BOISO: Weight optimized U-Net architecture for segmentation of hyperspectral image

Abstract

Recently, the Hyper Spectral Image (HSI) classification relies as a well-established study area in the topic related to Remote Sensing (RS). The classification of HSI is used in various applications such as military, agriculture, mineral mapping and so on. However, the existing techniques have underlying difficulties related to curse of dimensionalities and the lack of training data. To overcome these issues, this research using Butterfly Optimization Integrated Snake Optimization (BOISO) optimized U-Net for segmenting HSI. After data acquisition from Indian Pines dataset, the pre-processing is done using Weiner filter. Next to this, the proposed BOISO optimized U-Net is used to segment the pre-processed HSI. The spatial feature weight map is obtained based on spatial information path and corresponding features are obtained by multiplying semantic feature map. Finally, the feature map is linked with the spatial location to obtain final feature map which is optimized using the proposed BOISO. Then, the classification using hybrid classification approach is based on geometric mean of improved Deep Belief network (DBN) and Quantum Neural Network (QNN). The results exhibit that the BOISO achieves a sensitivity of 0.939 which is higher than SO, BOA, BES and PRO, with respective sensitivities of 0.877, 0.893, 0.847 and 0.857.