Debian GIS Remote Sensing packages

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.

GDAL is a translator library for raster geospatial data formats. As a library, it presents a single abstract data model to the calling application for all supported formats. The related OGR library (which lives within the GDAL source tree) provides a similar capability for simple features vector data.

GDAL supports 40+ popular data formats, including commonly used ones (GeoTIFF, JPEG, PNG and more) as well as the ones used in GIS and remote sensing software packages (ERDAS Imagine, ESRI Arc/Info, ENVI, PCI Geomatics). Also supported many remote sensing and scientific data distribution formats such as HDF, EOS FAST, NOAA L1B, NetCDF, FITS.

OGR library supports popular vector formats like ESRI Shapefile, TIGER data, S57, MapInfo File, DGN, GML and more.

This package contains utility programs, based on GDAL/OGR library, namely gdal_translate, gdalinfo, gdaladdo, gdalwarp, ogr2ogr, ogrinfo, ogrtindex.

GMTSAR is an open source Interferometric Synthetic Aperture RADAR (InSAR) 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:

• a preprocessor for each satellite data type (ERS-1/2, Envisat, ALOS-1, TerraSAR-X, COSMO-SkyMed, Radarsat-2, Sentinel-1A/B, and ALOS-2) to convert the native format and orbital information into a generic format;
• an InSAR processor to focus and align stacks of images, map topography into phase, and form the complex interferogram;
• 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 pdf files and KML images for Google Earth. A set of shell scripts has been developed for standard 2-pass processing as well as geometric image alignment for stacking and time series.

The ENVISAT Product Reader API is a set of C-source code files supporting developers who want to use MERIS, AATSR, and ASAR data products of the ESA ENVISAT satellite in their software.

The main use case for the C API is the ingestion of ENVISAT data into

• new scientific algorithms developed for the MERIS, AATSR or ASAR sensors or even all of them,
• existing scientific software packages written in C or C++, or COTS software systems which allow for extension using a C or C++ interface.

This package contains development files (headers and static libraries).

GDAL is a translator library for raster geospatial data formats. As a library, it presents a single abstract data model to the calling application for all supported formats. The related OGR library (which lives within the GDAL source tree) provides a similar capability for simple features vector data.

GDAL supports many popular data formats, including commonly used ones (GeoTIFF, JPEG, PNG and more) as well as the ones used in GIS and remote sensing software packages (ERDAS Imagine, ESRI Arc/Info, ENVI, PCI Geomatics). Also supported many remote sensing and scientific data distribution formats such as HDF, EOS FAST, NOAA L1B, NetCDF, FITS.

OGR library supports popular vector formats like ESRI Shapefile, TIGER data, S57, MapInfo File, DGN, GML and more.

This package contains the files needed to develop a software that will use the GDAL/OGR (headers, static objects, configuration script).

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 includes the development files to build programs that use the OSSIM library.

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 includes core tools that use the OSSIM library to perform some basic tasks.

PyEPR provides Python bindings for the ENVISAT Product Reader C API (EPR API) for reading satellite data from ENVISAT ESA (European Space Agency) mission.

PyEPR, as well as the EPR API for C, supports ENVISAT MERIS, AATSR Level 1B and Level 2 and also ASAR data products. It provides access to the data either on a geophysical (decoded, ready-to-use pixel samples) or on a raw data layer. The raw data access makes it possible to read any data field contained in a product file.

This is the Python 2 version of the package.

It's main task is to convert low level satellite data into a format understood by mpop (http://github.com/mraspaud/mpop). The primary purpose is to support Geostationary satellite data (level 1.5) but there is also support for the reading of some polar orbiting SAR data.

A more sophisticated interface to satellite data objects is supported by mpop.

Currently it handles data from all current Meteosat Second Generation (MSG) satellites, Meteosat 7, GOES 11-15, MTSAT's, and GOMS, all a retrieved via EUMETCast.

In addition mipp handles Synthetic Apperture Radar (SAR) data from Terrscan-X, Cosmo-Sky Med, and Radarsat 2.

This package is part of the PyTroll toolset.

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.

ORFEO Toolbox (OTB) is distributed as an open source library of image processing algorithms. OTB is based on the medical image processing library ITK and offers particular functionalities for remote sensing image processing in general and for high spatial resolution images in particular.

