Debian GIS Remote Sensing packages

GMTSAR is an open source Interferometric SAR (Synthetic Aperture Radar) processing system designed for users familiar with Generic Mapping Tools (GMT). The code is written in C and will compile on any computer where GMT and NETCDF are installed. The system has three main components:

  1. a preprocessor for each satellite data type (e.g., ERS, Envisat,
     and ALOS) to convert the native format and orbital information
     into a generic format
  2. an InSAR processor to focus and align stacks of images, map
     topography into phase, and form the complex interferogram
  3. a postprocessor, mostly based on GMT, to filter the interferogram
     and construct interferometric products of phase, coherence, phase
     gradient, and line-of sight displacement in both radar and
     geographic coordinates.

GMT is used to display all the products as postscript files and KML images for Google Earth. A set of C-shell scripts has been developed for standard 2-pass processing as well as image alignment for stacking and time series. ScanSAR processing is also possible but requires a knowledgeable user.

A common architecture for all Sentinel Toolboxes is being jointly developed by Brockmann Consult, Array Systems Computing and C-S called the Sentinel Application Platform (SNAP).

The SNAP architecture is ideal for Earth Observation processing and analysis due the following technological innovations: Extensibility, Portability, Modular Rich Client Platform, Generic EO Data Abstraction, Tiled Memory Management, and a Graph Processing Framework.

Feature Highlights: * Common architecture for all Toolboxes * Very fast image display and navigation even of giga-pixel images * Graph Processing Framework (GPF): for creating user-defined

  processing chains
* Advanced layer management allows adding and manipulation of new overlays
  such as images of other bands, images from WMS servers or ESRI shapefiles
* Rich region-of-interest definitions for statistics and various plots

• Easy bitmask definition and overlay
• Flexible band arithmetic using arbitrary mathematical expressions
• Accurate reprojection and ortho-rectification to common map projections,
• Geo-coding and rectification using ground control points
• Automatic SRTM DEM download and tile selection
• Product library for scanning and cataloging large archives efficiently
• Multithreading and Multi-core processor support
• Integrated WorldWind visualisation

SNAP also includes: * the Sentinel-1 Toolbox (S1TBX) consists of a collection of processing

  tools, data product readers and writers and a display and analysis
  application to support the large archive of data from ESA SAR missions
  including SENTINEL-1, ERS-1 & 2 and ENVISAT, as well as third party
  SAR data from ALOS PALSAR, TerraSAR-X, COSMO-SkyMed and RADARSAT-2.
  The various processing tools could be run independently from the
  command-line and also integrated within the graphical user interface.
  The Toolbox includes tools for calibration, speckle filtering,
  coregistration, orthorectification, mosaicking, data conversion,
  polarimetry and interferometry.
* the Sentinel-2 Toolbox consists of a rich set of visualisation,
  analysis and processing tools for the exploitation of optical
  high-resolution products including the upcoming Sentinel-2 MSI sensor.
  As a multi-mission remote sensing toolbox, it also provides support for
  third party data from RapidEye, SPOT, MODIS (Aqua and Terra),
  Landsat (TM) and others.
* the Sentinel-3 Toolbox consists of a rich set of visualisation,
  analysis and processing tools for the exploitation of OLCI and SLSTR
  data from the upcoming Sentinel-3 mission.
  As a multi-mission remote sensing toolbox, it also supports the ESA
  missions Envisat (MERIS & AATSR), ERS (ATSR), SMOS as well as third
  party data from MODIS (Aqua and Terra), Landsat (TM),
  ALOS (AVNIR & PRISM) and others.
  The various tools can be run from an intuitive desktop application or
  via a command-line interface.
  A rich application programming interface allows for development of
  plugins using Java or Python.
* the project SMOS Toolbox for SNAP (SMOS-Box) has been brought into life
  in order to support users of data acquired by ESA’s Soil Moisture and
  Ocean Salinity (SMOS) mission.
  SNAP and the SMOS-Box are user tools which ESA/ESRIN is providing free
  of charge to the Earth Observation Community.

