A Secure and Efficient Remote Sensing Image Classification Framework Using Chebyshev-SHA Encryption and Fox-Optimized Fast Recurrent Neural Networks

The exponential rise of remote sensing and sensor network technologies has generated massive amounts of visual data, posing challenges in safe transmission, data integrity, and categorization. In real-time applications, existing approaches fail to reconcile strong encryption, classification accuracy, and computing economy. This study aims to develop a safe and effective remote sensing image classification system that addresses both data security and intelligent analysis, enabling automated, context-aware insights in dispersed, real-time settings. The proposed work introduces the Fox-Optimized Secure Hybrid Image Encryption and Learning-based Detection (FOX-SHIELD) framework, which effectively integrates advanced encryption techniques with deep learning-based image classification, ensuring both data security and high classification accuracy for remote sensing images in real-time, distributed environments. An upgraded Chebyshev chaotic map and the Secure Hash Algorithm (SHA-256) provide dynamic, stable encryption keys in the first phase, ensuring data secrecy and integrity throughout transmission and storage. A Fast Recurrent Neural Network (FRNN) coupled with the Fox Optimization Algorithm improves convergence rate, stability, and classification accuracy even for encrypted input in the second phase. This integration enables powerful object detection while ensuring anonymity, an essential feature for sensitive remote sensing tasks. The FOX-SHIELD framework outperforms traditional models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and other hybrid encryption-learning models, in terms of classification accuracy, training convergence, and computational efficiency when applied to standard remote sensing datasets. This work addresses the fundamental issue of data security in remote sensing image classification by integrating lightweight cryptographic methods with metaheuristic deep learning optimization to enhance model accuracy, convergence, and computational efficiency in real-time applications.