM data. For example, an open-source toolkit for various platforms (Matlab, C, ENVI/IDL, etc.) for basic operations is a valuable target that was identified at the June 2008 Studying Earth Surface Processes with High-Resolution Topographic Data Workshop. Furthermore, software tools that are well linked with on-line data sources or archives can take advantage of significant computational resources beyond the user’s desktops. A comprehensive software scheme for high resolution topography data should include a field computing (e.g., mobile, PDA, tablet) to desktop to grid- or “cloud”-based architecture.

Many LiDAR point clouds are initially acquired as community datasets, and all are valuable in many ways beyond the original motivation for their collection. Some are valuable as iconic datasets on which important research has been performed (e.g., Roering and others Oregon Coast Range LiDAR DEMs, etc.). Others are valuable because they serve the needs of another scientific discipline (e.g. data being valued by the ecology community for its representation of vegetative canopy may be useful for earth scientists). Data collected and preprocessed by commercial vendors and NCALM (National Center for Airborne Laser Mapping) are typically provided to the purchaser (individual PI, state or federal agency, UNAVCO) on DVD or portable hard drive. The degree to which these data are then made available to the general community and the format in which they are provided is currently quite variable. Although no single data clearinghouse for community data has been established, there are several sites where such data may be downloaded and processed. The USGS CLICK effort (http://lidar.cr.usgs.gov/) provides data primarily in raw form [unclassified LAS] as provided by the dataset owner. The CLICK site provides no data processing and minimal QA/QC. Alternatively, Web-based LiDAR data access, data management, and data processing has been pioneered by the GEON (GEON LiDAR Workflow; Crosby, et al., 2006; Jaeger-Frank, 2006) and NOAA’s LDART tool (http://www.csc.noaa.gov/crs/tcm/about_ldart.html). The GEON LiDAR system begins with user-defined selection of a subset of point data and ends with download (including dynamically generated metadata) and visualization of DEMs and derived products. Users perform point cloud data selection, interactive DEM generation and analysis, and visualization all from an internet-based portal. Users may experiment with DEM resolution and DEM generation algorithms so as to optimize terrain models for their application. By using cyberinfrastructure resources, this approach allows users to carry out computationally intensive LiDAR data processing without having appropriate resources locally. This system gives users access to datasets of interest and basic tools to process and interact with the data. But, at some point, the user will need to process the data independently for their specific science objectives.

Although reprocessing the point cloud to improve positional accuracy is generally not a high priority for the majority of users, the legacy of these datasets can be greatly extended if all raw LiDAR measurements are archived. Archiving allows the opportunity to reprocess the data in the future as algorithms develop. Establishment of such archives is critical to support change detection studies.

Additional needs at the community level include:

A single-point internet-based clearing house for LiDAR point cloud and DEM data that makes it simple for dataset holders to make their data available to the user community and for users to discover data of scientific interest. All publically-funded LiDAR missions should be required to post data on this site within a specified timeframe (1-2 years). Ideally, this system should also provide tools for users to perform basic data processing, analysis and visualization tasks (e.g. the GEON LiDAR system). The site should provide comprehensive metadata characterizing each data set and all processing steps used to produce derived products such as an attributed, classified, merged and georeferenced point cloud or a bare-earth DEM. Standards for data delivery are included in this requirement.

A data archive (possibly combined with above) for the preservation of all raw measurements collected from the laser scanner, inertial reference system, and GPS receivers during a LiDAR survey. LiDAR data are typically valuable in many ways beyond the original motivation for their collection. Thus, a data archive will ensure maximum utility and longevity for these data by making it possible to reprocess the data for different applications or as community standards and algorithms evolve. The archive is also an important resource should errors be discovered in current processing approaches.

Support for development of algorithms for conducting topographic analyses directly on the scattered point cloud data. Common operations include calculations of slope, slope-aspect, stream profiles, catchment areas, and topographic roughness and curvature. Although such analyses are widely used, current implementations generally presume gridded data. Performing such operations directly on the point cloud is appealing for several reasons. It removes the processing steps required to generate the DEM. The operations should be more accurate because they are performed directly on the measured data, rather than a model of the surface. Analysis of the point cloud directly removes the need to discard or interpolate data in areas of high or low measurement density, respectively.

