Abstract
Classification based on polarimetric synthetic aperture radar (PolSAR) images is an emerging technology, and recent years have seen the introduction of various classification methods that have been proven to be effective to identify typical features of many terrain types. Among the many regions of the study, the Hunshandake Sandy Land in Inner Mongolia, China stands out for its vast area of sandy land, variety of ground objects, and intricate structure, with more irregular characteristics than conventional land cover. Accounting for the particular surface features of the Hunshandake Sandy Land, an unsupervised classification method based on new decomposition and large-scale spectral clustering with superpixels (ND-LSC) is proposed in this study. Firstly, the polarization scattering parameters are extracted through a new decomposition, rather than other decomposition approaches, which gives rise to more accurate feature vector estimate. Secondly, a large-scale spectral clustering is applied as appropriate to meet the massive land and complex terrain. More specifically, this involves a beginning sub-step of superpixels generation via the Adaptive Simple Linear Iterative Clustering (ASLIC) algorithm when the feature vector combined with the spatial coordinate information are employed as input, and subsequently a sub-step of representative points selection as well as bipartite graph formation, followed by the spectral clustering algorithm to complete the classification task. Finally, testing and analysis are conducted on the RADARSAT-2 fully PolSAR dataset acquired over the Hunshandake Sandy Land in 2016. Both qualitative and quantitative experiments compared with several classification methods are conducted to show that proposed method can significantly improve performance on classification.
Highlights
In recent years, desertification of grasslands has become increasingly severe due to continuous drought and grassland overload in Inner Mongolia Autonomous Region, China
In 3.2.1 of Section 3, the cross-polarization scattering components of the polarimetric synthetic aperture radar (SAR) (PolSAR) data are removed by orientation angle compensation (OAC) and phase angle rotation (PAR), which solves the problem that the arrangement direction of the building is not parallel to the radar azimuth direction
Spectral clustering with superpixels of [33] (HED-SC), unsupervised classification based and spectral clustering with superpixels of [33] (HED-SC), unsupervised classification on the use of Neumann decomposition and Random Forest Classifier (ND-RF), and the based on the use of Neumann decomposition and Random Forest Classifier (ND-RF), and proposed classification method (ND-large-scale spectral clustering (LSC))
Summary
Desertification of grasslands has become increasingly severe due to continuous drought and grassland overload in Inner Mongolia Autonomous Region, China. Hunshandake Sandy Land, has become one of the main dust sources, so it is of great significance to monitor sandy land [1]. The development of spaceborne remote sensing technology and a massive increase in monitoring data provide many effective means to monitor the sandy land echo-environment [1,2]. The polarimetric SAR (PolSAR) which can obtain relatively complete polarimetric scattering information through four different channels (HH, HV, VH and VV) is referred to as a fully PolSAR [5]. Theory and practice show that the polarization feature of electromagnetic echoes is sensitive to the shape, texture, and other physical features of targets, and the differences in polarization signatures help
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