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The potential of active learning (AL) methods for improving the marine oil spills identification system is exploited using 10-year(2004–2013) RADARSAT data. Six basic AL methods are proposed according to the uncertainty criteria and coupled with the support vector machine(SVM) classifier. As many as 56 commonly used features are used for the classification. The AUC measures are estimated using the...
In this paper the Supervised Locally Linear Embedding (SLLE) algorithm is introduced into polarimetric SAR (PolSAR) feature dimensionality reduction (DR) and land cover classification. SLLE technique, as a supervised nonlinear manifold learning method, can obtain a low-dimensional embedding space which preserves both the local geometric property of high-dimensional data and discriminative information...
ESA's Scientific Exploitation of Operational Missions (SEOM) programme represents a pathfinder for science and innovation addressing the needs and requirements of the Earth system science community in terms of providing novel observations, new algorithms and products that will be a driver for new and innovative scientific discoveries. The current paper aims to provide a brief overview of the various...
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...
One of the major problems of keypoint-based alignment of SAR and optical images is that keypoint operators react to very different object structures in both image types. This leads to a small mutual overlap in the corresponding sets of keypoints. This paper proposes to cast the task of keypoint detection as a classification problem. A machine-learning based classifier is trained to predict whether...
In this letter, an improved kernel density estimation (KDE) constant false alarm rate (CFAR) method is proposed for ship detection in single polarization synthetic aperture radar (SAR) images. The proposed method consists of a target enhancement filter, an adaptive KDE bandwidth estimation method and an improved KDE-CFAR. The gravity-based target enhancement filter is utilized to remove the inhomogeneity...
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...
A new method for Polarimetric Synthetic Aperture Radar (PolSAR) terrain classification based on Deep Sparse Filtering Network (DSFN) is proposed in this paper. It uses a novel deep learning network to learn features from the input raw data automatically. And the spatial information between pixels on PolSAR image is combined into the input data. Moreover, unlike the conventional deep networks, the...
The current paper aims to provide a brief overview of the ESA activities relevant to Polarimetric SAR missions mission development and exploitation, present achievements and discuss future opportunities for research.
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)...
Land cover change detection has long been a hot field in polarimetric synthetic aperture radar (SAR) applications. In certain cases, we care not only the changed areas but also from which type to another. This paper presents a supervised urban land cover change types identification method using a series of polarimetric descriptors from SAR observables and polarimetric decomposition. The normalized...
Recognizing targets in synthetic aperture radar (SAR) images is an important, yet challenging problem in SAR image interpretation. In traditional methods, the 2-D image data is rearranged into vectors and regressed to its label by a vector where the structure information is lost. Multiple rank regression (MRR) method directly manipulation on matrix data by applying a multiple-rank left projecting...
The exploitation of the high revisit time (8–16 days) by the COSMO-SkyMed® (CSK®) satellites is an important opportunity for agricultural mapping. This study aims at evaluating CSK® potentiality to classify different crop types, with CSK® multi-temporal images collected over the agricultural site of Marchfeld, in Austria. Two different time series of CSK® HIMAGE SAR scenes, at 3m resolution, 9 at...
A method of building height extraction from multi-polarization SAR imagery, taking radarsat-2 as an example, was proposed based on backscatter model. First, the connected component of double-scatter of the buildings in the image was analyzed and its contribution to radar cross section was got simultaneously—a case study in urban areas of Beijing, China. Then, optimal polarized combination was utilized,...
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...
In this paper, a classification method based on multi-layer network and transfer learning has been developed for synthetic aperture radar (SAR) images inspired by recent successful deep learning methods. Multi-layer network has excellent performance in the classification of optical images, while its application for SAR images is restricted by the limited quantity of SAR imagery training data. Given...
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