A boundary guidance and cross-scale information interaction network for water body extraction from remote sensing images
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摘要: 准确的水体提取对水资源保护、城市规划等方面具有重要的意义。然而, 在遥感影像中, 由于地物众多、环境复杂且不同水体可能具有不同形态、尺度及光谱特征, 水体难免会与其他地物产生类内异质性及类间相似性。现有方法未充分探索边界线索以及未充分利用不同层之间的语义相关性及多尺度表达, 导致从遥感影像中准确提取水体仍然是一项挑战性任务。针对这些问题, 本文提出了一种边界引导与跨尺度信息交互网络(boundary guidance and cross-scale information interaction network, BGCIINet)用于遥感影像水体提取。首先, 本文首次结合Sobel算子提出了一个边界引导(boundary guidance, BG)模块, 该模块可以有效捕获低层次特征中的边界线索并高效嵌入解码器为其提供丰富的边界知识; 其次, 为了加强网络多尺度表达能力, 促进层与层之间的信息交流, 提出了一个跨尺度信息交互(cross-scale information interaction, CII)模块。在2个数据集上进行了广泛实验, 结果表明: 本文方法优于其他4种先进方法, 在面对挑战性的场景时具有更丰富的边界细节及完整度, 能够更好地应用于遥感影像水体提取并为后续研究提供方法借鉴。Abstract: Extracting accurate water body information holds great significance for water resources protection and urban planning. However, due to numerous surface features and complex environments, along with different morphologies, scales, and spectral characteristics of different water bodies, remote sensing images inevitably exhibit heterogeneity, spectral similarities, and inter-class similarities between water bodies and other surface features. Existing methods fail to fully exploit boundary cues, the semantic correlation between different layers, and multi-scale representations, rendering the accurate information extraction of water bodies from remote sensing images still challenging. This study proposed a boundary guidance and cross-scale information interaction network (BGCIINet) for information extraction of water bodies from remote sensing images. First, this study proposed a boundary guidance (BG) module for the first time by combing the Sobel operator. This module can be used to effectively capture boundary cues in low-level features and efficiently embed these cues into a decoder to produce rich boundary information. Second, a cross-scale information interaction (CII) module was introduced to enhance the multi-scale representation capability of the network and facilitate information exchange between layers. Extensive experiments on two datasets demonstrate that the proposed method outperforms four state-of-the-art methods, offering rich boundary details and completeness under challenging scenarios. Therefore, the proposed method is more effective in extracting water body information from remote sensing images. This study will provide a valuable reference of methods for future research.
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