This package contains the Python 2 API that can be used to create Python versions of existing OTB applications provided by the libotb-apps package.

Pycoast is a Python package to add coastlines, borders and rivers to raster images using data from the GSHHG (previously known as GSHHS) and WDBII datasets.

This package is part of the PyTroll toolset.

This package contains pycoast for Python 2.

pykdtree is a kd-tree implementation for fast nearest neighbour search in Python. The aim is to be the fastest implementation around for common use cases (low dimensions and low number of neighbours) for both tree construction and queries.

The implementation is based on scipy.spatial.cKDTree and libANN by combining the best features from both and focus on implementation efficiency.

The interface is similar to that of scipy.spatial.cKDTree except only Euclidean distance measure is supported.

Queries are optionally multithreaded using OpenMP.

This is the Python 2 version of the package.

Python package for computing orbital parameters from TLE files, and making diverse astronomical computations.

This package is part of the PyTroll toolset.

This is the Python 2 version of the package.

Pyresample is a Python package for resampling (reprojection) of earth observing satellite data. It handles both resampling of gridded data (e.g. geostationary satellites) and swath data (polar orbiting satellites).

Pyresample can use multiple processor cores for resampling. Pyresample supports masked arrays.

This is the Python 2 version of the package.

SNAP is a general purpose gene finding program suitable for both eukaryotic and prokaryotic genomes. SNAP is an acroynm for Semi-HMM-based Nucleic Acid Parser.

NCO is a suite of programs known as operators. The operators are stand-alone, command-line programs executable in a POSIX shell. Operators take one or more netCDF files as input, perform operations (e.g., averaging, hyperslabbing), and produce a netCDF output file. NCO was originally designed to manipulate and analyze climate data, though it works on any netCDF format datasets.

You would use ncview to get a quick and easy, push-button look at your NetCDF files. You can view simple movies of the data, view along various dimensions, take a look at the actual data values, change color maps, invert the data and other simple visual operations.

Contains the programs ncdump and ncgen which convert NetCDF files to ASCII and back, respectively. NetCDF (network Common Data Form) is an interface for scientific data access and a freely-distributed software library that provides an implementation of the interface. The netCDF library also defines a machine-independent format for representing scientific data. Together, the interface, library, and format support the creation, access, and sharing of scientific data.

DORIS is an Interferometric Synthetic Aperture Radar (InSAR) processor developed by the Delft Institute of Earth Observation and Space Systems of Delft University of Technology.

Interferometric products and endproducts such as Digital Elevation Models and displacement maps can be generated with this software from Single Look Complex data. Data from the satellites ERS, ENVISAT (first ENVISAT interferogram, DEM, and perspective view, JERS (first JERS interferogram), and RADARSAT (first RADARSAT interferogram) can be processed with the Doris software.

Two-dimensional phase unwrapping is the process of recovering unambiguous phase data from a 2-D array of phase values known only modulo 2pi rad.

There are many applications, like Magnetic Resonance Imaging (MRI), Synthetic Aperture Radar (SAR), fringe pattern analysis, tomography and spectroscopy, which as part of their fundamental operation depend upon the extraction of a phase signal from their input image. Usually the phase is available in a form that suffers from 2-pi phase jumps due to the use of the mathematical arctangent function, which produces an inherently wrapped output. This wrapped phase is unusable until the phase discontinuities are removed.

SNAPHU is an implementation of the Statistical-cost, Network-flow Algorithm for Phase Unwrapping particularly suitable for SAR interferometry applications. This algorithm poses phase unwrapping as a maximum a posteriori probability (MAP) estimation problem, the objective of which is to compute the most likely unwrapped solution given the observable input data. Because the statistics relating the input data to the solution depend on the measured quantity, SNAPHU incorporates three built-in statistical models, for topography data, deformation data, and smooth generic data. The posed optimization problem is solved approximately with use of network-flow techniques.

As SNAPHU uses an iterative optimization procedure, its execution time depends on the difficulty of the interferogram. In single-tile mode the required memory is on the order of 100 MB per 1000000 pixels in the input interferogram.

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.

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.