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はラスター地理空間データフォーマットの翻訳ライブラリです。ライブラリと して、全サポートフォーマットに対しアプリケーション呼び出しのためのシングル 抽出データモデルを提供します。その関連するOGRライブラリィ (GDALソースツリー の中に含まれる) はシンプルな特色のベクタデータに対し類似の性能を備えます。

GDAL は一般的に使われる形式 (GeoTIFF、JPEG、PNG 等) や、 GIS と同様のリモートセンシングのソフトウェアパッケージ (ERDAS イメージ、ESRI Arc/Info、ENVI、PCI Geomatics) を含めた、 40 以上のポピュラーなデータフォーマットをサポートします。 また、多くのリモートセンシングと HDF、EOS FAST、NOAA L1B、NetCDF、FITS のような科学的なデータ配布フォーマットもサポートします。

OGRライブラリはESRIシェープファイル、TIGERデータ、S57、MapInfoファイル、 DGN、GML等、一般的なベクタフォーマットをサポートします。

このパッケージは GDAL/OGRライブラリ、つまり gdal_translate、gdalinfo、 gdaladdo、gdalwarp、ogr2ogr、ogrinfo、ogrtindex に基づいたユーティリティプログ ラムを含みます。

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.

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.

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.

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.

NetCDF ファイルを ASCII ファイルへの変換、又はその逆変換を行う ncdump および ncgen プログラムが含まれます。NetCDF (network Common Data Form) は、科学データへのアクセス用インターフェイスおよびインターフェイスの 実装を供給するフリーで配布されているソフトウェアインターフェイスです。 netCDF ライブラリは、科学データを表現するためのマシンに依存しない フォーマットも定義します。 合わせて、インターフェイス、ライブラリ、そしてフォーマットは科学データの 作成、アクセス、そして共有機能をサポートします。

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.

ADORE is a shell environment designed to simplify and streamline SAR interferometry with the DORIS InSAR processor. It is an attempt to simplify processing of different interferometric stacks like, single-master-stack, or short-baselines. It is highly configurable and provides sensible default options. Each DORIS step can be run individually.

BEST is a collection of executable software tools that has been developed to facilitate the use of ESA SAR data.

The purpose of the Toolbox is not to duplicate existing commercial packages, but to complement them with functions dedicated to the handling of SAR products obtained from the ASAR (Advanced Synthetic Aperture Radar) onboard Envisat and the AMIs (Active Microwave Instrument) onboard ERS 1/2.

BEST (Basic Envisat SAR Toolbox) is a collection of executable software tools that has been designed to facilitate the use of ESA (the European Space Agency) SAR data. It operates according to user-generated parameters files.

BESTGUI allows you to easily generate the parameters files for the BEST tools and launch the processing from the GUI. It provides:

• enhanced menus and toolbars
• persistent preferences
• smart environment handling
• colorized output log
• progress bars for processing completion
• the ability of stopping a running task from the GUI (stop button)
• a dialog for the AoI specification
• output preview for all (and not only) the tools that have an image as output

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.

The Next ESA SAR Toolbox (NEST) is a user friendly open source toolbox for reading, post-processing, analysing and visualising the large archive of data (from Level 1) from ESA SAR missions including ERS-1 & 2, ENVISAT and in the future Sentinel-1. In addition, NEST supports handling of products from third party missions including JERS-1, ALOS PALSAR, TerraSAR-X, Radarsat-2 and Cosmo-Skymed.

NEST helps the remote sensing community by supporting the handling of various SAR products and complimenting existing software packages. NEST has been built using the BEAM Earth Observation Toolbox and Development Platform and it covers the functionality of the older Basic Envisat SAR Toolbox BEST. NEST is currently undergoing development with periodic new releases. The major new functionality in NEST over BEST is an integrated viewer and orthorectification and mosaicking of SAR images.

NEST is extensible with an API that allows users to easily create their own plugin Readers, Processors and Writers. NEST is open source under the GNU General Public License (GNU GPL). If you are interested in contributing by developing a reader or writer for a product please contact us.

NEST is being developed by Array Systems Computing Inc. of Toronto Canada under ESA Contract number 20698/07/I-LG. InSAR functionalities are being developed by a joint effort of PPO.labs, Delft University of Technology and Array.

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.