Climate Change Data Portal
| DOI | 10.3390/rs16010112 |
| Multi-Level Attention Interactive Network for Cloud and Snow Detection Segmentation | |
| 发表日期 | 2024 |
| EISSN | 2072-4292 |
| 起始页码 | 16 |
| 结束页码 | 1 |
| 卷号 | 16期号:1 |
| 英文摘要 | The ground is typically hidden by cloud and snow in satellite images, which have a similar visible spectrum and complex spatial distribution characteristics. The detection of cloud and snow is important for increasing image availability and studying climate change. To address the issues of the low classification accuracy and poor generalization effect by the traditional threshold method, as well as the problems of the misdetection of overlapping regions, rough segmentation results, and a loss of boundary details in existing algorithms, this paper designed a Multi-level Attention Interaction Network (MAINet). The MAINet uses a modified ResNet50 to extract features and introduces a Detail Feature Extraction module to extract multi-level information and reduce the loss of details. In the last down-sampling, the Deep Multi-head Information Enhancement module combines a CNN and a Transformer structure to make deep semantic features more distinct and reduce redundant information. Then, the Feature Interactive and Fusion Up-sampling module enhances the information extraction of deep and shallow information and, then, guides and aggregates each to make the learned semantic features more comprehensive, which can better recover remote sensing images and increase the prediction accuracy. The MAINet model we propose performed satisfactorily in handling cloud and snow detection and segmentation tasks in multiple scenarios. Experiments on related data sets also showed that the MAINet algorithm exhibited the best performance. |
| 英文关键词 | satellite image; cloud and snow detection; semantic segmentation |
| 语种 | 英语 |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| WOS记录号 | WOS:001140630300001 |
| 来源期刊 | REMOTE SENSING
 |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/297352 |
| 作者单位 | Nanjing University of Information Science & Technology; Nanjing Forestry University; Nanjing University of Information Science & Technology |
| 推荐引用方式 GB/T 7714 | . Multi-Level Attention Interactive Network for Cloud and Snow Detection Segmentation[J],2024,16(1). |
| APA | (2024).Multi-Level Attention Interactive Network for Cloud and Snow Detection Segmentation.REMOTE SENSING,16(1). |
| MLA | "Multi-Level Attention Interactive Network for Cloud and Snow Detection Segmentation".REMOTE SENSING 16.1(2024). |
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