论文标题
EHSNET：用于大尺寸遥感图像语义分段的端到端整体学习网络
EHSNet: End-to-End Holistic Learning Network for Large-Size Remote Sensing Image Semantic Segmentation
论文作者
论文摘要
本文介绍了EHSNET，这是一个新的端到端细分网络，旨在用于大尺寸遥感图像语义分段（LRISS）的整体学习。大尺寸的遥感图像（LRIS）可能会导致GPU记忆力耗尽，因为它们的尺寸极大，这在以前的作品中通过全球 - 位置融合或多阶段的细化进行了处理，这两者在完全利用LRIS中可用的丰富信息的能力方面都受到限制。与他们不同，EHSNET具有三个内存友好的模块来利用LRI的特征：一个远程依赖模块来开发远程空间上下文，一个有效的互相关模块来建立整体上下文关系，并建立一个边界意识到的增强模块来维护完整的对象边界。此外，EHSNET设法借助内存卸载来处理整体LRIS。据我们所知，EHSNET是第一种能够执行整体Lriss的方法。为了使事情变得更好，EHSNET的表现优于先前的最先进的竞争对手，而FBP上的+5.65 miOU的幅度为+5.65 miou，而+4.28 MIOU在Inria天气方面表现出了有效性。我们希望EHSNET将为Lriss提供新的观点。代码和模型将公开可用。
This paper presents EHSNet, a new end-to-end segmentation network designed for the holistic learning of large-size remote sensing image semantic segmentation (LRISS). Large-size remote sensing images (LRIs) can lead to GPU memory exhaustion due to their extremely large size, which has been handled in previous works through either global-local fusion or multi-stage refinement, both of which are limited in their ability to fully exploit the abundant information available in LRIs. Unlike them, EHSNet features three memory-friendly modules to utilize the characteristics of LRIs: a long-range dependency module to develop long-range spatial context, an efficient cross-correlation module to build holistic contextual relationships, and a boundary-aware enhancement module to preserve complete object boundaries. Moreover, EHSNet manages to process holistic LRISS with the aid of memory offloading. To the best of our knowledge, EHSNet is the first method capable of performing holistic LRISS. To make matters better, EHSNet outperforms previous state-of-the-art competitors by a significant margin of +5.65 mIoU on FBP and +4.28 mIoU on Inria Aerial, demonstrating its effectiveness. We hope that EHSNet will provide a new perspective for LRISS. The code and models will be made publicly available.
