A Robust Band Compression Technique for Hyperspectral Image Classification
Dimension reduction is the key step of hyperspectral image classification. Many techniques have been developed in the past years, but our classification experiments show that some of these techniques are not robust while others suffer from the accuracy and the effectiveness for the classification of hyperspec teal data. In this paper, a novel band compression algorithm is proposed based on the fusion of segmented principle component analysis (SPCA) and linear discriminant analysis (LDA) for dimension reduction. We first select the bands independently via SPCA and LDA. Theoretical analysis shows that the selected bands have little correlation, and therefore, an iterative algorithm is adopt to adaptively co-optimizing both the parameter of merging SPCA bands and LDA bands, and the classification accuracy. Our extensive experiments on two real hyperspectral datasets (AVIRIS 1992 Indian pine image and HYDICE image of Washington DC Mall), proves that the proposed technique is not only robust but offers more classification accuracy than many conventional dimension reduction techniques over several well known classifiers.
Hyperspectral data fusion image region classification remote sensing spectral band compression aviris
Qazi Samiul Haq Lixin Shi Linmi Tao Shiqiang Yang
Key Laboratory of Pervasive Computing,Ministry of Education Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China
国际会议
厦门
英文
196-200
2010-10-29（万方平台首次上网日期，不代表论文的发表时间）