激光与光电子学进展, 2019, 56 (21): 212802, 网络出版: 2019-11-02
基于聚类降维和视觉注意机制的高光谱影像分类 下载: 876次
Hyperspectral Image Classification Based on Clustering Dimensionality Reduction and Visual Attention Mechanism
遥感 图像分类 聚类降维 视觉注意机制 多尺度显著性检测 remote sensing image classification clustering dimensionality reduction visual attention mechanism multi-scale saliency detection
摘要
将基于多尺度显著性检测的视觉注意机制引入到高光谱影像的噪声去除和图像增强处理中,并基于分层聚类算法,提出一种结合聚类降维和视觉注意机制的高光谱影像分类方法。以Indian数据集和Pavia数据集为例,开展降维、显著性映射图获取和支持向量机监督分类实验。结果表明,本文方法能够较大地提升高光谱影像的分类精度和效率。
Abstract
A multi-scale saliency detection-based visual attention mechanism is introduced to eliminate noise and enhance the quality of the hyperspectral images. Further, a hyperspectral image classification method is proposed by combining the clustering dimensionality reduction and visual attention mechanism in accordance with the hierarchical clustering algorithm. Subsequently, dimensionality reduction, acquisition of saliency mapping, and support-vector-machine-supervised classification experiments are conducted by considering the Indian and Pavia datasets as examples. The results denote that the proposed method can considerably improve the classification accuracy and efficiency of hyperspectral images.
曾朝平, 琚丽君, 张建辰. 基于聚类降维和视觉注意机制的高光谱影像分类[J]. 激光与光电子学进展, 2019, 56(21): 212802. Chaoping Zeng, Lijun Ju, Jianchen Zhang. Hyperspectral Image Classification Based on Clustering Dimensionality Reduction and Visual Attention Mechanism[J]. Laser & Optoelectronics Progress, 2019, 56(21): 212802.