# Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
## 1 Introduction
The presented algorithm merges and vectorizes local pixel features into DBN inputs. Then, the DBN model is established in order to capture key information for discrimination and to suppress irrelevant variations. An unsupervised clustering algorithm is then used to classify changed and unchanged pixels. Another approach utilizes joint features for change detection [23]. This work proposed an efficient change rule with a reliable expression of difference information. It learns the reliable change rule by recording the change information for a long-term sequence of remote sensing data with long short-term memory (LSTM) model. As mentioned above, all of the deep learning-based change detection algorithms yield relatively good performance. However, most of them still rely on the difference image as a feature input of their networks, resulting in them being sensitive to noisy conditions caused by geometric, radiometric distortions, and different viewing angles. In order to solve these problems, an alternative approach for change detection was developed by measuring similarity. A Siamese convolutional network was proposed to detect changed areas for optical aerial images [24]. The Siamese convolutional network with shared weights learns to extract features directly from image pairs. This work uses shared weights that are dependent from those of two branch networks. The shared weights can reduce parameters to be optimized, resulting in faster convergence. However, this model is also less flexible, which leads to overfitting due to shared weights with some other neurons. In order to overcome the problems described above, this paper proposes a dual-dense convolutional network for recognizing pixel-wise change based on dissimilarity analysis of neighborhood pixels on high resolution panchromatic (PAN) images. In this proposed algorithm, two fully convolutional neural networks are employed to measure dissimilarity of neighborhood pixels. Furthermore, dense connection in convolution layers is applied to reuse preceding feature maps by connecting them to all subsequent layers. It is proposed to enhance a feature-map representation. While the conventional change detection algorithm [24] and conventional Siamese network use shared weights, the proposed algorithm removes shared weights in order to obtain independent optimal weights for two points of input data. So, each network can independently learn for optimal weights, called the \"dual-dense convolutional network (dual-DCN)\". During its training, the dual-DCN is driven to learn more robust different representations to better distinguish different types of changes. The proposed algorithm gives better performance compared to conventional methods in qualitative and quantitative evaluation. It yields AUC of 0.97, PCC of 99%, and Kappa of 69 on average. The rest of this paper is organized into five sections: In Section 2, the conventional convolutional neural network and problem statements will be described. Section 3 presents the proposed algorithm in detail. Section 4 will present and analyze experiment results. Finally, we conclude it in the last section.