ABSTRACT

In hyperspectral images, endmembers characterizing one class of ground object may vary due to illumination, weathering, slight variations of the materials. This phenomenon is called intra-class endmember variability which is one of the important factors affecting the performance of unmixing. However, intra-class endmember variability is often ignored in unmixing, which causes a decrease in the accuracy of unmixing. How to deal with intra-class endmember variability is the focus. To address this problem, we propose a novel hyperspectral unmixing method based on Least Squares Twin Support Vector Machines (ULSTWSVM). ULSTWSVM uses multiple training samples (endmembers) to model a pure class, which takes intra-class endmember variability into account in unmixing. At the same time, ULSTWSVM obtains abundances by calculating the distances from the mixed pixels to the classification hyperplanes, which is simple and efficient. ULSTWSVM mainly comprises three steps: (1) to obtain the two non-parallel classification hyperplanes by solving two quadratic programming problems (QPPs) in least squares sense, (2) to calculate distances from the mixed pixels to classification hyperplanes, and (3) to normalize the distances and convert them to abundances. Experimental results on both synthetic and real hyperspectral data show that the proposed method outperforms the methods used for comparison.

摘要

在高光谱图像中，表征一类地面物体的端构件可能会由于光照，风化和材料的细微变化而发生变化。这种现象称为类内端成员变异性，它是影响分解性能的重要因素之一。但是，类内端成员的可变性在拆解中通常被忽略，这导致拆解精度下降。焦点是如何处理组内端成员的变异性。为了解决这个问题，我们提出了一种基于最小二乘双支持向量机（ULSTWSVM）的高光谱分解方法。ULSTWSVM使用多个训练样本（端成员）来对纯类进行建模，在分解时将类内端成员的可变性考虑在内。同时，ULSTWSVM通过计算从混合像素到分类超平面的距离来获得丰度，这既简单又有效。ULSTWSVM主要包括三个步骤：（1）通过解决两个最小平方意义上的二次规划问题（QPP）来获得两个非平行分类超平面；（2）计算从混合像素到分类超平面的距离；以及（3）标准化距离并将其转换为丰度。在合成和真实高光谱数据上的实验结果表明，所提出的方法优于用于比较的方法。（1）通过解决两个最小平方意义上的二次规划问题（QPP）来获得两个非平行分类超平面；（2）计算从混合像素到分类超平面的距离；（3）归一化距离并转换他们丰富。在合成和真实高光谱数据上的实验结果表明，所提出的方法优于用于比较的方法。（1）通过解决两个最小平方意义上的二次规划问题（QPP）来获得两个非平行分类超平面；（2）计算从混合像素到分类超平面的距离；（3）归一化距离并转换他们丰富。在合成和真实高光谱数据上的实验结果表明，所提出的方法优于用于比较的方法。