Change detection (CD) in remote sensing images aims at revealing Earth surface changes between co-registered bitemporal images. A common way to reveal changed areas is to directly mix bitemporal features and generate CD results through supervised learning. However, a certain change usually corresponds to a real object in either of the two images, which exhibits coarse/fine shapes in different scales. Therefore, a coarser-to-finer method called candidate-aware and change-guided network (CACG-Net) is proposed to effectively detect changes, where candidate objects are revealed and associated with interesting changes. Specifically, there are three key components. They are multistage change decoder (MCD), candidate-aware learning (CAL), and change guidance module (CGM). MCD reveals the most important changed objects in the coarse shape from the basic features extracted by the backbone (ResNet-18). To capture changes of interest, CAL is designed to select candidate objects in each temporal image, where a segmenter is utilized with variant change-losses. CGM intends to enrich the change details step-by-step by combining coarser change results and finer features so that changed objects are gradually revealed in a coarser-to-finer way. Furthermore, deep supervision is employed throughout the layers of CACG-Net in the training procedure, which mitigates the learning difficulty in both deep and shallow layers. Test results on four popular datasets indicate that the proposed method outperforms several state-of-the-art CD algorithms in terms of accuracy and efficiency.

中文翻译：

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用于遥感变化检测的候选人感知和变化引导学习


遥感图像中的变化检测（CD）旨在揭示共同配准的双时态图像之间的地球表面变化。揭示变化区域的一种常见方法是直接混合双时态特征并通过监督学习生成CD结果。然而，某种变化通常对应于两个图像中的任何一个中的真实物体，其在不同尺度上表现出粗/细形状。因此，提出了一种称为候选感知和变化引导网络（CACG-Net）的从粗到细的方法来有效地检测变化，其中候选对象被揭示并与有趣的变化相关联。具体来说，有三个关键组成部分。它们是多级变更解码器（MCD）、候选感知学习（CAL）和变更指导模块（CGM）。 MCD 从主干网（ResNet-18）提取的基本特征中揭示了粗略形状中最重要的变化对象。为了捕获感兴趣的变化，CAL 被设计为在每个时间图像中选择候选对象，其中分段器与变量变化损失一起使用。 CGM旨在通过将较粗的变化结果与较精细的特征相结合，逐步丰富变化细节，使变化的对象以由粗到细的方式逐渐显现出来。此外，在训练过程中，CACG-Net 的各个层都采用了深度监督，这减轻了深层和浅层的学习难度。对四个流行数据集的测试结果表明，所提出的方法在准确性和效率方面优于几种最先进的 CD 算法。