High-resolution remote sensing (HRS) semantic segmentation extracts key
objects from high-resolution coverage areas. However, objects of the same
category within HRS images generally show significant differences in scale and
shape across diverse geographical environments, making it difficult to fit the
data distribution. Additionally, a complex background environment causes
similar appearances of objects of different categories, which precipitates a
substantial number of objects into misclassification as background. These
issues make existing learning algorithms sub-optimal. In this work, we solve
the above-mentioned problems by proposing a High-resolution remote sensing
network (Hi-ResNet) with efficient network structure designs, which consists of
a funnel module, a multi-branch module with stacks of information aggregation
(IA) blocks, and a feature refinement module, sequentially, and Class-agnostic
Edge Aware (CEA) loss. Specifically, we propose a funnel module to downsample,
which reduces the computational cost, and extract high-resolution semantic
information from the initial input image. Secondly, we downsample the processed
feature images into multi-resolution branches incrementally to capture image
features at different scales and apply IA blocks, which capture key latent
information by leveraging attention mechanisms, for effective feature
aggregation, distinguishing image features of the same class with variant
scales and shapes. Finally, our feature refinement module integrate the CEA
loss function, which disambiguates inter-class objects with similar shapes and
increases the data distribution distance for correct predictions. With
effective pre-training strategies, we demonstrated the superiority of Hi-ResNet
over state-of-the-art methods on three HRS segmentation benchmarks.

本研究提出了一个高分辨率遥感图像网络模型，可解决同类别地物在不同地理环境下形态和比例显著不同，以及复杂背景噪声干扰下影响现有学习算法效果的问题。该模型包含漏斗模型、信息聚合模块、特征精炼模块和 CEA Loss 损失函数，并通过有效的预处理策略，在三个高分辨率遥感图像分割基准数据集上取得了优异表现。