دورية أكاديمية
SSDT: Scale-Separation Semantic Decoupled Transformer for Semantic Segmentation of Remote Sensing Images
| العنوان: | SSDT: Scale-Separation Semantic Decoupled Transformer for Semantic Segmentation of Remote Sensing Images |
|---|---|
| المؤلفون: | Chengyu Zheng, Yanru Jiang, Xiaowei Lv, Jie Nie, Xinyue Liang, Zhiqiang Wei |
| المصدر: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 9037-9052 (2024) |
| بيانات النشر: | IEEE, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| مصطلحات موضوعية: | Geophysical image processing, geoscience and remote sensing, semantic segmentation, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| الوصف: | As we all know, semantic segmentation of remote sensing (RS) images is to classify the images pixel by pixel to realize the semantic decoupling of the images. Most traditional semantic decoupling methods only decouple and do not perform scale-separation operations, which leads to serious problems. In the semantic decoupling process, if the feature extractor is too large, it will ignore the small-scale targets; if the feature extractor is too small, it will lead to the separation of large-scale target objects and reduce the segmentation accuracy. To address this concern, we propose a scale-separated semantic decoupled transformer (SSDT), which first performs scale-separation in the semantic decoupling process and uses the obtained scale information-rich semantic features to guide the Transformer to extract features. The network consists of five modules, scale-separated patch extraction (SPE), semantic decoupled transformer (SDT), scale-separated feature extraction (SFE), semantic decoupling (SD), and multiview feature fusion decoder (MFFD). In particular, SPE turns the original image into a linear embedding sequence of three scales; SD divides pixels into different semantic clusters by K-means, and further obtains scale information-rich semantic features; SDT improves the intraclass compactness and interclass looseness by calculating the similarity between semantic features and image features, the core of which is decoupled attention. Finally, MFFD is proposed to fuse salient features from different perspectives to further enhance the feature representation. Our experiments on two large-scale fine-resolution RS image datasets (Vaihingen and Potsdam) demonstrate the effectiveness of the proposed SSDT strategy in RS image semantic segmentation tasks. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1939-1404 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/10495748/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3383066 |
| URL الوصول: | https://doaj.org/article/122538a3592249e8830f94f1b349a92f |
| رقم الأكسشن: | edsdoj.122538a3592249e8830f94f1b349a92f |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19391404 21511535 |
|---|---|
| DOI: | 10.1109/JSTARS.2024.3383066 |