基于多维金字塔表达和AdaBoost的高分辨率SAR图像城区场景分类算法

提出了多维金字塔表达算法, 并使用基于多维金字塔表达的AdaBoost实现了高分辨率合成孔径雷达(Synthetic aperture radar, SAR)图像的城区场景分类. 多维金字塔表达算法首先在局部特征的各维计算金字塔表达矢量, 再将所有的金字塔表达矢量连接起来构成多维金字塔表达矢量. 多维金字塔表达算法克服了金字塔表达算法在处理高维局部特征时, 遇到的输出金字塔表达矢量的区分力受计算效率制约的问题. 本文分别在一个TerraSAR-X图像库和一张大幅TerraSAR-X图像上比较基于金字塔表达的AdaBoost和基于多维金字塔表达的AdaBoost的分类性能. 实验结果表明, 与前者相比, 后者显著提高了计算效率同时保证了分类精度.

Urban Scene Classification Based on Multi-dimensional Pyramid Representation and AdaBoost Using High Resolution SAR Images

This paper presents a new image representation algorithm called multi-dimensional pyramid representation (MPR), and combines MPR and AdaBoost (MPR-AdaBoost) for urban scene classification using high resolution SAR images. MPR calculates a pyramid representation (PR) vector in each dimension of local feature and combines the PR vectors together to get an MPR vector. The computational complexity of PR is high when the local feature is high-dimensional, which leads PR vector to lose discriminative information in real applications. MPR overcomes these limitations. Its computational complexity is low and the MPR vector has discriminative information, even when the local feature is high-dimensional. Using a TerraSAR-X data set and a TerraSAR-X image, AdaBoost based on PR (PR-AdaBoost) and MPR-AdaBoost are compared. The experimental results have shown that MPR-AdaBoost gives comparable results and reduces the computational cost.