Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime. The design of image processing techniques for  synthetic aperture radar applications requires tests and validation on real and synthetic images. The GRSS benchmark database supports the desing and analysis of algorithms to deal with SAR and PolSAR data.

Last Updated On: 
Tue, 02/08/2022 - 17:46
Citation Author(s): 
Nobre, R. H.; Rodrigues, F. A. A.; Rosa, R.; Medeiros, F.N.; Feitosa, R., Estevão, A.A., Barros, A.S.

These last decades, Earth Observation brought quantities of new perspectives from geosciences to human activity monitoring. As more data became available, artificial intelligence techniques led to very successful results for understanding remote sensing data. Moreover, various acquisition techniques such as Synthetic Aperture Radar (SAR) can also be used for problems that could not be tackled only through optical images. This is the case for weather-related disasters such as floods or hurricanes, which are generally associated with large clouds cover.


The dataset is composed of 336 sequences corresponding to areas in West and South-East Africa, Middle-East, and Australia. Each time series is located in a given folder named with the sequence ID (0001... 0336).

Two json files, S1list.json and S2list.json are provided to describe respectively the Sentinel-1 and Sentinel-2 images.The keys are the total number of images in the sequence, the folder name, the geography of the observed area, and the description of each image in the series. The SAR images description contains also the URLs to download the images.Each image is described by its acquisition date, its label (FLOODING: boolean), a boolean (FULL-DATA-COVERAGE: boolean) indicating if the area is fully or partially imaged, and the file prefix. For SAR images the orbit (ASCENDING or DESCENDING) is also indicated.

The Sentinel-2 images were obtained from the Mediaeval 2019 Multimedia Satellite Task [1] and are provided with Level 2A atmospheric correction. For one acquisition, there are 12 single-channel raster images provided corresponding to the different spectral bands.

The Sentinel-1 images were added to the dataset. The images are provided with radiometric calibration and range doppler terrain correction based on the SRTM digital elevation model. For one acquisition, two raster images are available corresponding to the polarimetry channels VV and VH.

The original dataset was split into 269 sequences for the train and 68 sequences for the test. Here all sequences are in the same folder.


To use this dataset please cite the following papers:

Flood Detection in Time Series of Optical and SAR Images, C. Rambour,N. Audebert,E. Koeniguer,B. Le Saux,  and M. Datcu, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020, 1343--1346

The Multimedia Satellite Task at MediaEval2019, Bischke, B., Helber, P., Schulze, C., Srinivasan, V., Dengel, A.,Borth, D., 2019, In Proc. of the MediaEval 2019 Workshop


This dataset contains modified Copernicus Sentinel data [2018-2019], processed by ESA.

[1] The Multimedia Satellite Task at MediaEval2019, Bischke, B., Helber, P., Schulze, C., Srinivasan, V., Dengel, A.,Borth, D., 2019, In Proc. of the MediaEval 2019 Workshop


Synthetic Aperture Radar (SAR) images can be extensively informative owing to their resolution and availability. However, the removal of speckle-noise from these requires several pre-processing steps. In recent years, deep learning-based techniques have brought significant improvement in the domain of denoising and image restoration. However, further research has been hampered by the lack of availability of data suitable for training deep neural network-based systems. With this paper, we propose a standard synthetic data set for the training of speckle reduction algorithms.


In Virtual SAR we have infused images with varying level of noise, which helps in improving the accuray fo blind denoising task. The holdout set can be created using images from USC SIPI Aerials database and the the provided matlab script (preprocess_holdout.m) tested on Matlab R2019b.


The usage for research purposes is for free. If you use this dataset, please cite the following paper along with the dataset: Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms


PS_DISP is a trial bundled script written on bash shell and Matlab code. The script requires Generic Mapping Tools (GMT) and Matlab Software and runs under Linux operating system. The purpose of PS DISP is to generate 2D or 3D vectors displacement from InSAR both ascending and descending orbit either from the mean velocity or time-series data. The 1.5 beta version includes the computation of the 3D field using an optimized approach with variance component estimation (VCE).


Accurate information about crop rotation is essential for administrators, managers and various government departments for assessment, monitoring, and management of various resources for crop escalation. Radar remote sensing, because of its all-weather capability and assured uninterrupted data supply can show a substantial part in the evaluation of crop rotation.


In tropical/subtropical regions, the favorable climate associated with the use of agricultural technologies, such as no-tillage, minimum cultivation, irrigation, early varieties, desiccants, flowering inducing and crop rotation, makes agriculture highly dynamic. In this paper, we present the Campo Verde agricultural database. The purpose of creating and sharing these data is to foster advancement of remote sensing technology in areas of tropical agriculture, primarily the development and testing of methods for crop recognition and agricultural mapping.


Synthetic Aperture Radar is a monitoring solution which is especially well fitted to maritime surveillance. The large swath widths, time-independent, weather resistant observations can be very useful for the detection of ships typically invisible to other forms of monitoring. A dataset which contains 43 Sentinel-1 Extended Wide Swath images and 3 RADARSAT-2 ScanSAR Narrow images between the 6th October 2014 to the 22 July 2015 is described and presented here.