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, 11/12/2019 - 10:38
Citation Author(s): 
Nobre, R. H.; Rodrigues, F. A. A.; Rosa, R.; Medeiros, F.N.; Feitosa, R., Estevão, A.A., Barros, A.S.

A synthetic signal dataset of 12 different modulations (including PSK, QPSK, 8PSK, QFSK, 8FSK, 16APSK, 16QAM, 64QAM, 4PAM, LFM, DSB-SC, and SSBSC) with different DOAs (discrete angles ranging from -60° to 60° with the step size of 1°) is generated using MATLAB 2021a. Regarding the signal model configuration for the data generation, we specify a uniform linear antenna array of M = 5 elements to acquire incoming signals having N = 1024 envelope complex samples, thus conducting an I/Q data array of size 1024 × 2 × 5.


A long-strip synthetic aperture radar interferometric (InSAR) measurement based on multiframe image mosaicking is currently the realizable approach to measure large-range ground deformation. As the spatial range of the mosaicked images increases, the degree of ground effects are more significant, and using empirical global ocean tidal models or plane fitting to correct the OTL displacement will produce large errors in a region with a complex coastline.



Europe is covered by distinct climatic zones which include semiarid, the Mediterranean, humid subtropical, marine,

humid continental, subarctic, and highland climates. Land use and land cover change have been well documented in the

past 200 years across Europe1where land cover grassland and cropland together make up 39%2. In recent years, the

agricultural sector has been affected by abnormal weather events. Climate change will continue to change weather




<p>Our data set contains five subsets, which are Seadata, RCSdata, RD_SeaImage, BP_SeaImage and SSHdata. Seadata is the data of simulated sea. RCSdata is the data of sea surface backward scattering coefficient. RD_SeaImage is the simulated images of sea surface. BP_SeaImage is the simulated images of sea surface. SSHdata is the sea surface height data.</p>


<p>The datasets were used for Our manuscript “Sea Surface Imaging Simulation for 3D Interferometric Imaging Radar Altimeter”(DOI: 10.1109/JSTARS.2020.3033164) . This readme file details the specific meaning of the data contained in the five subsets of this dataset. All data are stored in .mat and can be opened and used with MATLAB. When using data sets, it is recommended to use the version above matlab 2016.</p>


This dataset contains satellite images of areas of interest surrounding 30 different European airports. It also provides ground-truth annotations of flying airplanes in part of those images to support future research involving flying airplane detection. This dataset is part of the work entitled "Measuring economic activity from space: a case study using flying airplanes and COVID-19" published by the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. It contains modified Sentinel-2 data processed by Euro Data Cube.


Details regarding dataset collection and usage are provided at


This dataset is created for ocean front evolution trend recognition and tracking. 


This dataset  provide researchers a benchmark to develop applicable and adaptive harbor detection algorithms.


SI-STSAR-7 is a labeled spatiotemporal dataset for sea ice classification based on SAR images. The dataset is produced from 80 Sentinel-1 A/B SAR scenes during the two freezing periods of Hudson Bay from October 2019 to May 2020 and from October 2020 to April 2021, which are provided by the Copernicus Open Access Center. The Sentinel-1 SAR images were preprocessed with noise floor reduction and incidence angle dependence correction before use. 



SI-STSAR-7 is based on Copernicus Sentinel-1 data provided by European Commission and European Space Agency (ESA) and weekly regional ice charts and weekly regional ice data provided by Canadian Ice Service (CIS).

  1. If we infringe on the rights of European Commission, ESA or CIS, please contact us immediately and we will remove it in time.
  2. The ownership and copyright of the SI-STSAR-7 dataset belong to Shanghai Ocean University (SHOU). The dataset is distributed freely, but those who use the dataset must also comply with the relevant data usage agreements of the European Commission, ESA and CIS.
  3. Please cite the DOI: 10.21227/d6kp-s174 if you use this dataset in any form in publications. You may not redistribute our material without our written permission.