基于局部竞争策略的极化SAR图像精细分类

合成孔径雷达（Synthetic Aperture Radar, SAR）成像技术已经成为一种高分辨对地观测的重要手段之一，而极化SAR图像地物分类一直是其中的研究热点。基于复Wishart分布的最大似然（Maximum Likelihood,ML）分类器是最经典的极化SAR图像分类算法之一，但由于地物类型的复杂性、区域的不均匀性等原因使得基于像素的ML-Wishart分类器的分类精度不高。针对这个问题，本文提出了一种基于复Wishart分布的局部最大后验概率（Maximum a Posteriori，MAP）竞争方法，该算法通过计算伪先验概率，并在每个像素的局部窗口中实施MAP分类器，可以提高复杂区域图像的分类精度。该文主要研究了4种基于Wishart分布的分类算法，包括经典复Wishart分类算法、混合复Wishart模型、基于马尔科夫随机场（Markov Random Field, MRF）的混合复Wishart模型和基于局部竞争策略的MAP分类算法。在混合模型建模中，不同于以往的对整幅图像进行建模的模型策略，本文采用对单个类别进行混合建模的策略。实验对比分析了上述4个分类器和SVM分类器在C波段RADARSAT-2多时相的全极化SAR农田数据上的分类效果。实验结果表明，所提出的基于局部竞争策略的分类器对数据的分类结果稳定，具有最高的分类精度，基于混合Wishart的MRF模型分类结果次之。

Refined Polarimetric SAR Image Classification Based on Localized Competition

Polarimetric synthetic aperture radar (SAR) imaging technology has become one of the important means of high-resolution ground observation. Land cover classification for polarimetric SAR image has always been a hot research topic. The complex Wishart distribution-based maximum likelihood (ML) classifier is one of the most classic polarimetric SAR image classification algorithms. However, due to the complexity of feature types, regional heterogeneity, etc., the classification accuracy of the pixel-based Wishart classifier is not high. To solve this problem, this paper proposes a local maximum a posteriori (MAP) competition method based on the complex Wishart distribution, which can improve the classification accuracy for images with complex terrains by calculating pseudo-prior probabilities and then implementing MAP classifier in a local window of each pixel.This paper mainly studies 4 classification algorithms based on the Wishart distribution, including the classic complex Wishart classification algorithm, mixed complex Wishart model, Markov random field (MRF)-based mixed complex Wishart model and local competitive Wishart classification algorithm. For mixed model modeling, different from the previous modeling strategy that models the whole image, this paper adopts the strategy to model a single class with a mixture of Wishart distributions. The experiments were conducted to compare and analyze the classification results of the above-mentioned four classifiers and the SVM classifier on C-band RADARSAT-2 multi-temporal fully polarimetric SAR data collected over farmlands. Experimental results show that the proposed classifier based on local competition strategy has stable and superior classification performances for the multi-temporal data sets over the other four methods.