Multi-Source Attention U-Net: A Novel Deep Learning Framework for the Land Use and Soil Salinization Classification of Keriya Oasis in China with RADARSAT-2 and Landsat-8 Data

Yang Xiang, Ilyas Nurmemet (), Xiaobo Lv, Xinru Yu, Aoxiang Gu, Aihepa Aihaiti and Shiqin Li
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Yang Xiang: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Ilyas Nurmemet: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Xiaobo Lv: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Xinru Yu: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Aoxiang Gu: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Aihepa Aihaiti: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China
Shiqin Li: College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China

Land, 2025, vol. 14, issue 3, 1-26

Abstract: Soil salinization significantly impacts global agricultural productivity, contributing to desertification and land degradation; thus, rapid regional monitoring of soil salinization is crucial for agricultural production and sustainable management. With advancements in artificial intelligence, the efficiency and precision of deep learning classification models applied to remote sensing imagery have been demonstrated. Given the limited feature learning capability of traditional machine learning, this study introduces an innovative deep fusion U-Net model called MSA-U-Net (Multi-Source Attention U-Net) incorporating a Convolutional Block Attention Module (CBAM) within the skip connections to improve feature extraction and fusion. A salinized soil classification dataset was developed by combining spectral indices obtained from Landsat-8 Operational Land Imager (OLI) data and polarimetric scattering features extracted from RADARSAT-2 data using polarization target decomposition. To select optimal features, the Boruta algorithm was employed to rank features, selecting the top eight features to construct a multispectral (MS) dataset, a synthetic aperture radar (SAR) dataset, and an MS + SAR dataset. Furthermore, Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbor (KNN), and deep learning methods including U-Net and MSA-U-Net were employed to identify the different degrees of salinized soil. The results indicated that the MS + SAR dataset outperformed the MS dataset, with the inclusion of the SAR band resulting in an Overall Accuracy (OA) increase of 1.94–7.77%. Moreover, the MS + SAR MSA-U-Net, in comparison to traditional machine learning methods and the baseline model, improved the OA and Kappa coefficient by 8.24% to 12.55% and 0.08 to 0.15, respectively. The results demonstrate that the MSA-U-Net outperformed traditional models, indicating the potential of integrating multi-source data with deep learning techniques for monitoring soil salinity.

Keywords: soil salinity; RADARSAT-2; classification; polarimetric decomposition; deep learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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