Saline patch identification using a semi-supervised knowledge integration deep learning framework based solely on UAV RGB imagery
文献类型：期刊论文
| 作者 | Li, Zhen2,3; Sun, Zhigang1,2,3; Yang, Ting2,3; Liu, Zhen1,2,3; Sun, Wentao1; Zhang, Yixuan2,3; Wang, Jundong2,3; He, Yajuan4,5; Yang, Zeqian4,5; Han, Wei4,5 |
| 刊名 | COMPUTERS AND ELECTRONICS IN AGRICULTURE
 |
| 出版日期 | 2026 |
| 卷号 | 240页码:111061 |
| 关键词 | Saline patch identification Knowledge integration Semi-supervised learning UAV imagery |
| ISSN号 | 0168-1699 |
| DOI | 10.1016/j.compag.2025.111061 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Soil saline patches, as a direct and early indicators of soil salinization degradation, pose a serious threat to agricultural productivity and land sustainability in coastal and arid areas. However, most existing studies focus on regional-scale classification using medium- and low-resolution imagery, lacking fine-grained, high-precision identification of soil salinity patches. In this study, we propose a semi-supervised knowledge integration (SSKI) framework for the accurate extraction of soil saline patches using unmanned aerial vehicle (UAV) RGB imagery. To address the time-consuming and labor-intensive problem of sample labeling, a dual-sample optimization strategy is designed. It simultaneously enhances the diversity of labeled samples via color and spatial augmentation, and selects reliable pseudo-labels for unlabeled samples through sample stability evaluation. Moreover, considering the unique characteristics of soil saline patches, a multimodal information learning network is proposed, which integrates multi-scale soil saline patch information, spectral-ecological features, and texture-semantic joint information, aiming to enhance boundary precision and inter-class discrimination. We validate the proposed method using a newly constructed UAV dataset (DongY) covering approximately 16 km2 of saline-affected farmland in Dongying, China in 2022 and 2024. Experimental results show that SSKI consistently outperforms state-of-the-art methods. In particular, the multi-year, UAV-based saline patch identification demonstrates the strong temporal generalization capability of the proposed method. Furthermore, by mapping the spatial distribution of soil saline patches, the study provides a basis for assessing the impact of salinization on crop growth and offers essential data support for the management and remediation of saline-alkali land. |
| URL标识 | 查看原文 |
| WOS关键词 | SOIL-SALINITY |
| WOS研究方向 | Agriculture ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001619328400004 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219784]  |
| 专题 | 禹城站农业生态系统研究中心_外文论文 |
| 通讯作者 | Sun, Zhigang |
| 作者单位 | 1.Shandong Dongying Inst Geog Sci, Dongying 257000, Peoples R China; 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 4.Minist Agr & Rural Affairs Peoples Republ China, Big Data Dev Ctr, Beijing, Peoples R China; 5.Minist Agr & Rural Affairs Peoples Republ China, Key Lab Cultivated Land Use, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Zhen,Sun, Zhigang,Yang, Ting,et al. Saline patch identification using a semi-supervised knowledge integration deep learning framework based solely on UAV RGB imagery[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2026,240:111061. |
| APA | Li, Zhen.,Sun, Zhigang.,Yang, Ting.,Liu, Zhen.,Sun, Wentao.,...&Han, Wei.(2026).Saline patch identification using a semi-supervised knowledge integration deep learning framework based solely on UAV RGB imagery.COMPUTERS AND ELECTRONICS IN AGRICULTURE,240,111061. |
| MLA | Li, Zhen,et al."Saline patch identification using a semi-supervised knowledge integration deep learning framework based solely on UAV RGB imagery".COMPUTERS AND ELECTRONICS IN AGRICULTURE 240(2026):111061. |
入库方式： OAI收割
来源：地理科学与资源研究所
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