基于多尺度压缩感知金字塔的极化干涉SAR图像分类

提出了一种新的基于多尺度压缩感知(Compressed sensing, CS)金字塔的分类方法, 用于合成孔径雷达(Synthetic aperture radar, SAR)图像的分类. 首先通过原始图像上的小波平滑和特征提取构建多尺度极化干涉特征空间, 然后利用压缩感知提取每一个尺度上图像子块的观测域特征并在数据域重建稀疏特征, 最后组合多尺度的稀疏特征生成最终用于分类的多尺度金字塔表达. 针对稀疏编码和一般金字塔算法的局限性, 提出了基于压缩感知和多尺度金字塔的方法, 利用观测矩阵降低特征维数的优势的同时, 对SAR图像的纹理特征进行不同尺度的分析. 在国内首批极化干涉SAR数据上的实验证明了上述算法的有效性.

PolInSAR Image Classification Based on Compressed Sensing and Multi-scale Pyramid

In this paper, we propose a novel approach based on compressed sensing (CS) and multi-scale pyramid in synthetic aperture radar (SAR) image classification. Firstly, a multi-scale PolInSAR feature space is constructed by wavelet transform and feature extraction on the original image; then, CS provides a transform for the measurement domain and recovers the sparse features in the data domain on the image patches in each scale; finally, the combination of multi-scale sparse features generates the final multi-scale pyramid representation of the image for classification. Motivated by the limitations of sparse coding and general pyramid methods, we not only take the advantages of observation matrix in dimension reduction, but also perform analysis on texture features in different scales. Experimental results on the first batch of PolInSAR data show the presented approach's efficiency.