Optimized GPDCNN: A Novel Deep Learning Based Spatial-Spectral Feature Extraction Architecture for Hyperspectral Image Classification

Hyperspectral images are essential for remote sensing applications in environment, surveillance, and precision agriculture. In recent years, convolutional neural networks [CNNs] have demonstrated remarkable results in computer vision applications, such as hyperspectral image classification. This research proposes a novel Deep Learning [DL] based approach to hyperspectral image classification. Initially, spectral and spatial smoothing is performed using Savitzky-Golay and median filters, respectively, in the pre-processing stage. After that, a Multi-scale Residual Deep Convolutional Neural Network [MSRDCNN] and Stacked Bidirectional Long Short-Term Memory [SB-LSTM] are utilized to retrieve spatial and spectral attributes. Then, the Chaotic Dwarf Mongoose Optimization Algorithm [CDMOA] is used to select features. The Modified Prairie Dog Optimization [MPDO] algorithm is used to enhance the performance when a Global Pooling Dilated Convolutional Neural Network [GPDCNN] is employed for categorization. Comprehensive analyses show the proposed approach performs better than the state-of-the-art models.