Format conversion capability: no one software solution will be achieved for the entire community interested in these data. Therefore, delivery in and conversion between common file formats for both point data (LAS, ASCII) and DEM data (ASCII grid, binary grid, etc.) is necessary. Data from publicly supported data acquisitions should be released in such common non-proprietary formats.

The community would most benefit from a Wiki or similar system where users could post tools, tutorials, scripts etc. that they have found useful in building LiDAR processing workflows to address their science goals. A community forum for idea and method exchange. Existing venues that could be adopted by the community include: the HydroVent (http://pasternack.ucdavis.edu/hydrovent.html), GEON Forums (http://www.geongrid.org), the USGS CLICK Bulletin Board (http://lidar.cr.usgs.gov/), or email listserves (TLS listserv from U. New Mexico, lidar@asu.edu, GEOMORPH-L@listserv.boisestate.edu). The GEON LiDAR team (led by Chris Crosby) is building the OpenTopography Portal (http://www.opentopography.org/) would be a logical place to host such a Wiki and some of the other community-based functionality we have identified.

Development of community-oriented data systems and software libraries can be enhanced with external support for collaboration with computer scientists and employment of professional programmers to build a framework on top of which the community could develop specific tools and workflows. Support for such an effort could come from NASA or NSF collaborative geoscience initiatives. Such support will be particularly important for developing new algorithms to handle quantitative analysis of point data, because a number of these algorithmic challenges are on the frontier of scientific computing.

Educational resources

Training on technology, tools, scientific and management applications is an area in which significant impacts can be made. Enabling students, scientists, and managers to analyze their data independently and for science/management-specific needs will provide for improved application. Recent topography and LiDAR-oriented workshops were sold out (2007 Geological Society of America Meeting: New Tools for Quantitative Geomorphology: Extraction and Interpretation of Stream Profiles from Digital Topographic Data & Processing and Analysis of GeoEarthscope and Other Community LiDAR Topography Datasets http://www.geosociety.org/meetings/2007/cw_gsa.htm and UNAVCO: http://www.unavco.org/edu_outreach/uscs/2008/LiDAR_Course_2008.html). The demand for data and knowledge on how to handle and analyze high-resolution topography is very high. Such 1-2 day courses with 20-30 people are one of the most effective mechanisms for the engagement of the communities interested in the data and for the propagation of the scientific discoveries and enhanced management that come from their analysis.

In addition to training workshops, documentation via web-based tutorials and curricula needs to be created and/or improved upon. Documentation will not only provide users the knowledge about the tools but provide an opportunity for the tools to be enhanced by the community.

Finally, free, quick and easy tools for visualization of LiDAR data within a widely used system such as Google Earth (see here: http://www.cs.unc.edu/~isenburg/googleearth/) can provide education and outreach beyond the scientific community. Visualizing LiDAR data in Google Earth can provide opportunities for managers to understand the value in high resolution topographic data, further promoting its use.

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Minecode Corp. Announces Release of LiDAR-M Data Processing Software
SEATTLE, Sept. 25 /PRNewswire/ -- Minecode Corporation, a provider of
software and product development solutions for enterprise businesses,
announces the release of LiDAR-M version 3.2, a stand-alone LiDAR data
processing software developed on Microsoft .NET technology. LiDAR software
is used for displaying, viewing, editing and processing the LiDAR files.
LiDAR-M is extensively used in markets such as energy, engineering,
utility, surveying, and municipal governments.
LiDAR, which stands for "Light Detection and Ranging," is an optical
remote sensing technology that measures properties of scattered light to
find range and other information of distant targets. LiDAR sends pulses of
laser light to the ground; in the time it takes for the signal to reflect
back, the signal is measured. Then, relative heights of objects and changes
in terrain are detected.
After the LiDAR data is gathered, LiDAR-M analyzes the data and then
processes it into industry-standard file formats, such as LAS, BIN, DXF
formats.
"Unlike other similar LiDAR processing products, LiDAR-M does not
require third-party software to run," said PK Samal, President of Minecode.
"LiDAR-M has no limit on the number of data points it is able to process."
LiDAR-M has the capability to observe cloud points accurately in
different cross-sectional views. Also, LiDAR-M gives users the ability to
classify data points automatically and manually. LiDAR-M features extensive
data viewing capabilities, such as "zoom" and "pan", and delivers data
files in multiple input/output formats. The mapping-supportive
functionalities of CAD software are built into the interface.
"Our industry-standard software coupled with Minecode's vast technical
expertise supplies engineers with quality photogrammetry and LiDAR tools,"
Samal said. "Our worldwide resources and project teams cut costs and
increase speed for users."
Minecode is a privately held company that provides software and product
development solutions for medium to large enterprises and government
agencies globally. Headquartered in Bellevue, Washington, Minecode has
offices in India and the United States. Clients include small and
medium-sized businesses, Fortune 500 companies, and government agencies.

