用于粤港澳大湾区多类潮汐湿地变化检测的时空-光谱-语义感知卷积变换器网络

IF 10.6 1区 地球科学 Q1 GEOGRAPHY, PHYSICAL
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引用次数: 0

摘要

沿海潮汐湿地对环境和经济健康至关重要,但也面临着各种环境变化的威胁。检测潮汐湿地的变化对于促进沿海地区的可持续发展至关重要。尽管对潮汐湿地变化进行了广泛的研究,但仍然存在持续的挑战。首先,潮汐湿地类型之间的高度相似性阻碍了现有通用指数的有效性。其次,目前的许多方法依赖于手工创建特征,既费时又受个人偏见的影响。第三,很少有研究能有效整合多时信息和语义信息,导致环境噪声和潮汐变化造成误读。针对上述问题,我们提出了一种新型的时间-光谱-语义感知卷积变换网络(TSSA-CTNet),用于多类潮汐湿地变化检测。首先,针对不同潮汐湿地之间的光谱相似性,我们提出了稀疏二阶特征构建(SSFC)模块,以构建更多可分离的光谱表示。其次,为了自动获取更多可分离的特征,我们构建了时空特征提取器(TSFE)和连体语义共享(SiamSS)模块来提取时空语义特征。第三,为了充分利用语义信息,我们提出了中心比较标签平滑(CCLS)模块来生成语义感知标签。利用 2000 年至 2019 年的 Landsat 数据在大湾区进行的实验表明,TSSA-CTNet 的总体准确率达到 89.20%,优于其他方法 3.75%-16.39%。研究显示,滩涂、红树林和沼泽的面积损失巨大,分别减少了3148公顷、35公顷和240公顷。在全球滩涂区城市中,珠海的面积损失最为严重,共减少了 1626 公顷。事实证明,TSSA-CTNet 能够有效地进行多类潮汐湿地变化检测,为潮汐湿地保护提供有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Temporal-spectral-semantic-aware convolutional transformer network for multi-class tidal wetland change detection in Greater Bay Area

Coastal tidal wetlands are crucial for environmental and economic health, but facing threats from various environmental changes. Detecting changes of tidal wetlands is essential for promoting sustainable development in coastal areas. Despite extensive researches on tidal wetland changes, persistent challenges still exist. Firstly, the high similarity among tidal wetland types hinders the effectiveness of existing common indices. Secondly, many current methods, relying on hand-crafted features, are time-consuming and subject to personal biases. Thirdly, few studies effectively integrate multi-temporal and semantic information, leading to misinterpretations from environmental noise and tidal variations. In view of the abovementioned issues, we proposed a novel temporal-spectral-semantic-aware convolutional transformer network (TSSA-CTNet) for multi-class tidal wetland change detection. Firstly, to address spectral similarity among different tidal wetlands, we proposed a sparse second order feature construction (SSFC) module to construct more separable spectral representations. Secondly, to get more separable features automatically, we constructed temporal-spatial feature extractor (TSFE) and siamese semantic sharing (SiamSS) blocks to extract temporal-spatial-semantic features. Thirdly, to fully utilize semantic information, we proposed a center comparative label smoothing (CCLS) module to generate semantic-aware labels. Experiments in the Greater Bay Area, using Landsat data from 2000 to 2019, demonstrated that TSSA-CTNet achieved 89.20% overall accuracy, outperforming other methods by 3.75%–16.39%. The study revealed significant area losses in tidal flats, mangroves, and tidal marshes, decreased by 3148 hectares, 35 hectares, and 240 hectares, respectively. Among the cities in GBA, Zhuhai shows the most significant area loss with a total of 1626 hectares. TSSA-CTNet proves effective for multi-class tidal wetland change detection, offering valuable insights for tidal wetland protection.

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来源期刊
ISPRS Journal of Photogrammetry and Remote Sensing
ISPRS Journal of Photogrammetry and Remote Sensing 工程技术-成像科学与照相技术
CiteScore
21.00
自引率
6.30%
发文量
273
审稿时长
40 days
期刊介绍: The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) serves as the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). It acts as a platform for scientists and professionals worldwide who are involved in various disciplines that utilize photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The journal aims to facilitate communication and dissemination of advancements in these disciplines, while also acting as a comprehensive source of reference and archive. P&RS endeavors to publish high-quality, peer-reviewed research papers that are preferably original and have not been published before. These papers can cover scientific/research, technological development, or application/practical aspects. Additionally, the journal welcomes papers that are based on presentations from ISPRS meetings, as long as they are considered significant contributions to the aforementioned fields. In particular, P&RS encourages the submission of papers that are of broad scientific interest, showcase innovative applications (especially in emerging fields), have an interdisciplinary focus, discuss topics that have received limited attention in P&RS or related journals, or explore new directions in scientific or professional realms. It is preferred that theoretical papers include practical applications, while papers focusing on systems and applications should include a theoretical background.
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