Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation
This paper presents an automated procedure for optimizing datasets used in land/water segmentation tasks with deep learning models. The proposed method employs the Normalized Difference Water Index (NDWI) with a variable threshold to automatically assess the quality of annotations associated with mu...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1793 |
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| author | Marco Scarpetta Luisa De Palma Attilio Di Nisio Maurizio Spadavecchia Paolo Affuso Nicola Giaquinto |
| author_facet | Marco Scarpetta Luisa De Palma Attilio Di Nisio Maurizio Spadavecchia Paolo Affuso Nicola Giaquinto |
| author_sort | Marco Scarpetta |
| collection | DOAJ |
| description | This paper presents an automated procedure for optimizing datasets used in land/water segmentation tasks with deep learning models. The proposed method employs the Normalized Difference Water Index (NDWI) with a variable threshold to automatically assess the quality of annotations associated with multispectral satellite images. By systematically identifying and excluding low-quality samples, the method enhances dataset quality and improves model performance. Experimental results on two different publicly available datasets—the SWED and SNOWED—demonstrate that deep learning models trained on optimized datasets outperform those trained on baseline datasets, achieving significant improvements in segmentation accuracy, with up to a 10% increase in mean intersection over union, despite a reduced dataset size. Therefore, the presented methodology is a promising scalable solution for improving the quality of datasets for environmental monitoring and other remote sensing applications. |
| format | Article |
| id | doaj-art-96cc1cfdfea745fdb03cedb4c9c9e76d |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-96cc1cfdfea745fdb03cedb4c9c9e76d2025-08-20T02:43:07ZengMDPI AGSensors1424-82202025-03-01256179310.3390/s25061793Optimizing Satellite Imagery Datasets for Enhanced Land/Water SegmentationMarco Scarpetta0Luisa De Palma1Attilio Di Nisio2Maurizio Spadavecchia3Paolo Affuso4Nicola Giaquinto5Department of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, ItalyDepartment of Electrical and Information Engineering, Polytechnic University of Bari, Via E. Orabona 4, 70125 Bari, ItalyThis paper presents an automated procedure for optimizing datasets used in land/water segmentation tasks with deep learning models. The proposed method employs the Normalized Difference Water Index (NDWI) with a variable threshold to automatically assess the quality of annotations associated with multispectral satellite images. By systematically identifying and excluding low-quality samples, the method enhances dataset quality and improves model performance. Experimental results on two different publicly available datasets—the SWED and SNOWED—demonstrate that deep learning models trained on optimized datasets outperform those trained on baseline datasets, achieving significant improvements in segmentation accuracy, with up to a 10% increase in mean intersection over union, despite a reduced dataset size. Therefore, the presented methodology is a promising scalable solution for improving the quality of datasets for environmental monitoring and other remote sensing applications.https://www.mdpi.com/1424-8220/25/6/1793dataset quality evaluationmetrology for AINDWIwater detectioncoastline monitoringremote sensing |
| spellingShingle | Marco Scarpetta Luisa De Palma Attilio Di Nisio Maurizio Spadavecchia Paolo Affuso Nicola Giaquinto Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation Sensors dataset quality evaluation metrology for AI NDWI water detection coastline monitoring remote sensing |
| title | Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation |
| title_full | Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation |
| title_fullStr | Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation |
| title_full_unstemmed | Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation |
| title_short | Optimizing Satellite Imagery Datasets for Enhanced Land/Water Segmentation |
| title_sort | optimizing satellite imagery datasets for enhanced land water segmentation |
| topic | dataset quality evaluation metrology for AI NDWI water detection coastline monitoring remote sensing |
| url | https://www.mdpi.com/1424-8220/25/6/1793 |
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