Band Dual Density Discrimination Analysis for Hyperspectral Image Classification

Qv, Hui; Yin, Jingwei; Luo, Xiaoyan; Jia, Xiuping

doi:10.1109/tgrs.2018.2849881

Cited by 12 publications

(2 citation statements)

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“…Thus, the entropy can be used for encoding the information of every band in the dataset by using Equation (4) [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. An alternative methodology has been using joint entropy between two variables A and B [ 17 , 82 , 83 , 84 , 85 ]. From Equation (3), the joint information of variables A and B is given by the mutual information (MI) I(A,B), which is in the form of:

where p(A) and p(B) are the marginal probability distributions of variables A and B, respectively, and p(A, B) is the joint probability distribution of variables A and B.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, the entropy can be used for encoding the information of every band in the dataset by using Equation (4) [21][22][23][24][25][26][27][28][29]. An alternative methodology has been using joint entropy between two variables A and B [17,[82][83][84][85]. From Equation (3), the joint information of variables A and B is given by the mutual information (MI) I(A,B), which is in the form of:…”

Section: Spectral Band Selections Using Mutual Information (Mi)mentioning

confidence: 99%

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Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications

et al. 2020

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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.

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where p(A) and p(B) are the marginal probability distributions of variables A and B, respectively, and p(A, B) is the joint probability distribution of variables A and B.…”

Section: Methodsmentioning

confidence: 99%