Abstract
Understanding the physical properties and scattering mechanisms contributes to synthetic aperture radar (SAR) image interpretation. For single-polarized SAR data, however, it is difficult to extract the physical scattering mechanisms due to lack of polarimetric information. Time-frequency analysis (TFA) on complex-valued SAR image provides extra information in frequency perspective beyond the “image” domain. Based on TFA theory, we propose to generate the subband scattering pattern for every object in complex-valued SAR image as the physical property representation, which reveals backscattering variations along slant-range and azimuth directions. In order to discover the inherent patterns and generate a scattering classification map from single-polarized SAR image, an unsupervised hierarchical deep embedding clustering (HDEC) algorithm based on TFA (HDEC-TFA) is proposed to learn the embedded features and cluster centers simultaneously and hierarchically. The polarimetric analysis result for quad-pol SAR images is applied as reference data of physical scattering mechanisms. In order to compare the scattering classification map obtained from single-polarized SAR data with the physical scattering mechanism result from full-polarized SAR, and to explore the relationship and similarity between them in a quantitative way, an information theory based evaluation method is proposed. We take Gaofen-3 quad-polarized SAR data for experiments, and the results and discussions demonstrate that the proposed method is able to learn valuable scattering properties from single-polarization complex-valued SAR data, and to extract some specific targets as well as polarimetric analysis. At last, we give a promising prospect to future applications.
Highlights
YNTHETIC Aperture Radar (SAR) image understandingS is important but challenging
We propose an unsupervised deep learning method of hierarchical deep embedding clustering based on time-frequency analysis (HDEC-Time-frequency analysis (TFA)) to study the physical scattering properties for high-resolution single-polarized complex-valued Synthetic Aperture Radar (SAR) data from time-frequency perspective
In order to extract the physical scattering properties from single-polarized SAR data, a hierarchical deep embedding clustering based on time-frequency analysis method (HDEC-TFA) is proposed
Summary
We propose an unsupervised deep learning method of hierarchical deep embedding clustering based on time-frequency analysis (HDEC-TFA) to study the physical scattering properties for high-resolution single-polarized complex-valued SAR data from time-frequency perspective. The contributions of this paper are as follows: 1) For every target in single-polarized SAR images with complex-values, a sub-band scattering pattern is extracted with time-frequency analysis approach which reveals the backscattering variations along range and azimuth directions. It is regarded as a representation of the target physical scattering property.
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More From: IEEE Transactions on Geoscience and Remote Sensing
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