SOURCE Minecode Corporation

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QCoherent Software Releases Free LiDAR Viewer
Monday January 12th 2009

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QCoherent Software releases ArcGIS LIDAR Extension
17 August 2007, 11:17am
August 17, 2007: QCoherent Software announces the release of LP360 and LP360 Classify v1.5, the LIDAR Extension for ArcGIS.

Colorado Springs, CO USA - QCoherent Software, provider of Limitless LIDAR software tools, announces the release of LP360 v1.5 and LP360 Classify v1.5. With an advanced architecture for accessing and processing LIDAR points, LP360 has quickly become the LIDAR tool of choice for integrating LIDAR point clouds into ArcGIS. LP360 v1.5 provides ESRI users with unique new abilities to functionally leverage enormous LIDAR point cloud datasets into sophisticated geospatial analysis.

New features in LP360 v1.5 include:

* Contour exporting with annotation options for shapefile, .dgn or .dxf formats
* Shapefile point exporting utilities
* Breakline integration options
* Floating/concurrent licensing

Significant new LP360 Classify v1.5 features:

* Classification of LIDAR point clouds using GIS layers, operations, and selection sets
* Elevation conflation tools for adding LIDAR elevations and statistics to features
* Ability to develop 3D geometries and breaklines utilizing the LIDAR point cloud.

LP360 v1.5 continues QCoherent Software’s mission of developing flexible LIDAR software that enables users of LIDAR data to easily and efficiently access gigantic LIDAR point clouds in day-to-day geospatial operations. With LP360 v1.5 users can now export contours directly from LIDAR LAS files, integrate and create 3D breaklines from 2D features, classify LIDAR point clouds using familiar geospatial operations, and conflate elevation statistics to features. With exceptional standard software features and an open application programming interface (LPObjects), there is no better way to use LIDAR in ArcGIS than LP360.

For more information and a free software trial, visit QCoherent online at www.QCoherent.com, contact us by phone at 719-386-6900, or by email us at info@qcoherent.com. QCoherent Software is an innovative provider of high-capacity, integrated, Limitless LIDAR software tools. QCoherent is headquartered in Colorado Springs, CO, USA.

For more information visit:
www.QCoherent.com

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http://freegeographytools.com/200...ery-analysis-software-a-through-h

The Big List Of Satellite/Aerial Imagery Analysis Software – A Through H

In the spirit of my previous “Big List” series on free GIS programs and free metadata programs comes the Big List Of Satellite/Aerial Imagery Analysis Software. While a few of these programs are general image analysis/manipulation programs, most are specifically designed to primarily deal with some aspect of displaying and analyzing satellite or aerial imagery. The posts will group applications alphabetically rather than by function, because they can be hard to classify by function, and also because it’s easier for me that way. Descriptions and feature sets come mainly from the application websites, with the occasional random note by me at the end.

There are a number of general purpose GIS programs that also include significant satellite/aerial imagery functionality, and I’ll include those in a separate post at the end of the series.

Chips For Windows

Chips, the Copenhagen Image Processing System, is a general-purpose software package for remote sensing image processing and spatial data analysis with extensive support for NOAA AVHRR data.

