Spatial-Spectral Sparse Optimized CNN for Hyper Spectral Image Classification.

Venkateshalu, Sneha G.; Deshpande, Santhosh Laxman

The proposed spatial-spectrum sparse aware CNN (SSCNN) approach with a CNN architecture employing sparse aware fusion/compression technique to reconstruct high-quality HSI images, departing from prior approaches. The developed model comprises three phases: creating the dictionary, including sparse coding into the dictionary, and evaluating the quality of the parametric reconstruction for the fusion of spatial data. From fused HSI colors, multiscale features are extracted in the second phase. For classification, spatial, and spectral optimization used, a soft margin decision boundary method to reduce the misclassification error. The regularisation factor C was added to manage the commutation among margin and misclassification sensitivity. SSCNN network enables the training of high-level semantic features of fused bands with the best multi-categorization accuracy of 99.46% and kappa of 99.37% in addition to localization-preserved information within the 143.97 secs elapsed time. The findings of the proposed are helpful in spatial data analysis development in the study area.

CABLE News Network; IMAGE recognition (Computer vision); HYPERSPECTRAL imaging systems; SPECTRAL imaging; FEATURE extraction; ENCYCLOPEDIAS & dictionaries

International Journal of Intelligent Engineering & Systems, 2023, Vol 16, Issue 6, p896

2185-310X

Academic Journal

10.22266/ijies2023.1231.74