For introducing the advantages of feature learning and multilayer network in the interpretation of Polarimetric synthetic aperture radar (PolSAR) image, a classification algorithm based on deep convolutional neural network is proposed, and is used for PolSAR image classification. Firstly, a special convolutional neural network (CNN) for PolSAR image is constructed, secondly, a large number of P...

Segmentation and classification of polarimetric SAR (PolSAR) imagery are very important for interpretation of PolSAR data. This paper presents a new object-oriented classification method which is based on Statistical Region Merging (SRM) segmentation algorithm and a two-level hierarchical clustering technique. The proposed method takes full advantage of the polarimetric information contained in...

When High Resolution (HR) is required, single-look processing of SAR images may be desirable. The multi-look G distribution has been known to fit homogeneous to extremely heterogeneous data accurately. However, it does not reduce to single-look form for PolSAR data. To eliminate this limitation, the new single-look polarimetric G distribution has been proposed, which has single-look Kp and G0 p...

The composite kernel feature fusion proposed in this paper attempts to solve the problem of classifying polarimetric synthetic aperture radar (PolSAR) images. Here, PolSAR images take into account both polarimetric and spatial information. Various polarimetric signatures are collected to form the polarimetric feature space, and the morphological profile (MP) is used for capturing spatial inform...

In the field of microwave remote sensing, polarimetric synthetic aperture radar (POLSAR) image analysis is one of the most significant and effective imaging techniques since scattering matrix obtained by POLSAR system may provide us detailed three dimensional high resolution target information [1],[2]. It has been reported in Ref.[3] that three-component scattering model, which is based on simp...

Here, we examined multitemporal behavior of fully polarimetric SAR (PolSAR) parameters at L-band in relation to the stem volume of boreal forests. The PolSAR parameters were evaluated in terms of their temporal consistency, inter-dependence and suitability for forest stem volume estimation across several seasonal conditions (frozen, thaw and unfrozen). The satellite SAR data were represented by...

For many applications where High Resolution (HR) Synthetic Aperture Radar (SAR) images are required, like urban structures detection, road map detection, marine structures and ship detection etc., single-look processing of SAR images may be desirable. The G family of distributions have been known to fit homogeneous to extremely heterogeneous Polarimetric SAR (PolSAR) data very well and can be v...

In this paper we demonstrate that simple yet flexible modelling of multilook polarimetric synthetic aperture radar (PolSAR) data can be obtained through a relaxation of the Wishart model. The degrees of freedom of the complex Wishart distribution is treated as a spatially nonstationary parameter, which is allowed to vary between thematic classes and segments of the PolSAR scene.

Classification of land cover types is one important application of polarimetric synthetic aperture radar (PolSAR) remote sensing. There are numerous features that can be extracted from PolSAR images. Among them, eigenvalues λi, entropy H, alpha angle α, and anisotropy A are effective and popular tools for the analysis and quantitative estimation of the physical parameters. Nevertheless, the spe...

Inspired by enormous success of fully convolutional network (FCN) in semantic segmentation, as well as the similarity between semantic segmentation and pixel-by-pixel polarimetric synthetic aperture radar (PolSAR) image classification, exploring how to effectively combine the unique polarimetric properties with FCN is a promising attempt at PolSAR image classification. Moreover, recent research...