Use Of Polarimetric Information Research Articles

Automatic land cover classification of multi-resolution dualpol data using convolutional neural network (CNN)

Synthetic Aperture Radar is an interesting topic of research for scientists & researchers as it is associated with polarimetric information which helps to detect surface & subsurface features of land, sea, and ice. Classical techniques include the use of polarimetric information to simplify SAR image interpretation and to classify it for various earth observation applications. The deep learning (DL) techniques like Convolutional Neural Network (CNN), extract useful information from an image (here dual polarimetric SAR dataset) about the land surface to segment or classify the dataset for various earth applications. In the current research paper convolutional neural network is used to automatically classify RISAT-1 dataset over the Mumbai region for land cover classification. Also impact of patch size variation was studied. In addition, the efficiency of the CNN model was tested using an approach similar to transfer learning approach on multi resolution images (different multilooked images) i.e. CNN was trained twice on different resolution images; one trained on coarser resolution and tested on comparatively higher resolution datasets and other vice versa. It was found that increasing the patch size for convolution classified the image more accurately but at the same time it smoothens the output image. Also, CNN model trained on low spatial resolution image predicted better the higher spatial resolution image as compared to the reverse scenario.

Polarimetric Convolutional Network for PolSAR Image Classification

The approaches for analyzing the polarimetric scattering matrix of polarimetric synthetic aperture radar (PolSAR) data have always been the focus of PolSAR image classification. Generally, the polarization coherent matrix and the covariance matrix obtained by the polarimetric scattering matrix only show a limited number of polarimetric information. In order to solve this problem, we propose a sparse scattering coding way to deal with polarimetric scattering matrix and obtain a close complete feature. This encoding mode can also maintain polarimetric information of scattering matrix completely. At the same time, in view of this encoding way, we design a corresponding classification algorithm based on convolution network to combine this feature. Based on sparse scattering coding and convolution neural network, the polarimetric convolutional network is proposed to classify PolSAR images by making full use of polarimetric information. We perform the experiments on the PolSAR images acquired by AIRSAR and RADARSAT-2 to verify the proposed method. The experimental results demonstrate that the proposed method get better results and has huge potential for PolSAR data classification. Source code for sparse scattering coding is available at https://github.com/liuxuvip/Polarimetric-Scattering-Coding.

Effects of BOW Model With Affinity Propagation and Spatial Pyramid Matching on Polarimetric SAR Image Classification

The bag of visual words (BOW) model has attracted a great deal of attention in high-resolution remote sensing image classification. Related studies based on polarimetric synthetic aperture radar (PolSAR) image classification are also still challenging. This paper presents a new method of PolSAR image classification based on the BOW model and supplemented by an affinity propagation clustering algorithm (AP), as well as a spatial pyramid matching (SPM) technique (AP_SPM_BOW). Its classification effect is analyzed compared to those of BOW models with other clustering algorithms (e.g., K -means, meanshift, Gaussian mixture model), as well as the ML classification method by using two types of PolSAR images with different resolutions collected from EMISAR and RADARSAT-2 sensors. These results demonstrate that the proposed AP_SPM_BOW model is not only a stable and effective tool for PolSAR image classification but also more suitable for the analysis of high-resolution PolSAR images. In addition, the use of polarimetric information in BOW models can effectively improve the classification accuracy.

Passive polarimetric IR target classification

Automatic detection and recognition of targets by means of passive IR sensors suffer from limitations due to lack of sufficient contrast between the targets and, their backgrounds and among the facets of a target. A set of novel algorithms is designed and tested that uses the target and background Stokes parameters for detection, segmentation, and classification of targets, In these algorithms, it is assumed that for each pixel in the image data three of the four Stokes parameters are provided. This assumption is justified because we have developed a custom designed Polarimetric IR (PIR) imaging sensor that generates three of the Stokes parameters at each pixel location, in real-time. The empirical performance of the above algorithms, in terms of the probabilities of detection, false alarm rate, segmentation accuracy, and recognition probabilities are computed. The comparison of these results with the results associated with intensity-only imaging sensor (nonpolarimetric IR sensory data) shows that use of polarimetric information can noticeably improve the performance.