论文标题
课程风格的本地到全球适应性用于跨域遥感图像分段
Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation
论文作者
论文摘要
尽管在基于自然图像的分割任务中已经对域的适应性进行了广泛的研究，但有关非常高分辨率（VHR）遥感图像（RSIS）的跨域分割的研究仍然没有被驱动。基于VHR RSIS的跨域分割主要面临两个关键挑战：1）具有许多不同物体类别的大面积土地覆盖物带来了严重的局部斑块级数据分配偏差，从而给不同的本地贴片带来了不同的适应性困难； 2）不同的VHR传感器类型或动态变化的模式会导致VHR图像即使在相同的地理位置中也经历了密集的数据分布差异，从而导致不同的全局特征级级域间隙。为了应对这些挑战，我们提出了一个课程风格的局部到全球跨域适应框架，以用于分割VHR RSIS。所提出的课程式适应性根据适应困难进行适应过程，以适应困难的方式执行适应过程，该适应困难可以使用基于熵的目标域的熵分数获得，从而很好地使域图像中的局部贴片很好地对齐。提出的局部到全球适应性执行了从本地语义到全球结构特征差异的特征对齐过程，并由语义级别的域分类器和熵级别的域分类器组成，这些域分类器可以减少上述交叉域特征差异。在各种跨域场景中进行了广泛的实验，包括地理位置的位置变化和成像模式变化，实验结果表明，所提出的方法可以显着提高VHR RSIS分割网络的域适应性。我们的代码可在以下网址提供：https：//github.com/bobrown/ccda_lgfa。
Although domain adaptation has been extensively studied in natural image-based segmentation task, the research on cross-domain segmentation for very high resolution (VHR) remote sensing images (RSIs) still remains underexplored. The VHR RSIs-based cross-domain segmentation mainly faces two critical challenges: 1) Large area land covers with many diverse object categories bring severe local patch-level data distribution deviations, thus yielding different adaptation difficulties for different local patches; 2) Different VHR sensor types or dynamically changing modes cause the VHR images to go through intensive data distribution differences even for the same geographical location, resulting in different global feature-level domain gap. To address these challenges, we propose a curriculum-style local-to-global cross-domain adaptation framework for the segmentation of VHR RSIs. The proposed curriculum-style adaptation performs the adaptation process in an easy-to-hard way according to the adaptation difficulties that can be obtained using an entropy-based score for each patch of the target domain, and thus well aligns the local patches in a domain image. The proposed local-to-global adaptation performs the feature alignment process from the locally semantic to globally structural feature discrepancies, and consists of a semantic-level domain classifier and an entropy-level domain classifier that can reduce the above cross-domain feature discrepancies. Extensive experiments have been conducted in various cross-domain scenarios, including geographic location variations and imaging mode variations, and the experimental results demonstrate that the proposed method can significantly boost the domain adaptability of segmentation networks for VHR RSIs. Our code is available at: https://github.com/BOBrown/CCDA_LGFA.
