This study evaluates seven segmentation algorithms for cloud detection and segmentation using the Biome and SPARCS datasets. DeepLabV3+ and RS-Net consistently perform well, while U-Net and U-Net++ show relatively lower performance. Dataset compatibility significantly influences model performance.
The evaluation includes metrics such as AUC, Dice coefficient, IoU, and coverage similarity to assess algorithm performance. Results indicate the importance of selecting algorithms tailored to specific dataset characteristics for optimal performance in cloud segmentation tasks.
Future research directions could involve ensemble methods, novel architectures, hyperparameter optimization, and integration of temporal patterns to enhance segmentation accuracy in remote sensing applications.
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by Loddo Fabio,... klokken arxiv.org 03-04-2024
https://arxiv.org/pdf/2402.13918.pdfDypere Spørsmål