Multiple RGB/pseudocolor image views with both vector and raster overlays (ESRI shapes
Vector editing and digitizing
Image rectification
Image classification
Image statistics and visualization of statistics
Image arithmetics, filtering, profiles, semivariograms, principal components, scattergram and interpolation
3D image visualization
Built-in scripting language
Note: No longer under active development.

CoastWatch Utilities – “The CoastWatch Software Library and Utilities is a package of software tools for working with earth data sets distributed by the NOAA/NESDIS CoastWatch program. The tools allow data users to easily manipulate and visualize data from the newer CoastWatch HDF files (local and network-accessible), the older CoastWatch IMGMAP files (.cwf extension), and NOAA 1b AVHRR. The CoastWatch Utilities have both graphical tools with a point-and-click interface and command-line tools for use in batch data processing scripts.”

e-Foto

A free GNU/GPL educational digital photogrammetric workstation.

Image Rectification
Interior Orientation
Exterior Orientation
Phototriangulation
Stereopair Normalization
Stereoplotting
D.E.M. and Orthorectification
Note: While program comes in an English version, the principal documention is only available in Portuguese.

Endrov

Java-based general image analysis program.

The GIMP

A free and open source equivalent to the graphics editor Photoshop, albeit not as easy to use and not as many features. Interface is still tough to use; there’s an older version with an interface skinned to to look more like Photoshop, called GIMPShop.

HEG

The HDF-EOS To GeoTIFF Conversion Tool (HEG) is a tool developed to allow a user to reformat, re-project and perform stitching/mosaicing and subsetting operations on HDF-EOS objects. The output GeoTIFF file is ingestible into commonly used GIS applications. HEG will also write to HDF-EOS Grid & SWATH formats (i.e for Subsetting purposes) and native (or raw) binary. HEG presently works with MODIS (AQUA and TERRA), ASTER, MISR, AIRS, and AMSR-E HDF-EOS data sets.

Reprojection
Spatial (geolocation) Subsetting
Band and Parameter (aka Field) Subsetting of HDF-EOS datasets
Support for MODIS, ASTER, MISR, AIRS, and AMSR-E
(Check List of Supported Products for full details)
Format Conversion of various output Format types: GeoTIFF, HDF-EOS GRID & SWATH, MultiBand GeoTIFF, Multi-Band HDF-EOS GRID & SWATH, and native binary.
Format Conversions without reprojection or manipulation of input data. Allows data to remain in original unaltered state.
Stitching (or mosaicing) HDF-EOS SWATH and GRID datasets
Stitching with combinations of Reformatting/Reprojection/Subsetting Operations
Data subsampling
Control of various parameters including output pixel resolution and output projection parameters
Metadata preservation and creation
Java GUI
Command-line interface (Useful for running batch jobs. This is usually seen in automated production environments where large quantities of granules are processed.)
Supported Platforms: LINUX, WINDOWS, SUN, SGI, and MAC OSX (built on Darwin Kernel Version 7.5.0)

HighView

The free trial version of GUI-based HighView is fully functional for band combination of 8- or 16-bit satellite imagery (e.g., global orthorectified Landsat 7 ETM+ imagery available at USGS GloVis and the Global Land Cover Facility, ASTER, SPOT, QuickBird and IKONOS), having no limitation on image size and output format. Stretched output in 24-bit BMP format and/or unstretched output in native GeoTIFF format can be readily used as base maps or backdrops in major GIS software, such as MapInfo and ArcGIS. Various options of linear and nonlinear stretches are allowed during the band combination.
The free trial version of GUI-based HighView has no expiry date.
For other modules, the trial version has the following limitations:

Pan-sharpening with local & global optimisation methods: The maximum size of input images is 32768*32768 pixels. Outputs will be limited to 24-bit BMPs only and watermarked with white stripes.
Maximum number of scenes to be processed in a batch mode is five.
Maximum size of input images for quantitative assessment modules is 128*128 pixels.
False-to-true colour simulation for G/R/NIR input is disabled.
HyperCube

HyperCube is a Macintosh and Windows application program (update information) specifically directed to the analysis and display of multi and hyperspectral imagery. This includes the static and dynamic display of the image cube and the generation of spectral classifications using both imagery and spectral libraries. In addition, HyperCube contains functions to filter, warp, mosaic, reformat, calibrate, combine, photogrammetrically project, stereo compile and to perform arithmetic on imagery and data.

