Land Cover Classification of Fused Hyperspectral and Multispectral Image Using In-Ception-Resnetv2

Remote sensing data from various satellites and multimodal data processing techniques have grabbed the curiosity of geoscience communities. Accurate classification of land cover from remotely sensed satellite images remains a serious challenge. The proposed work develops an Inception-ResNet v2 to classify different land covers of the Konkan region of India. Quick Atmospheric Correction (QUAC), stacking, and merging techniques are used to pre-process multispectral images. SG smoothing and merging techniques are used to pre-process hyperspectral images. The pre-processed spectral information contains

multiple wavebands, resulting in a high-dimensional feature set. Therefore, Principal Component Analysis(PCA) with Fennec Fox Optimization(FFO)is applied to extract useful wavebands. Subsequently, the extracted bands are sent to the hybrid NCTCP-DTCWT data fusion algorithm. These fused images are then given to Inception-ResNet v2 for classification. Proposed deep learning model can detect different land covers such as forest region, vegetation land, water, built-up, and barren land in the given fused image. The model achieved 96% accuracy, 91% precision, and 98% specificity. Comparison of different metrics prove that our hybrid model outperformed several other machine learning models explored in earlier work. Further, the proposed model is tested with fused images and non-fused images. The study proves that fused images can generate accurate classes than non-fused images. This study examines whether optimized band selection

combined with multi-resolution hyperspectral–multispectral fusion within a unified deep learning framework can significantly improve land-cover classification in complex coastal environments.