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Convolutional Neural Network (CNN) has attracted much attention for feature learning and image classification, mostly related to close range photography. As a benchmark work, we trained a relatively large CNN to classify SAR image patches into five different categories, where the image patches tiled and annotated from a typical TerraSAR-X spotlight scene of Wuhan, China. The neural network designed...
Filtering process plays significant roles in the generation of a digital elevation model (DEM) from interferogram obtained by interferometric synthetic aperture radar (InSAR). In order to remove the distortion of a so-called singular unit (SU), this paper proposes two novel filtering techniques which both exhibit strong nonlinearity. The first method attempts to remove the distortion by focusing on...
SAR images from Italian COSMO-SkyMed mission can have a significant impact on the production and updates of land cover maps. However, for the full exploitation of the data and their application to nationwide extensions, robust automatic procedures need to be designed. In this paper we present the preliminary results obtained by the implementation of a processing scheme using COSMO-SkyMed images to...
At present, the performance of image registration mainly depends on the extracted features in feature-based image registration. However, due to the speckle noise, synthetic aperture radar (SAR) image registration will have a lower accuracy and less robustness. For this purpose, we design a deep neural network (DNN) for SAR image registration, using the DNN to learn the image features, automatically...
The use of fully polarimetric SAR data for oil spill detection is relatively new and shows great potential for operational off-shore platform monitoring. Greater availability of these kind of SAR data calls for a development of time critical processing chain capable of detecting and distinguishing oil spills from ‘look-alikes’. This paper describes the development of an automated Near Real Time (NRT)...
This paper focuses on the problem of built-up areas detection in single high-resolution SAR images. In consideration of the rich structure information of built-up areas in high-resolution SAR images, we put forward a multiscale CNN model to extract multiscale trained features directly from image patches to detect built-up areas. By processing features extraction and classification as a whole, we overcome...
SAR Polarimetry has become a valuable tool in spaceborne SAR based sea ice analysis. The two major objectives in SAR based remote sensing of sea ice is on the one hand to have a large coverage of the imaged ground area, and on the other hand to obtain a radar response that carries as much information as possible. Whereas single-polarimetric acquisitions of existing sensors offer a wide coverage on...
In recent years, unmanned aerial vehicles (UAVs) have been widely used for civilian remote sensing applications. One of them is to assess damages due to man-made or natural disasters and search for bodies in the debris. In this work, we propose to support avalanche search and rescue (SAR) operation with UAVs. The image acquired by the UAV is processed through a pre-trained convolutional neural network...
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