激光与光电子学进展, 2017, 54 (12): 122803, 网络出版: 2017-12-11
基于邻域相似度的联合稀疏表示的高光谱图像分类算法 下载: 755次
Hyperspectral Image Classification Algorithm Based on Joint Sparse Representation of Neighborhood Similarity
图像处理 高光谱图像 图像分类 联合稀疏表示 邻域相似度 image processing hyperspectral image image classification joint sparse representation neighborhood similarity
摘要
为了提高基于联合稀疏表示的高光谱图像的分类精度, 提出一种基于邻域相似度联合稀疏表示的分类算法。与传统的联合稀疏表示算法相比, 邻域内不同地物类别的像元对待测像元P的影响权重不同, 依据邻域内的像元与像元P的相似程度, 设定相似度阈值。通过联合稀疏表示与像元P相似度高的像元来确定像元P的类别, 然后进一步利用空间信息修正分类算法, 即关联邻近像元的类别, 平滑分类结果。实验结果表明, 基于邻域相似度的联合稀疏表示的分类算法精度更高, 结果更稳定。
Abstract
In order to improve classification accuracy of hyperspectral image based on the joint sparse representation, we propose a classification algorithm based on neighborhood similarity. Compared with conventional joint sparse representation algorithm, the weight of different feature categories pixels to pixel P to be test in neighborhood is different. Similarity threshold can be set based on the similarity of all pixels in neighborhood and pixel P. Category of pixel P can be obtained by joint sparse representation pixels which have high similarity with pixel P. And then the spatial information is used to modify classification algorithm, which associates with the categories of the neighboring pixels and gets smooth classification results. Experiment results demonstrate that the proposed algorithm has higher classification accuracy and more stable results.
李佳逊, 董安国, 沈亚栋, 张蓓. 基于邻域相似度的联合稀疏表示的高光谱图像分类算法[J]. 激光与光电子学进展, 2017, 54(12): 122803. Li Jiaxun, Dong Anguo, Shen Yadong, Zhang Bei. Hyperspectral Image Classification Algorithm Based on Joint Sparse Representation of Neighborhood Similarity[J]. Laser & Optoelectronics Progress, 2017, 54(12): 122803.