Note: Has an excellent manual in PDF format, and sample data.

Other posts in the GIS Tools series

Converting E00 Vector Data To Shapefiles – A Free And Fairly Painless Approach
Simplifying Line And Polygon Shapefiles
Converting US Census TIGER Data Into Shapefiles For Free
Converting Shapefiles and ArcINFO Coverages To AutoCAD DXF Format
Converting Point Shapefiles To Text/Spreadsheet Format
Converting Text/Spreadsheet Files To Point Shapefile Format
An Easier Way To Convert Shapefiles to Text/Spreadsheet Format
Converting Text/Spreadsheet Data To Line/Area Shapefiles
Full Resolution Raster Map Combining, Subsetting And Export With The TatukGIS Viewer
Viewing Vector Data In The TatukGIS Viewer
The LizardTech Stand-Alone MRSID Viewer
Converting Raster Area Images Into Polygon Shapefiles
SAGA GIS 2.0 Released
ILWIS GIS Is Now Open Source
AVHRR Analysis Add-On For ILWIS
Advanced Image Mosaicking With Regeemy
A Free GIS Viewer (And Cheap GIS Editor) For Windows Mobile Systems
Updates For MapWindow And Saga GIS Programs
Updates For Two Open-Source GIS Programs
Putting Together A Basic Linux GIS Workstation
Free Online Courses For Open Source GIS
GIS-Oriented Linux Distributions
Tabular Terrain Elevation Data
Quick Data Gridding With QuikGrid
A Good Introduction To Geospatial Data Analysis
Converting Digital Elevation Models To Shapefile/DXF Contours
Fixing "Broken" Shapefiles
A Simple DBF Editor
Two Online Vector GIS/GPS/KML Conversion Utilities
Another Shapefile Repair Tool
Quantum GIS (qGIS) Version 0.10 Released
Online Raster Map Georeferencing/Registration With Map Rectifier
Using The Demo Version Of Global Mapper As A Raster/Vector Data Viewer
New Stable Release Of MapWindow GIS
The Big List Of Free Metadata Software I
The Big List Of Free Metadata Software II
GIS On A Stick

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9 Responses to “The Big List Of Satellite/Aerial Imagery Analysis Software – A Through H”
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joelar
Jan 27th, 2009 at 9:17 am
Awesome post Leszek – most-times the Satellite/Aerial Analysis takes a back seat with what I’m usually busy with … this is a great help to keep me “in-the-know” w/ RS.

STH
Jan 27th, 2009 at 11:38 am
Great place to find new software to test!

Melaneum
Feb 3rd, 2009 at 7:10 am
You might want to check the Orfeo Toolbox (OTB) as well: http://www.orfeo-toolbox.org

markusN
Feb 3rd, 2009 at 4:17 pm
Not to forget about GRASS GIS with strong image processing capabilities (aerial, satellite, slassification, segmentation, Lidar, …):

Introduction: http://grass.osgeo.org/grass64/manuals/html64_user/imageryintro.html

Module list: http://grass.osgeo.org/grass64/manuals/html64_user/imagery.html

Leszek Pawlowicz
Feb 3rd, 2009 at 9:36 pm
Absolutely; I include GRASS in the category of general GIS programs that have a significant image processing capability, and it’ll be covered in the final post in this series, coming soon.

The Big List Of Satellite/Aerial Imagery Analysis Software II – I Through M | Free Geography Tools
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The Big List Of Satellite/Aerial Imagery Analysis Software III – N Through R | Free Geography Tools
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The Big List Of Satellite/Aerial Imagery Analysis Software IV – S Through Z | Free Geography Tools
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The Big List Of Satellite/Aerial Imagery Analysis Programs V - GIS | Free Geography Tools
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