Dan Stahlke's GDAL contributed tools are a collection of useful programs to perform common raster operations. The included tools are: gdal_contrast_stretch, gdal_dem2rgb, gdal_get_projected_bounds, gdal_landsat_pansharpi, gdal_list_corners, gdal_merge_simple, gdal_merge_vrt gdal_raw2geotiff, gdal_trace_outline, gdal_wkt_to_mask, gdal_make_ndv_mask.

GSDView open source edition is a lightweight viewer for geo-spatial data and products written in Python and Qt4. GSDView is modular and has a simple plug-in architecture. It is mainly intended to be a graphical front-end for the GDAL library and tools.

The MapReady Remote Sensing Tool Kit accepts level 1 detected SAR data, single look complex SAR data, and optical data from ASF and some other facilities. It can terrain correct, geocode, apply polarimetric decompositions to multi-pol SAR data, and save to several common imagery formats including GeoTIFF. Other software included in the package are an image viewer, metadata viewer, a projection coordinate converter, and a variety of command line tools.

Open Source Software Image Map (OSSIM) is a high performance engine for remote sensing, image processing, geographical information systems and photogrammetry. It has been actively developed since 1996.

Designed as a series of high performance software libraries, it is written in C++ employing the latest techniques in object-oriented software design.

The library provides advanced remote sensing, image processing, and geo-spatial functionality. A quick summary of OSSIM functionality includes ortho-rectification, precision terrain correction, rigorous sensor models, very large mosaics, and cross sensor fusions, a wide range of map projections and datums, and a large range of commercial and government data formats. The architecture of the library supports parallel processing with mpi (not enabled), a dynamic plugin architecture, and dynamically connectable objects allowing rapid prototyping of custom image processing chains.

This package contains the OSSIM graphical interface.

Open Source Software Image Map (OSSIM) is a high performance engine for remote sensing, image processing, geographical information systems and photogrammetry. It has been actively developed since 1996.

Designed as a series of high performance software libraries, it is written in C++ employing the latest techniques in object-oriented software design.

The library provides advanced remote sensing, image processing, and geo-spatial functionality. A quick summary of OSSIM functionality includes ortho-rectification, precision terrain correction, rigorous sensor models, very large mosaics, and cross sensor fusions, a wide range of map projections and datums, and a large range of commercial and government data formats. The architecture of the library supports parallel processing with mpi (not enabled), a dynamic plugin architecture, and dynamically connectable objects allowing rapid prototyping of custom image processing chains.

This package contains the OSSIM data shared between packages.

Open Source Software Image Map (OSSIM) is a high performance engine for remote sensing, image processing, geographical information systems and photogrammetry. It has been actively developed since 1996.

Designed as a series of high performance software libraries, it is written in C++ employing the latest techniques in object-oriented software design.

The library provides advanced remote sensing, image processing, and geo-spatial functionality. A quick summary of OSSIM functionality includes ortho-rectification, precision terrain correction, rigorous sensor models, very large mosaics, and cross sensor fusions, a wide range of map projections and datums, and a large range of commercial and government data formats. The architecture of the library supports parallel processing with mpi (not enabled), a dynamic plugin architecture, and dynamically connectable objects allowing rapid prototyping of custom image processing chains.

This package contains the OSSIM planet utilities.

Open Source Software Image Map (OSSIM) is a high performance engine for remote sensing, image processing, geographical information systems and photogrammetry. It has been actively developed since 1996.

Designed as a series of high performance software libraries, it is written in C++ employing the latest techniques in object-oriented software design.

The library provides advanced remote sensing, image processing, and geo-spatial functionality. A quick summary of OSSIM functionality includes ortho-rectification, precision terrain correction, rigorous sensor models, very large mosaics, and cross sensor fusions, a wide range of map projections and datums, and a large range of commercial and government data formats. The architecture of the library supports parallel processing with mpi (not enabled), a dynamic plugin architecture, and dynamically connectable objects allowing rapid prototyping of custom image processing chains.

