A method for information extraction of buildings from remote sensing images based on hybrid attention mechanism and Deeplabv3+
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摘要: 在大量且复杂的遥感影像中提取建筑物信息是遥感智能应用的重要研究内容之一。针对复杂环境下的遥感影像建筑物提取不精准及小型建筑物易被忽略等问题, 文章提出了一种基于混合注意力机制和Deeplabv3+的遥感影像语义分割算法——SC-deep网络。该网络采用编码-解码结构, 利用主干残差注意力网络提取深层特征和浅层特征, 通过空洞空间金字塔池化模块和通道空间注意力模块聚合遥感影像的空间和通道信息权重, 有效利用了遥感影像建筑物的多尺度信息, 从而减少影像细节在训练中的损失。实验结果表明, 所提方法在Aerial imagery dataset数据集上的分割结果均优于其他主流分割网络, 能够有效识别并提取复杂建筑物边缘和小型建筑物, 表现出更优异的建筑物提取性能。Abstract: Extracting information about buildings from a large and complex set of remote sensing images has always been a hot research topic in the intelligent applications of remote sensing. To address issues such as inaccurate information extraction of buildings and the tendency to ignore small buildings within a complex environment in remote sensing images, this study proposed the SC-deep network-a semantic segmentation algorithm for remote sensing images based on a hybrid attention mechanism and Deeplabv3+. Utilizing an encoder-decoder structure, this network employs a backbone residual attention network to extract deep- and shallow-layer features. Meanwhile, this network aggregates the spatial and channel information weights in remote sensing images using a dilated space pyramid pool module and a channel-space attention module. These allow for effectively utilizing the multi-scale information of building structures in remote sensing images, thereby reducing the loss of image details during training. The experimental results indicate that the proposed method outperforms other mainstream segmentation networks on the Aerial imagery dataset. Overall, this method can effectively identify and extract the edges of complex buildings and small structures, exhibiting superior building extraction performance.
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