Effective Image and Video Recognition Techniques in Environmental and Earth Monitoring Systems Using Remote-Sensed Intelligent Visual Analytics

Abstract

Earth and environmental monitoring are very crucial to identify changes in climatic conditions, destruction of an ecosystem and calamities. The increased access to high-resolution satellite, aerial, and UAV imagery requires sophisticated intelligent visual analytics that can be used to derive actionable information on the basis of massive streams of remote-sensed data. The current image and video recognition methods are not always capable of attaining reliable performances in the presence of multimodal data heterogeneity, environmental dynamics, and interference of noise in remote-sensing images. These issues restrict the precision and flexibility of traditional deep learning-based monitoring systems to real-life applications. In this paper, we have suggested the Enhanced Visual Intelligence for Adaptive Recognition Network (EVIAR-Net). This deep learning model is a hybrid one that uses Graph-Convolutional Vision Transformers (GCVT) and Adaptive Multi-Source Fusion (AMSF). EVIAR-Net is able to store spatial correlations along with temporal dependencies using the graph-based spatial reasoning and transformer-based temporal encoding. AMSF actively combines multispectral, hyperspectral and video modalities to provide resistance to illumination, motion, and atmospheric perturbations. Performance assessments of various Earth observation datasets indicate an improvement in recognition accuracy of 21 percent, inference speed of 30 percent, and generalisation to unknown environments are better than CNN, ViT, and LSTM-based models. The suggested EVIAR-Net concept exhibits a smart, adaptable, and energy-saving strategy towards the next-generation environmental monitoring and predictive analytics.