Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications

Journal of Imaging

Peter Godfree¹  PeterW.T. Yuen²  Johathan Piper²  RubenMoya Torres²  Changfeng Yuan²  Chris McCullough² 

[1] Defence Science and Technology Laboratory (DSTL), Space and Sensing Systems Group, Porton Down, Salisbury, Wiltshire SP4 0JQ, UK;Department of Electronic Warfare, Cranfield University, Swindon SN6 8LA, UK;

关键词: band selection;    spatial spectral band selection;    hyperspectral imaging;    classification;    mutual information;    curse of dimensionality;   

DOI  :  10.3390/jimaging6090087

来源: DOAJ

【 摘 要 】

Despite the numerous band selection (BS) algorithms reported in the field, most if not all have exhibited maximal accuracy when more spectral bands are utilized for classification. This apparently disagrees with the theoretical model of the ‘curse of dimensionality’ phenomenon, without apparent explanations. If it were true, then BS would be deemed as an academic piece of research without real benefits to practical applications. This paper presents a spatial spectral mutual information (SSMI) BS scheme that utilizes a spatial feature extraction technique as a preprocessing step, followed by the clustering of the mutual information (MI) of spectral bands for enhancing the efficiency of the BS. Through the SSMI BS scheme, a sharp ’bell’-shaped accuracy-dimensionality characteristic that peaks at about 20 bands has been observed for the very first time. The performance of the proposed SSMI BS scheme has been validated through 6 hyperspectral imaging (HSI) datasets (Indian Pines, Botswana, Barrax, Pavia University, Salinas, and Kennedy Space Center (KSC)), and its classification accuracy is shown to be approximately 10% better than seven state-of-the-art BS schemes (Saliency, HyperBS, SLN, OCF, FDPC, ISSC, and Convolution Neural Network (CNN)). The present result confirms that the high efficiency of the BS scheme is essentially important to observe and validate the Hughes’ phenomenon in the analysis of HSI data. Experiments also show that the classification accuracy can be affected by as much as approximately 10% when a single ‘crucial’ band is included or missed out for classification.

【 授权许可】

Unknown