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The Big List Of Satellite/Aerial Imagery Analysis Programs V – GIS



The last in this series (parts one, two, three, and four). The previous applications have been primarily focused on remote sensing applications, but there are also a number of general purpose freeware GIS programs that include significant remote sensing analysis capabilities.

GRASS

A powerful but often difficult to use GIS program. Quantum GIS is implementing an easier interface for GRASS’s capabilities. A Java version of GRASS (JGRASS) is being built on top of uDIG, but is concentrating on hydrogeological and geomorphological capabilities for now.

  • Canonical component analysis (CCA)
  • Color composite generation
  • Edge detection
  • Frequency filtering (Fourier, convolution matrices)
  • Fourier and inverse fourier transformation
  • Histogram stretching
  • IHS transformation to RGB
  • Image rectification (affine and polynomial transformations on raster and vector targets)
  • Ortho photo rectification
  • Principal component analysis (PCA)
  • Radiometric corrections (Fourier)
  • Resampling
  • Resolution enhancement (with RGB/IHS)
  • RGB to IHS transformation
  • Texture oriented classification (sequential maximum a posteriori classification)
  • Shape detection
  • Supervised classification (training areas, maximum likelihood classification)
  • Unsupervised classification (minimum distance clustering, maximum likelihood classification)

gvSIG

gvSIG is moving towards release of version 2.0, which will add a substantial number of remote sensing analysis functions. If you want to look at an early version, there’s a 1.9 alpha release available.

- Clipping of bands and data
– Export layers
– Save from view to raster file
– Color table and gradient edition
– No data values management
– Pixel process (filters)
– Color interpretation management
– Overview creation
– Enhanced radiometric
– Histogram
– Geolocation
– Raster reprojection
– Georeferencing
– Automatic vectorization
– Band algebra
– Region of Interest (ROI) definition
– Supervised classification
– Semi-supervised classification
– Decision tree
– Transformations
– Image fusion
– Mosaic
– Scatter diagram
– Image profiles

ILWIS

Integrated Land and Water Information System, formerly commercial software, now open source.

  • Image enhancement (contrast, linear stretching)
  • Filtering
  • Band combination and compositing
  • Band ratios and indices
  • Georeferencing
  • Multi-band statistics
  • Principal component analysis
  • Image arithmetic
  • Image fusion (e.g. pan-sharpening)
  • Image classification

MicroDEM

A very strong terrain/DEM analysis program, but has other applications as well, including remote sensing.

  • Contrast enhancement
  • Filters
  • Band histograms
  • Correlation (covariance) matrix
  • Scattergrams
  • Band ratios and normalized indices (e.g. NDVI)
  • Principal components analysis
  • Multi-band merges
  • Image training and classification
  • Hyperspectral image analysis (AVIRIS)
  • Image averaging
  • Pan-sharpening

SAGA

Not primarily a remote sensing application, but has some modules for grid manipulation, statistics and analysis that could be useful.

Spring

  • LANDSAT, SPOT, ERS-1 and NOAA/AVHRR Data input;
  • Registration and Geometric Correction;
  • Image Mosaic with gray level equalization;
  • Image Enhancement by Histogram Manipulation;
  • Spatial Filtering;
  • IHS and Principal Components Transformations;
  • Arithmetical Operations;
  • Pixel Values Reading;
  • Maximum-likelihood pixel-based classifier;
  • Image Segmentation and Region Classifiers (Supervised and Unsupervised);
  • LANDSAT and SPOT Images Restoration;
  • Morphological Filters for Images;
  • Mixture Models;
  • Markov-based Techniques for Image Post-Classification;
  • Radar Image Processing.
  • And if you want to create your own remote sensing analysis program, or add capabilities to a GIS, there’s the Orfeo Toolbox (OTB), an open-source library/API for image processing:

    • image access: optimized read/write access for most of remote sensing image formats, meta-data access, simple visualization;
    • sensor geometry: sensor models, cartographic projections;
    • radiometry: atmospheric corrections, vegetation indices;
    • filtering: blurring, denoising, enhancement;
    • fusion: image pansharpening;
    • feature extraction: interest points, alignments, lines;
    • image segmentation: region growing, watershed, level sets;
    • classification: K-means, SVM, Markov random fields;
    • change detection.

    HT to Melaneum in comments.


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