# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 1 Introduction
Such change detection algorithms based on deep learning yield a relatively good performance in terms of the detection accuracy. However, most can be categorized into front-end differential change detection using low-level features such as a difference image as a feature input of their networks, resulting in sensitivity to several deteriorated conditions caused by geometric/radiometric distortions, different viewing angles, and so on. This front-end differential change detection conducts an early feature extraction of two image inputs into a single-path network. In contrast, back-end differential detection methods by employing dual-path networks have been proposed for fusing higher-level features with a long short-term memory (LSTM) model [24] to avoid the use of low-level difference features such as a difference image. In addition, a Siamese convolutional network (SCN) [25; 26; 27] and dual-DCN (dDCN) [28] were also proposed to detect changed areas by measuring the similarity with high-level network features. These algorithms achieve a relatively good performance, although false negatives are still observed. To reduce false positives and false negatives in change detection, a fusion network incorporating low- and high-level feature spaces in neural networks was proposed in this paper. For low-level differential features, the difference image is fed into the front-end differential DCN (FD-DCN). For a high-level differential feature, a back-end differential dDCN (BD-dDCN) is employed. In addition, a two-stage decision algorithm is incorporated for post-processing to enhance the detection rate during the inference stage. The intersection and union operations are employed to validate the change map. First, an intersection operation is used to avoid false positives. The second-stage decision operates a union by considering the local information of the first decision. This stage is developed to validate and repair the change map from the first decision. In addition, this study introduces a new loss function that combines a contrastive loss and weighted binary cross entropy loss function to optimize high- and low-level differential features, respectively. In our experiment, we found that the proposed algorithm can yield a better performance than existing algorithms by achieving an average area under the curve (AUC) of 0.9709, a percentage correct classification (PCC) of 99%, and a Kappa of 75 for several test datasets. This work contributes three main key features as follows. (1) Unlike the mentioned existing works above, we propose a fusion network by combining a front- and back-end networks to perform the low- and high-level differential detection in one structure. (2) A combining loss function between contrastive loss and binary cross entropy loss is proposed to accomplish fusion of the proposed networks in training stage. (3) The two-stage decision as a post-processing is presented to validate and ensure the changes prediction at the inference stage to obtain better the final change map. This paper is organized into five sections. In Section 2, related studies are briefly described. Section 3 presents the proposed algorithm in detail. Section 4 describes and analyzes the experiment results. Finally, we provide some concluding remarks regarding this research.