Remote Sensing

Automatic classification of sensitive content in remote sensing images, such as drug crop sites, is a promising task as it can aid law-enforcement institutions fighting illegal drug dealers worldwide, while, at the same time, it can help monitoring legalized crops in countries that regulate them. However, existing art on detecting drug crops from remote sensing images is limited in some key factors not taking full advantage of the available hyperspectral info for analysis.


High-fidelity, physics-based multichannel radar data cube provided by the DARPA KASSPER project. This data is ideal for analyzing space-time adaptive processing (STAP) algorithms since both sample data and truth data are provided.


This dataset contains aerial images acquired with a medium format digital camera and point clouds collected using an airborne laser scanning (ALS) unit, as well as ground control points and direct georeferencing data. The flights were performed in 2014 over an urban area in Presidente Prudente, State of São Paulo, Brazil, using different flight heights. These flights covered several features of interest for research, including buildings of different sizes and roof materials, roads and vegetation.


This study deals with derivation of an exact expressions for the current distribution and the input impedance of a circular loop antenna over a lossy half-space. the analysis is based on the image method, the direct integration of the vector potential, and the spherical function expansion. the results for the current distribution of this study are in very good agreement with those corresponding results available in the literature, which checks the correctness of formulations of the study.   




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.