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.


The submitted file is a supplemental of IEEE JSTAR article with DOI: 10.1109/JSTARS.2019.2955955

The dataset consists of three sections. The first section briefly reviews the subpixel classification (SPC) techniques and justifies the use of support vector machines in this study. It also highlights the key contribution of this study in the field of glaciology.

The second section details the steps involved in correcting the geometric, atmospheric, and topographic effects in the satellite images. It also specifies about the conversion of thermal band data to surface temperature.

The third section indicates how the ancillary layers used in this study are helpful in the segregation of various glacier facies.

Besides this, three tables (A.1, A.2, and A.3) are given. Table A.1 lists the ancillary layers used in this study, their source and applicability. Table A.2 provides a brief review on the SPC of different land-covers. The reported accuracies were compared with those obtained in this study. Table A.3 quantitatively illustrates how the ancillary layers are able to distinguish among various glacier facies.       

The dataset also contains seven figures (Figs. A.1, A.2, A.3, A.4, A.5, A.6, and A.7) depicting the research approach, correlation between SPC-derived and reference glacier facies area, SPC outputs from eight-class case using spectral data, SPC outputs from three-class case using spectral data, SPC-derived and reference glacier facies area obtained for different cases, SPC accuracy statistics, and texture-based differentiation of glacier facies respectively.

Each of these sections, tables and figures have been referred in the main article at appropriate places.


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.


=================  Authors  ===========================

Lichao Mou,

Yuansheng Hua,

Pu Jin,

Xiao Xiang Zhu,


=================  Citation  ===========================

If you use this dataset for your work, please use the following citation:


  title= {{ERA: A dataset and deep learning benchmark for event recognition in aerial videos}},

  author= {Mou, L. and Hua, Y. and Jin, P. and Zhu, X. X.},

  journal= {IEEE Geoscience and Remote Sensing Magazine},

  year= {in press}



==================  Notice!  ===========================

This dataset is ONLY released for academic uses. Please do not further distribute the dataset on other public websites.


Dataset for change detection (before and after change) are generated by matlab code.


Hyperspectral data set includes Indian Pines  and   Salinas A


The data relates to a study to captured deciduous broadleaf Bidirectional Scattering Distribution Functions (BSDFs) from the visible through shortwave-infrared (SWIR) spectral regions (350-2500 nm) and accurately modeled the BSDF 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).


There are three different file types in this database.  The first are .raw files that are ascii files of the estimated BSDF data from measurements (Note that the measurements are really bi-conical), the second are .py python files for reading, plotting, and fitting the data to a microfacet model.  The last file type are .txt files of the microfacet fit parameters previously found. 


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.


These data are stored as numpy (.npy) files. Sample reading and plotting code is provided by the Jupyter notebook.


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.

Last Updated On: 
Mon, 01/25/2021 - 09:03

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.


Cloud-free imageries, acquired from Landsat 8 OLI during 2016 to 2018, were used to delineate the extents of the glacial lakes in the mountainous terrain of CPEC


The Contest: Goals and Organisation

 The 2019 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Johns Hopkins University (JHU), and the Intelligence Advanced Research Projects Activity (IARPA), aimed to promote research in semantic 3D reconstruction and stereo using machine intelligence and deep learning applied to satellite images.