This package contains the OSSIM planet Qt utility.

Open Source Software Image Map (OSSIM) is a high performance engine for remote sensing, image processing, geographical information systems and photogrammetry. It has been actively developed since 1996.

Designed as a series of high performance software libraries, it is written in C++ employing the latest techniques in object-oriented software design.

The library provides advanced remote sensing, image processing, and geo-spatial functionality. A quick summary of OSSIM functionality includes ortho-rectification, precision terrain correction, rigorous sensor models, very large mosaics, and cross sensor fusions, a wide range of map projections and datums, and a large range of commercial and government data formats. The architecture of the library supports parallel processing with mpi (not enabled), a dynamic plugin architecture, and dynamically connectable objects allowing rapid prototyping of custom image processing chains.

This package contains the OSSIM core plugins.

The Polarimetric SAR Data Processing and Educational Tool aims to facilitate the accessibility and exploitation of multi-polarised SAR datasets including those from ESA Third Party Missions (ALOS PALSAR), Envisat ASAR Alternating Polarisation mode products, RADARSAT-2 and TerraSAR-X.

A wide-ranging tutorial and comprehensive documentation provide a grounding in polarimetry and polarimetric interferometry necessary to stimulate research and development of scientific applications that utilise such techniques; the toolbox of processing functions offers users the capability to implement them.

PolSARpro is developed under contract to ESA by a consortium comprising IETR at the University of Rennes 1, The Microwaves and Radar Institute (HR) of DLR and AEL Consultants, together with Dr Mark Williams. The initiative is a direct result of recommendations made at the POLInSAR Workshops held at ESRIN in January 2003, 2005 and 2007.

The Meteorological Post-Processing package is a Python library for generating RGB products for meteorological remote sensing. As such it can create RGB composites directly from satellite instrument channels, or take advantage of precomputed PGEs.

It is designed to be easily extendable to support any meteorological satellite by the creation of plugins. In the base distribution, it is provided support for Meteosat 7, 8, 9, MTSAT1R, MTSAT2, GOES 11, GOES 12, GOES 13 through the use of mipp, and Noaa 15, 16, 17, 18, 19, and Metop A through the use of aapp and ahamap.

Reprojection of data is also available through the use of the pyresample package.

This package is part of the PyTroll toolset.

Automated DORIS Environment (adore) is a set of bash scripts to ease use of TU-DELFT's DORIS software. ADORE stands for Automated DORIS Environment. It is development started at the University of Miami Geodesy Group, to help researchers generate interferograms with ease. Just like DORIS it is an open source project and it comes with the same license. ADORE tries to provide a streamlined user interface for generating interferograms with DORIS and has some additional features for displaying and exporting the results, and time series analysis.

EOLi (Earth Observation Link) is the European Space Agency's client for Earth Observation Catalogue and Ordering Services. Using EOLi, you can browse the metadata and preview images of Earth Observation data acquired by the satellites ENVISAT, ERS, Landsat, IKONOS, DMC, ALOS, SPOT, Kompsat, Proba, JERS, IRS, Nimbus, NOAA, SCISAT, SeaStar, Terra/Aqua.

Scientific Users with a registered account can order or download products of various processing levels.

The software consists of a program, lodr, to list the content of the Orbital Data Records (ODRs), a subroutine library, getorb.a, that can be ported to some other program to interpolate the orbits and any given moment, and an example program getorb to demonstrate the use of the getorb.a library.

IMCORR takes two images and a series of input parameters and attempts to match small subscenes (called 'chips') from the two images. The program uses a fast fourier transform based version of a normalized cross-covariance method.

