Department of Computer Science and Information Systems
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Item Optimal use of polarimetric signature on PALSAR-2 data for land cover classification(IEEE, 217) Phartiyal, Gopal SinghSAR data is playing key role in monitoring, the current status or change in, the land cover. For unsupervised SAR image classification, polarization signatures can play a significant role. Since it is difficult to obtain specific polarization signature of real land cover, it is customary to represent them with standard canonical structures polarization signatures. A critical analysis of the complex signatures of real targets is essential thereafter it is also a challenge to decide the thresholds or class boundary value on the correlation images. Therefore, in this paper an attempt has been made to critically analyze the polarimetric signature of complex targets and based on the correlation image analysis an OTSU multi-thresholding based approach is proposed to decide the individual class boundary values which will finally help in building a decision tree (DT) based classification technique. For this purpose L band fully polarimetric SAR data (PALSAR-2) has been used. DT class thresholds are computed using OTSU multi-thresholding method, scatter plot method, and a priori information. Obtained results reveal that complementary features like polarization signatures can help in identification as well as classification of land surface objects significantly by the proposed method.Item Improved utilization of polsar polarization signatures using convolutional-deep neural nets for land cover classification(IEEE, 2019-11) Phartiyal, Gopal SinghNormalized Euclidean distance (NED) and normalized signature correlation mapper (NSCM) are most popularly used pattern classifiers with polarization signatures (PSs) based polarimetric synthetic aperture radar (PolSAR) data applications. These methods are not able to fully exploit the PSs as they do not exploit the spatial context or pattern of PSs which is essential. Improved utilization of PSs is still required for PolSAR applications such as agriculture crop classification and monitoring. In this study, convolutional deep neural networks (C-DNNs) are introduced and utilized as pattern classifiers for PS classification. C-DNNs have the ability to consider and control the influence of local neighborhood pixels during classification. Therefore, in this study C-DNNs are utilized to extract and exploit subtle changes between PSs of land covers to improve classification performance. Comparison with NED and NSCM classifiers signify the contribution of C-DNNs by improved performance in PolSAR data classification.Item Improved mapping of flood affected villages in India: a novel three-stage approach using PolSAR polarization signatures and ensembled dilated CNNs(Taylor & Francis, 2023-11) Phartiyal, Gopal SinghDuring floods, updated and accurate information on affected human settlements helps save lives and reduces time to rescue. Therefore, approaches that can provide reliable information during floods via the use of all-weather and real-time functional technology is highly needful. The study presented here attempts to efficiently and precisely map villages in the Indian sub-continent during floods via a three-stage approach which uses PolSAR data. However, an accurate segregation of villages in India even with PolSAR data is challenging because the built-up structures in the villages of rural India are closely placed and are randomly oriented w.r.t. each other. This condition either hinders their segregation or otherwise induces false alarms during extraction. More descriptive land cover characterization features and powerful feature classifiers may address this challenge. The study in this paper proposes a novel approach to efficiently detect and map flood affected villages which utilize polarization signatures from PolSAR imagery, ensemble-of -dilated-convolutions based CNNs, apriori knowledge and image morphology. The approach broadly involves three stages: first, built-up area extraction from a PolSAR image: second, detection of villages in a built-up area image and third, identification and mapping of villages that are affected by the flood. In the first stage, an ensemble of varying dilated-convolutions based novel CNN classifier which directly utilizes PolSAR-2 polarization signatures (PSs) in window-mode as features are developed to extract built-up areas. The second stage provides a novel village detection filter based on apriori knowledge and image morphology to detect actual villages and mask out the false objects. Finally, in the third stage, flood affected villages are mapped via a series of morphological operations based degree-of-intersection measure. Experiments are conducted on both simulated and natural flooded area datasets. Experimental results show 81% detection accuracy and 100% mapping performance of the proposed approach which indicates its potential as an effective flood affected village mapping system.