Change detection (CD), as one of the central problems in Earth observation, has attracted a lot of research interest over recent decades. Due to the rapid development of satellite sensors in recent years, we have witnessed an enrichment of the CD source data with the availability of very-high-resolution (VHR) multispectral imagery, which provides abundant change clues. However, precisely locating real changed areas still remains a challenge. In this article, we propose an end-to-end superpixel-enhanced CD network (ESCNet) for VHR images, which combines differentiable superpixel segmentation and a deep convolutional neural network (DCNN). Two weight-sharing superpixel sampling networks (SSNs) are tailored for the feature extraction and superpixel segmentation of bitemporal image pairs. A UNet-based Siamese neural network is then employed to mine the different information. The superpixels are then leveraged to reduce the latent noise in the pixel-level feature maps while preserving the edges, where a novel superpixelation module is used to serve this purpose. Furthermore, to compensate for the dependence on the number of superpixels, we propose an innovative adaptive superpixel merging (ASM) module, which has a concise form and is fully differentiable. A pixel-level refinement module making use of the multilevel decoded features is also appended to the end of the framework. Experiments on two public datasets confirmed the superiority of ESCNet compared to the traditional and state-of-the-art (SOTA) deep learning-based CD (DLCD) methods.

中文翻译：

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ESCNet：用于超高分辨率遥感图像的端到端超像素增强变化检测网络


变化检测（CD）作为地球观测的核心问题之一，近几十年来引起了广泛的研究兴趣。近年来，由于卫星传感器的快速发展，我们看到CD源数据不断丰富，超高分辨率（VHR）多光谱图像的出现，提供了丰富的变化线索。然而，精确定位真正发生变化的区域仍然是一个挑战。在本文中，我们提出了一种用于 VHR 图像的端到端超像素增强 CD 网络（ESCNet），它结合了可微分超像素分割和深度卷积神经网络（DCNN）。两个权重共享超像素采样网络（SSN）专为双时图像对的特征提取和超像素分割而定制。然后使用基于 UNet 的 Siamese 神经网络来挖掘不同的信息。然后利用超像素来减少像素级特征图中的潜在噪声，同时保留边缘，其中使用新颖的超像素化模块来实现此目的。此外，为了补偿对超像素数量的依赖，我们提出了一种创新的自适应超像素合并（ASM）模块，该模块具有简洁的形式并且是完全可微分的。利用多级解码特征的像素级细化模块也被附加到框架的末尾。对两个公共数据集的实验证实了 ESCNet 相对于传统和最先进的（SOTA）基于深度学习的 CD（DLCD）方法的优越性。