An Efficient Flood Detection Method With Satellite Images Based on Algorithm-Hardware Co-Design

• Published in: IEEE geoscience and remote sensing letters Vol. 21; pp. 1 - 5
• Main Authors: Pan, Dingwei, Wang, Zhihao, Wang, Xueqian, Li, Gang, Zeng, Shulin, Wang, Yu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Algorithm–hardware co-optimization; Artificial neural networks; Central processing units; Co-design; Computation; Computational efficiency; Computational modeling; Computing time; CPUs; Data integration; Detection algorithms; Edge computing; efficient computing; Field programmable gate arrays; flood detection; Floods; Graphics; Graphics processing units; Hardware; heterogeneous hardware; Image processing; Information processing; Modules; Neural networks; Satellite imagery; Satellite images; satellite remote sensing; Satellites
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2024.3472050

In this letter, we propose an efficient flood detection (EFD) method using multisource satellite images based on the algorithm-hardware co-design strategy. This method aims to improve flood detection efficiency in resource-constrained edge computing environments. First, a hybrid heterogeneous computing platform is designed to incorporate central processing units (CPUs), graphics processing units (GPUs), and field programmable gate arrays (FPGAs) hardware units to combine their individual advantages for efficient satellite image processing during the flood detection process. Second, the different flood detection algorithm modules (containing convolutional neural networks and information fusion operations) are designed and assigned to appropriate hardware units based on the characteristics of each algorithm module and the capabilities of each hardware, to reduce hardware computation waste during the operation of flood detection algorithms. Experimental results based on measured data from four flood events demonstrate that our proposed flood detection method achieves a significant improvement in computational efficiency without a noticeable loss in flood detection accuracy compared with existing state-of-the-art methods.