基于图正则自适应联合协同表示的高光谱图像分类

针对由于空间信息利用不充分而导致的高光谱图像分类精度较低的问题,提出一种基于图正则自适应联合协同表示的高光谱图像分类算法.首先,采用双边滤波操作对高光谱图像进行空间信息提取,以充分挖掘每个像素的空间信息;其次,在联合协同表示的目标函数中引入图正则约束项,以保持高光谱数据的流形结构;再次,一方面利用图像分割来自适应调整空间邻域的形状,另一方面通过对中心像素的空间近邻赋予不同的权重,提出一种自适应空间-光谱特征融合策略;最后,基于误差最小原则,给出测试样本的类别标签.在两个高光谱数据集上的实验结果表明,所提出算法的整体分类精度分别达到98.50%和97.30%.

Graph regularized adaptive joint collaborative representation for hyperspectral image classification

In this paper, a graph regularized adaptive joint collaborative representation algorithm is proposed to overcome a low classification accuracy problem caused by insufficient utilization of spatial information for hyperspectral image classification. Firstly, the bilateral filter is adopted to extract spatial information for hyperspectral image to fully explore the spatial information of each pixel. Then, a graph regularized term is introduced into the objective function of joint collaborative representation to maintain the local manifold structure of hyperspectral image(HSI) data. On one hand, the image segmentation is used to adjust the shape of the spatial neighborhood; on the other hand, an adaptive space-spectral feature fusion strategy is proposed by assigning different weights to the spatial neighbors of the central pixels. Finally, the label of each testing sample is determined by utilizing the least errors criterion. Experimental results on Indian Pines and Pavia University datasets show that overall accuracies of the proposed algorithm can achieve 98.50% and 97.30% respectively.