SBSS：面向超高分辨率遥感影像的基于Stacking的语义分割框架
SBSS: Stacking-Based Semantic Segmentation Framework for Very High Resolution Remote Sensing Image
AI总结：
针对超高分辨率遥感影像中物体尺度差异大导致的分割难题，本文提出基于Stacking的SBSS框架，通过学习不同类别物体的偏好重缩放尺度优化分割，兼具有效性与灵活性。
摘要：
超高分辨率（VHR）遥感影像的语义分割是众多应用中的一项基础任务。然而，这类VHR影像中物体尺度的巨大差异给实现精准语义分割带来了挑战。现有语义分割网络最多可在4种重缩放尺度下分析输入图像，但考虑到物体尺度的多样性，这一数量可能仍显不足。因此，实践中常采用多尺度（MS）测试时数据增强来获取更准确的分割结果，该方法会平等利用不同重缩放尺度下得到的分割结果。但本研究发现，不同类别的物体为实现更精准的语义分割，各有其偏好的重缩放尺度。基于这一特性，本文提出了基于Stacking的语义分割（SBSS）框架，通过学习该特性来提升分割效果。该框架包含一个用于分割结果融合的可学习纠错模块（ECM），以及一个用于控制计算复杂度的纠错方案（ECS）。本研究提出并探究了两种ECS，即ECS-MS和ECS-SS。ECS-MS和ECS-SS所需的浮点运算量（Flops）分别与常用的多尺度测试和单尺度（SS）测试相近。在Cityscapes、UAVid、LoveDA和Potsdam四个数据集上开展的大量实验表明，SBSS是一个有效且灵活的框架：采用ECS-MS时，其准确率高于多尺度方法；采用ECS-SS时，其准确率与单尺度方法相近，但内存占用仅为后者的四分之一。
英文摘要：
Semantic segmentation of Very High Resolution (VHR) remote sensing images is a fundamental task for many applications. However, large variations in the scales of objects in those VHR images pose a challenge for performing accurate semantic segmentation. Existing semantic segmentation networks are able to analyse an input image at up to four resizing scales, but this may be insufficient given the diversity of object scales. Therefore, Multi Scale (MS) test-time data augmentation is often used in practice to obtain more accurate segmentation results, which makes equal use of the segmentation results obtained at the different resizing scales. However, it was found in this study that different classes of objects had their preferred resizing scale for more accurate semantic segmentation. Based on this behaviour, a Stacking-Based Semantic Segmentation (SBSS) framework is proposed to improve the segmentation results by learning this behaviour, which contains a learnable Error Correction Module (ECM) for segmentation result fusion and an Error Correction Scheme (ECS) for computational complexity control. Two ECS, i.e., ECS-MS and ECS-SS, are proposed and investigated in this study. The Floating-point operations (Flops) required for ECS-MS and ECS-SS are similar to the commonly used MS test and the Single-Scale (SS) test, respectively. Extensive experiments on four datasets (i.e., Cityscapes, UAVid, LoveDA and Potsdam) show that SBSS is an effective and flexible framework. It achieved higher accuracy than MS when using ECS-MS, and similar accuracy as SS with a quarter of the memory footprint when using ECS-SS.
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