CRLSTM-HEXNET: Hybrid Deep Learning Framework with Harris Hawk Optimization in Multi-Label Classification

Abstract

Deep learning has enabled significant advancements in the classification of remote sensing images; however, the task of classifying images in remote sensing remains a formidable challenge because of the high item diversity and complexity that result from spatial and temporal combination and connection. The problem of insufficient differentiation of feature representations generated by deep learning remains, which is mostly due to the similarity and variety of inter-class and intra-class images, respectively. This paper introduces a novel hexagonal network architecture called DenseNet-169, which is based on end-to-end convolutional methods (Bi-LSTM and RNN model) known as CRLSTM-Hexnet. The proposed model comprises three distinct components: (1) a module for extracting features, (2) a feature selection module utilizing the Harris Hawk optimization (HHO) algorithm, and (3) a sub-network based on LSTM and RNN, incorporating a class attention module learning layer. Positive quantitative and qualitative findings from experiments on the RSI-CB256 multi-label dataset confirm the efficacy of our model.