AROSICS
AROSICS - "Automated and Robust Open-Source Image Co-Registration Software."
AROSICS is a Python package to perform automatic subpixel co-registration of two satellite image datasets based on an image matching approach working in the frequency domain, combined with a multistage workflow for effective detection of false-positives.
It detects and corrects local as well as global misregistrations between two input images in the subpixel scale, that are often present in satellite imagery. The algorithm is robust against the typical difficulties of multi-sensoral/multi-temporal images. Clouds are automatically handled by the implemented outlier detection algorithms. The user may provide user-defined masks to exclude certain image areas from tie point creation. The image overlap area is automatically detected. AROSICS supports a wide range of input data formats and can be used from the command line (without any Python experience) or as a normal Python package.
contact: Daniel Scheffler
Ensemble Correlation
Visual Analytics approach for the correlation-based comparison of two ensemble time series.
Git:to be done
GeoMultiSens
GeoMultiSens supports the holistic processing and analysis of optical remote sensing data from different archives.
Spatial and spectral homogenization of satellite remote sensing data.
contact: Dr. Daniel Eggert, Daniel Scheffler
Habitat Sampler
Habitat Sampler (HaSa, Community Version), ein innovatives Werkzeug, das selbstständig repräsentative Referenzproben für die prädiktive Modellierung von Oberflächenklassenwahrscheinlichkeiten erzeugt. Das R-Paket kann auf alle Bilddaten angewendet werden, die Oberflächenstrukturen und -dynamik jeglicher Art auf mehreren räumlichen und zeitlichen Skalen zeigen.
Die Hauptinnovation des Tools besteht darin, dass es die Abhängigkeit von umfassenden In-situ-Bodenwahrheitsdaten oder umfassenden Trainingsdatensätzen reduziert, die eine genaue Bildklassifizierung insbesondere in komplexen Szenen einschränken. HaSa wurde von Carsten Neumann (Helmholtz-Zentrum Potsdam GFZ Deutsches GeoForschungsZentrum) im Rahmen des vom Bundesministerium für Bildung und Forschung (BMBF) geförderten Projekts NaTec - KRH entwickelt. Für eine detaillierte Beschreibung des Habitat Samplers und seiner Anwendungen siehe Neumann et al., (2020).
Kontakt: Dr. Carsten Neumann
HYSOMA
HYSOMA (Hyperspectral SOil MApper) is a software interface currently developed at the GFZ German Research Center for Geosciences. It is an experimental platform for soil mapping applications of hyperspectral imagery that allows easy implementation in the hyperspectral and non-hyperspectral communities (distribution under the idl-virtual machine) and gives the choice of multiple algorithms for each soil parameter. The main motivation for HYSOMA development is to provide experts and non-expert users with a suite of tools that can be used for soil applications. The algorithms focus on the fully automatic generation of semi-quantitative soil maps for key soil parameters such as soil moisture, soil organic carbon, and soil minerals (iron oxides, clay minerals, carbonates). Additional soil analyses tools were implemented to allow e.g. the derivation of quantitative maps based on in-situ data sets.
contact: Dr. Sabine Chabrillat
LocalPLSR
The local PLSR methodology: Quantification of soil parameters e.g. organic carbon, in field soil samples using spectrally similar soil samples from a large scale soil spectral database. For this purpose, Laboratory spectra measured from the field soil samples are required and compared with the laboratory spectra from the soil spectral database. The most similar soil samples from the soil spectral database are then used to train a separate PLSR model for each field sample. Each model is applied to the respective field sample to quantify the soil parameter. The localPLSR thus represents an alternative to the conventional quantification (in the chemical laboratory) of spectrally active soil parameters. For this purpose, laboratory spectra of the field samples need to be measured with laboratory spectrometers.
contact: Kathrin Ward
PyRQA
PyRQA conducts recurrence analysis in a massively parallel manner using the OpenCL framework. It is designed to efficiently process time series consisting of hundreds of thousands of data points.
Pip: https://pypi.org/project/PyRQA
contact: Dr. Mike Sips
SEVA
SEVA supports users in training classification models, assessing the classifier's errors, and exploring the classification results.
contact: Dr. Daniel Eggert
SICOR
SICOR is the Sensor Independent atmospheric CORrection of optical Earth Observation (EO) data from both multispectral and hyperspectral instruments. Currently, SICOR can be applied to Sentinel-2 and EnMAP data but the implementation of additional space- and airborne sensors is under development. As a unique feature for the processing of hyperspectral data, SICOR incorporates a three phases of water retrieval based on Optimal Estimation (OE) including the calculation of retrieval uncertainties. The atmospheric modeling in case of hyperspectral data is based on the MODTRAN radiative transfer code whereas the atmospheric correction of multispectral data relies on the MOMO code. The MODTRAN trademark is being used with the express permission of the owner, Spectral Sciences, Inc.
contact: Niklas Bohn
Slivisu
Visual Analytics approach for assessing the quality of simulation models with sparse and imprecise observation data.
Git: https://gitext.gfz-potsdam.de/sec15pub/slivisu
contact: Dr. Daniel Eggert
SpecHomo
Spectral homogenization of multispectral satellite data
SpecHomo is a Python package for spectral homogenization of multispectral satellite data, i.e., for the transformation of the spectral information of one sensor into the spectral domain of another one. This simplifies workflows, increases the reliability of subsequently derived multi-sensor products and may also enable the generation of new products that are not possible with the initial spectral definition.
contact: Daniel Scheffler
SPECTATION
Originating from the DBU-funded project "Monitoring of Döberitzer Heide: Vegetation and Remote Sensing", SPECTATION provides a comprehensive data survey as a reference for the calibration of hyperspectral methods, particularly with regard to modern machine learning approachs. SPECTATION holds multitemporal, spectrally continuous reflectance values (350-2500 nm) and links them with information of species abundances, structural stand characteristics and abiotic site factors in mainly natural vegetation of open habitats.
contact: Dr. Carsten Neumann