CAMFNet: A Context-Aware Multiscale Fusion Network for Enhanced Multispectral Image Fusion in Remote Sensing and Security

This research is mainly concerned with enhancing the object recognition and classification of multispectral imagery imagery especially of people and vehicles in the heavily vegetated regions. Sub-technology such as PCA and WT have inherent limit with regards to the ability to preserve both spatial and spectral properties. To fill the above-mentioned gaps, this work presents a CAMFNet; a framework that integrates multiscale fusion together with Visible (VIS), Near-Infrared (NIR) and Thermal Infrared (TIR) data for fusion and feature extraction and classification. CAMFNet uses classifiers like Gradient Tree Boost and SVM for differentiating people, cars, trees and other such objects. Feature extraction is carried out using PCA and HOG for enhancing the methods prior to the extraction. One of the radical innovations is the employment of certain aspects in CAMFNet, including multiscale feature extraction and attention mechanisms for boosted data fusion. The framework also involves generative aerial networks (GANs) and it is capable of preserving important spatial and spatial details profiles thereby making it to be more accurate, to more recalls and has low false positives compared to normal methods. Experiments made on real-world datasets coming from different landscapes, revealed that the CAMFNet outperformed the other networks in the classification metrics, as well as in PSNR and SSIM. Surveillance, agriculture, and the military are some of its uses to emphasize work in large-scale and distant investigations. It pushes the state-of-art in multispectral image fusion with this strong, scalable, real-time method for nonmilitary and military domains for large-scale objects recognition.