Water Adroit Forum

Publications => Publications from the member of forum => Topic started by: Subir Paul on June 25, 2018, 10:00:20 PM

Title: Spectral-spatial classification of hyperspectral data with mutual information based segmented stacke
Post by: Subir Paul on June 25, 2018, 10:00:20 PM
The work carried out by Subir Paul along with Prof. D Nagesh Kumar titled "Spectral-spatial classification of hyperspectral data with mutual information based segmented stacked autoencoder approach" was published in the ISPRS Journal of Photogrammetry and Remote Sensing.

Abstract: Hyperspectral (HS) data comprises of continuous spectral responses of hundreds of narrow spectral bands with very fine spectral resolution or bandwidth, which offer feature identification and classification with high accuracy. In the present study, Mutual Information (MI) based Segmented Stacked Autoencoder (S-SAE) approach for spectral-spatial classification of the HS data is proposed to reduce the complexity and computational time compared to Stacked Autoencoder (SAE) based feature extraction. A non-parametric dependency measure (MI) based spectral segmentation is proposed instead of linear and parametric dependency measure to take care of both linear and nonlinear inter-band dependency for spectral segmentation of the HS bands. Then morphological profiles are created corresponding to segmented spectral features to assimilate the spatial information in the spectral-spatial classification approach. Two non-parametric classifiers, Support Vector Machine (SVM) with Gaussian kernel and Random Forest (RF) are used for classification of the three most popularly used HS datasets. Results of the numerical experiments carried out in this study have shown that SVM with a Gaussian kernel is providing better results for the Pavia University and Botswana datasets whereas RF is performing better for Indian Pines dataset. The experiments performed with the proposed methodology provide encouraging results compared to numerous existing approaches.

https://www.sciencedirect.com/science/article/pii/S0924271618300303?via%3Dihub