In the last decades it often has been shown that Multilayer Perceptrons (MLPs) are powerful function approximators. They were successfully applied to a lot of different classification problems. However, originally they only deal with real valued numbers. Since PolSAR data is a complex valued signal this paper propose the usage of Complex Valued Neural Networks (CVNNs), which are an extension of...

In recent years, the use of Polarimetric Synthetic Aperture Radar (PolSAR) data in different applications dramatically has been increased. In SAR imagery an interference phenomenon with random behavior exists which is called speckle noise. The interpretation of data encounters some troubles due to the presence of speckle which can be considered as a multiplicative noise affecting all coherent i...

In this paper, we present a weakly supervised classification method for a large polarimetric SAR (PolSAR) imagery using multi-modal markov aspect model (MMAM). Given a training set of subimages with the corresponding semantic concepts defined by the user, learning is based on markov aspect model which captures spatial coherence and thematic coherence. Classification experiments on RadarSat-2 Po...

Traditional pixel-based classification methods yield poor results when applied to synthetic aperture radar (SAR) imagery because of the presence of the speckle and limited spectral information in SAR data. A novel classification method, integrating polarimetric target decomposition, object-oriented image analysis, and decision tree algorithms, is proposed for land use and land cover (LULC) clas...

Due to the low information content of individual SAR images, single-band SAR data do not provide highly accurate land cover classification. However, in areas under risk where rapid land cover mapping is required, the advantages of SAR which include cloud penetration and day/night acquisition, are evident in comparison to optical data. The main research goal of this study is to fuse different fr...

In recent years, sparse representation-based techniques have shown great potential for pattern recognition problems. In this paper, the problem of polarimetric synthetic aperture radar (PolSAR) image classification is investigated using sparse representation-based classifiers (SRCs). We propose to take advantage of both polarimetric information and contextual information by combining sparsity-b...

We present a modified version of the polarimetric whitening filter (PWF) for target detection in single-look complex images captured by polarimetric synthetic aperture radar (PolSAR). The modified version enables the derivation of the sampling distribution of the PWF under several product model distributions for the polarimetric scattering vector. Specificly, a constant false alarm rate (CFAR) ...

After an earthquake, rapidly and accurately obtaining building damage information can help to effectively guide the implementation of the emergency rescue and can reduce disaster losses and casualties. Using a single post-earthquake fully-polarimetric synthetic aperture radar (PolSAR) image to interpret building damage information not only involves a guaranteed data source but is also easy and ...

The objective of this study is to develop models based on both optical and L-band Synthetic Aperture Radar (SAR) data for above ground dry biomass (hereafter AGB) estimation in mountain forests. We chose the site of the Loveh forest, a part of the Hyrcanian forest for which previous attempts to estimate AGB have proven difficult. Uncorrected ETM+ data allow a relatively poor AGB estimation, bec...