Satellite image segmentation using Unet++ andMobileNetV2 deep learning model

Abstract Satellite image segmentation plays a pivotal role in extracting valuable information for applications like ,disaster management, environmental monitoring and land cover classification. In this work, a comprehensive approach for satellite image segmentation utilizing a fusion of UNet++ architecture and the lightweight MobileNetv2 encoder deep learning model is proposed. The UNet++ architecture, an extension of the widely adopted UNet, is employed for its ability to capture hierarchical features and enhance segmentation performance. Integrating MobileNet as the encoder provides computational efficiency, making the model well-suited for resource-constrained environments, such as satellite image analysis on edge devices. The proposed model leverages the strengths of both architectures, combining the expressive power of UNet++ with the efficiency of MobileNet. Extensive experiments are conducted on a diverse satellite image dataset, evaluating the model's segmentation accuracy, computational efficiency, and generalization capability. The results demonstrate the effectiveness of the proposed approach in achieving accurate and efficient satellite image segmentation, making it a promising solution for real-world applications in remote sensing and geospatial analysis.