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.
This dataset contains 291 coregistered image pairs of RGB aerial images from IGS's BD ORTHO database. Pixel-level change and land cover annotations are provided, generated by rasterizing Urban Atlas 2006, Urban Atlas 2012, and Urban Atlas Change 2006-2012 maps.
Subpixel classification (SPC) extracts meaningful information on land-cover classes from the mixed pixels.However, the major challenges for SPC are to obtain reliable soft reference data (RD), use apt input data, and achieve maximum accuracy. This article addresses these issues and applies the support vector machine (SVM) to retrieve the subpixel estimates of glacier facies (GF) using high radiometric-resolution Advanced Wide Field Sensor (AWiFS) data. Precise quantification of GF has fundamental importance in the glaciological research.
Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance.
The data relates to a study that captured deciduous broadleaf Bidirectional Reflectance Distribution Functions (BRDFs) from the visible through shortwave-infrared (SWIR) spectral regions (350-2500 nm) and accurately modeled the BRDF for extension to any illumination angle, viewing zenith, or azimuthal angle. Measurements were made from three species of large trees, Norway maple (Acer platanoides), American sweetgum (Liquidambar styraciflua), and northern red oak (Quercus rubra).
This dataset is compiled from five years of observation from the Global Precipitation Measurement (GPM) core observatory Microwave Imager (GMI) and Dual-Frequency Precipitation Radar (DPR). Retrieved emissivites and surface backscatter cross sections are gridded at quarter-degree, monthly resolution separately for non-snow-covered land, snow-covered land, and sea ice.
The 2020 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) and the Technical University of Munich, aims to promote research in large-scale land cover mapping based on weakly supervised learning from globally available multimodal satellite data. The task is to train a machine learning model for global land cover mapping based on weakly annotated samples.
Here we present OpenSARUrban: a Sentinel-1 dataset dedicated to the content- related interpretation of urban SAR images, including a well- defined hierarchical annotation scheme, data collection, well- established procedures for dataset compilation and organization as well as properties, visualizations, and applications of this dataset.
