AUXG: Deep Feature Extraction and Classification of Remote Sensing Image Scene Using Attention Unet and XGBoost

Classification of remote sensing image scenes is an important and challenging task in understanding the Earth's surface and its changes. At the same time, classification is a complex task because of the high variability, multifaceted composition, and dimensionality present in data. The Attention U-Net was originally designed for image segmentation rather than classification. In the proposed framework Attention U-Net is modified, and modifications are made by incorporating a classification layer to AttentionUNet. The full connection layer of AttentionUNet is utilized as the base learner for XGBoost, to develop an efficient framework for remote sensing image classification. However, adapting AttentionUnet for classification presents several advantages such as harnessing attention mechanisms to emphasize pertinent image regions and potentially increasing classification accuracy. Additionally, it enables the exploitation of the U-Net's multi-scale contextual capabilities, aiding in the classification tasks. The proposed approach was evaluated on a dataset of high-resolution remote sensing images from the NWPU-RESISC45 and RSI-CB256 datasets. The results show that the proposed approach has outperformed other baseline models by achieving an overall accuracy of 92.67% in remote sensing image classification. The proposed architecture demonstrates the potential of combining Attention U-Net and XGBoost and highlights the importance of considering both the spatial and contextual information present in remote sensing images.