Space Object Recognition Using Multi-modal Image Fusion and Stacking of CoAtNets

Produced merged thermal images with RGB and depth images by using the CoAtNet model or stacked Convolutional and Attention Network, so as to give an improvement in performance to preserve the robustness all the time in different thermal conditions of space. The proposed research then extends the conventional RGB-depth fusion with thermal data to realize the role of thermal characteristics in the identification of material properties and operational status of space objects. This will allow for the very comprehensive capture of spatial and thermal features that are very essential in the differentiation of various space objects. By adding synchronized thermal, RGB, json, and depth images into the substantially enlarged dataset, it has now paved the way for a new class of multimodal fusion algorithms that can exploit the unique information each modality harbors. This work proposes an efficient deep learning fusion model based on all types of images to obtain improved feature representations. We show substantial increases in the classification performance under various illumination and temperature conditions by adapting the CoAtNet architecture to deal with the complexities of fused multi-modal input that is pivotal for space situational awareness.