The most common use of this type of algorithm in image processing is to accurately locate tie-point pairs in two images to coregister them. However, if the images are already coregistered by other means, the algorithm may be used to find the displacements of moving features, provided that the features show little change in their appearance, and that the motion is strictly translational.

The correlation, peak finding, and error estimation routines in IMCORR are derived from FORTRAN subroutines from the Land Analysis System software (LAS) written at NASA Goddard Space Flight Center and USGS Eros Data Center.

IMCORR consists of a C code wrapper which makes the use of the routines more straightforward and automated for velocity-mapping applications.

Opticks is an expandable remote sensing and imagery analysis software platform that is free and open source. Unlike other competing tools, you can add capability to Opticks by creating an extension. Opticks provides the most advanced extension capability of any other remote sensing tool on the market.

Features:

• Supports the following file formats: NITF 2.0/2.1, GeoTIFF, ENVI, ASPAM/PAR, CGM, DTED, Generic RAW, ESRI Shapefile, HDF5, AVI, MPEG, JPEG, GIF, PNG, BMP
• Zoom, pan, rotate spatially large datasets
• Quickly layer GIS features, annotations, results, and other information over your data to provide context
• Many image display controls such as colormap, histogram, transparency, etc
• Support for datasets larger than four gigabytes
• Analysts can quickly combine steps using graphical wizards
• Support for processing data in it's native interleave of BIP, BSQ or BIL
• Get extensions to drop in new capability

Points2Grid generates Digital Elevation Models (DEM) using a local gridding method. The local gridding algorithm computes grid cell elevation using a circular neighbourhood defined around each grid cell based on a radius provided by the user. This neighbourhood is referred to as a bin, while the grid cell is referred to as a DEM node.

Up to four values - minimum, maximum, mean, or inverse distance weighted (IDW) mean - are computed for points that fall within the bin. These values are then assigned to the corresponding DEM node and used to represent the elevation variation over the neighbourhood represented by the bin. If no points are found within a given bin, the DEM node receives a value of null.

The Points2Grid service also provides a null filing option, which applies an inverse distance weighted focal mean via a square moving window of 3, 5, or 7 pixels to fill cells in the DEM that have null values.

GIAnT is a suite of Python libraries and scripts that implement various published time-series InSAR algorithms in a common framework. GIAnT was developed for

  1, Rapid generation of time-series products from interferogram.
  2. Direct comparison of time-series InSAR products using different
     algorithms.
  3. Cal-Val for time-series InSAR.

The toolbox currently includes implementations of SBAS, N-SBAS and MInTS algorithms. The toolbox also includes support for using weather model data for stratified troposphere corrections and GPS data for orbital error corrections.

PyAPS is a python implementation of stratified tropospheric correction methods using inputs from global atmospheric models (GAM).

The package currently works with ERA-Interim Reanalysis, NARR and MERRA data, but can be easily extended to support more weather models.

Generic Python BUFR file reader based on the ECMWF BUFR library. File support is similar to the ECMWF BUFR library and the package currently only supports read-only mode.

This module is part of the Pytroll project.

ROI_PAC allows researchers in the area of topography and surface change to apply Interferometric Synthetic Aperture Radar (InSAR) methods.

InSAR is the synthesis of conventional SAR techniques and interferometry techniques that have been developed over several decades in radio astronomy and radar remote sensing, and in recent years has opened entirely new application areas for radar in the earth system sciences, including topographic mapping and geodesy [v. e.g., Thompson et al. 1986, Massonnet and Feigl 1998, Rosen et al. 2000].

ROI_PAC uses raw radar data, ancillary information from telemetry and navigation solutions, and digital elevation models (DEM; externally provided or interferometrically derived) to produce a variety of derived data products, including the full resolution images, interferograms, phase images measured as principal value and continuously "unwrapped," DEMs, and error estimates.

The Varres package implements a curvature-based quadtree-like interferogram resampler.

The original matlab package has been extended to include support for using a predefined map for resampling and estimation of an approximate covariance matrix for the samples.