ABSTRACT Change detection has been a research hotspot in remote sensing fields for decades. However, the increasing use of very high-resolution (VHR) remote sensing images have introduced more difficulties in change detection because of the complex details these images contain. In this paper, we propose a novel deep learning architecture for change detection composed of a Trisiamese subnetwork and a long short-term memory (LSTM) subnetwork that fully utilizes the spatial, spectral and multiphase information and improves the change detection capabilities for VHR remote sensing images. Multiscale simple linear iterative clustering (SLIC)-based image segmentation is first performed on multitemporal images at different image scales to obtain edge information-based objects. A Trisiamese subnetwork with six inputs can extract abundant spectral-spatial feature representations; the LSTM subnetwork then uses the extracted image features to effectively analyse the multiphase information in bitemporal images. The proposed method has the following advantages: 1) it can fully utilize the significant spatial information to improve the detection task; 2) it combines the advantages of convolutional architectures for image feature representation and recurrent neural network (RNN) architectures for sequential data representation, unlike most of the algorithms that use either method or that merely use image differencing or stacking operations. The controlled experiments reveal that the multiphase information extracted by the LSTM subnetwork is important to improve the accuracy of the change detection results. The influence of the Trisiamese subnetwork on change detection is even more significant than that of the LSTM subnetwork. Comparisons with other state-of-the-art change detection methods indicate that in areas with clear surface features and limited interference, the proposed method obtains more competitive results compared to state-of-the-art methods, and in regions where the changed objects occur in complex patterns, the proposed method exhibited an ideal performance.

中文翻译：

---

基于Trisiamese-LSTM的VHR遥感图像基于对象的变化检测

摘要 几十年来，变化检测一直是遥感领域的研究热点。然而，由于这些图像包含的复杂细节，越来越多地使用超高分辨率 (VHR) 遥感图像给变化检测带来了更多困难。在本文中，我们提出了一种新颖的变化检测深度学习架构，由 Trisiamese 子网和长短期记忆 (LSTM) 子网组成，充分利用空间、光谱和多相信息，提高了 VHR 遥感的变化检测能力图片。首先对不同图像尺度的多时相图像进行基于多尺度简单线性迭代聚类（SLIC）的图像分割，以获得基于边缘信息的对象。具有六个输入的 Trisiamese 子网络可以提取丰富的光谱空间特征表示；LSTM 子网络然后使用提取的图像特征来有效分析双时态图像中的多相信息。所提出的方法具有以下优点：1）可以充分利用显着的空间信息来改进检测任务；2) 它结合了用于图像特征表示的卷积架构和用于顺序数据表示的循环神经网络 (RNN) 架构的优点，这与大多数使用任一方法或仅使用图像差分或堆叠操作的算法不同。受控实验表明，LSTM 子网络提取的多相信息对于提高变化检测结果的准确性很重要。Trisiamese 子网对变化检测的影响甚至比 LSTM 子网更显着。与其他最先进的变化检测方法的比较表明，在表面特征清晰且干扰有限的区域，与最先进的方法相比，所提出的方法获得了更具竞争力的结果，并且在物体发生变化的区域出现在复杂的模式中，所提出的方法表现出理想的性能。