Efficient classification of remote sensing images using DF-DNLSTM: a deep feature densenet bidirectional long short term memory model

Abstract

Scene classification in remote sensing is challenging due to high inter-class similarity and low intra-class similarity. Numerous techniques have been introduced, but accurately classifying scenes remains arduous. To address this challenge, To address this, we propose a hybrid framework, DF-DNLSTM, integrating DenseNet-121 for feature extraction and BiLSTM for sequential modeling, enhancing accuracy and contextual understanding. Second, a Conditional Generative Adversarial Network (CGAN) is employed for data augmentation, improving training data quantity and quality. Finally, the study introduces SwarmHawk, a hybrid optimization algorithm that combines particle swarm optimization (PSO) and Harris hawk optimization (HHO). SwarmHawk ensures the selection of informative features while concurrently eliminating duplicates and redundancies. It also reduces computational time to 4863 s. The proposed DF-DNLSTM model is rigorously assessed on three public datasets-UCM, AID, and NWPU. Results demonstrate its superior efficacy, achieving 99.87% accuracy on UCM, equivalent accuracy on NWPU, and sustaining 98.57% accuracy on AID. This study establishes DF-DNLSTM’s effectiveness, highlighting its potential contributions to advancing remote sensing scene classification.