## 1 Introduction
Their method exploits multi-scale features to enhance the power of the feature representation. Recently, an attention U-Net was proposed to enhance the detection performance of U-Net by finding target attention with a small number of additional weight parameters and minimal complexity [24]. This network employs attention gates to suppress node responses at irrelevant background regions and boost them in foreground regions by enhancing the responses through skip connections. Thus, the attention U-Net is more reliable than the conventional U-Net for change detection. However, those architectures involve only a single path encoding that extracts the features of input images based on the DI scheme. By using DI as the input of the U-Net, the same detection result can be obtained regardless of the input image order. It can detect both land cover changes and its inverse changes without any additional training dataset for its inverse order, for instance, construction and destruction changes. However, this leads to spectral information loss of the surface characteristic due to absolute image subtraction. By contrast, a joint feature (JF) can be extracted with its image stacking. The JFs on the U-Net architecture can combine multi-temporal input images by extending the input data dimensionality without any spectral information loss. However, change detection methods using JF tend to be sensitive and dependent on the order at the inference stage; hence, such methods do not work for the reversed order input case. More specifically, because they are order-dependent, they could generate different results in the reverse order of the images, as shown in Figure 1. Owing to the problems mentioned above, order-independency and spectral information preservation are important issues in change detection. This study proposes an order-independent bilateral change detection method based on a proposed dissimilarity attention gate (DAG) for attention U-Net that preserves spectral information. This bilateral detection approach avoids spectral information loss by the proposed DAG in the dual-encoding path to facilitate two input images. Feature maps are extracted from both images without any spectral information loss. The proposed algorithm also incorporates a DAG to overcome the order-dependency problem by exploiting two combinations of two joint features and difference features. The DAG boosts the channel feature responses for the most informative channels through channel attention. Overall, the proposed architecture yielded better detection rates than several conventional algorithmsbased on \(F_{1}\)-score and kappa coefficients. The proposed algorithm also resolves the order-dependency problem, resulting in the same detection results even when the order of any pair of input images is reversed. This work makes the following two key contributions. (1) Unlike conventional U-Net, which consists of a single-path encoder, the proposed architecture facilitates two inputs to preserve each part of spectral information. (2) DAG is introduced to overcome the order-dependency problem by exploiting two combinations of two joint features and difference features. The remainder of this paper is organized as follows. Section 2 presents related studies on change detection. Section 3 details the proposed change detection architecture. Section 4 outlines the experimental evaluations conducted and analyzes the results obtained compared with conventional algorithms. Section 5 discusses the results obtained vis-a-vis the research objectives. Finally, Section 6 presents concluding remarks.