Application of Remote Sensing Image Processing for Classification and Recognition

Aiming at the difficulties in object detection and recognition in remote sensing images caused by high background complexity, large scale variations of targets, and the presence of numerous small objects, an improved method for remote sensing image object detection based on YOLOv7-tiny is proposed. This method combines the loss function based on normalized Gaussian Wasserstein distance (NWD) with the CIoU loss function to address the problem of sensitivity to positional deviation of small objects by IoU-Loss. The addition of a global attention mechanism (GAM) in the backbone network reduces information diffusion and enhances the interaction at the global dimension to mitigate the interference of complex backgrounds in remote sensing images on the model, enabling the model to focus on the feature extraction of the desired targets. Finally, the coupled detection head (Coupled Head) of the model is replaced with a decoupled detection head (Decoupled Head), allowing the classification and regression tasks to output from different branches to achieve decoupling and avoid the decrease in detection accuracy caused by conflicts between classification and regression. The experimental results of this method on the public dataset DIOR achieved 88.73% accuracy, which is an improvement of 1.78% compared to the unimproved method's accuracy of 86.95%. Furthermore, compared to other researchers' methods tested on DIOR, the proposed method also shows improvement, thus validating its effectiveness.