# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 2 Deep Convolutional Network and Related Studies on Change Detection  
Deep neural architectures with hundreds of hidden layers have been developed to learn high-level feature spaces. The recently developed convolutional neural network (CNN) is a deep learning architecture that has been shown to be effective in image recognition and classification [29]. The CNN architecture employs multiple convolutional layers, followed by an activation function, resulting in the development of feature maps. The rectified linear unit (ReLU) is widely used as the activation function in many CNN architectures. To progressively gather global spatial information, the feature maps are sub-sampled by the pooling layer. The final feature maps are connected to a fully connected layer to produce the class probability outputs \((P_{class})\), as shown in Figure 1. During the training stage, an objective loss such as cross-entropy is computed. All of the weighting parameters of the network are updated to reduce the cost function using the back-propagation algorithm. The related studies on change detection based on deep learning can be categorized into two categories based on the type of network that is used: A front-end differential network (FDN) and a back-end differential network (BDN). The front-end network uses low-level differential features such as a DI or joint feature (JF) as the feature input of the network, as shown in Figure 2a. In this case, a network with a single-path architecture receives the extracted DI as low-level differential features of the temporal images to identify changed pixels. Several studies based on an FDN have been proposed to improve the performance of the change detection rate. In addition, a deep neural network (DNN) is applied to detect objects from synthetic aperture radar (SAR) data [30]. The differential feature of temporal data is employed instead of a DI. This feature is used to solve the initial weight problem through pre-training using the restricted Boltzmann machine (RBM) algorithm. These pre-trained weights are then fed into the initial weights of the DNN during the training stage. In contrast, unsupervised change detection has been proposed by combining DBNs with a feature change analysis [23]. The feature maps of temporal input images are obtained using the DBN. The magnitude and direction of these feature maps are analyzed to distinguish the types of feature changes using an unsupervised fuzzy C-means algorithm. Other unsupervised systems have been proposed by combining a sparse autoencoder (SAE), unsupervised clustering, and a CNN to overcome the change detection problem without supervision [20]. First, a DI is computed using a log-ratio operator. The feature maps of the DI are extracted through the SAE and clustered into  
Figure 1: Convolutional neural network (CNN) architecture. change classes as the labels for the training CNN. Next, some feature maps extracted by the SAE are taken as the training data for the CNN. In addition, an autoencoder and multi-layer perceptron (MLP) are combined to identify changed pixels [31]. Change detection using faster R-CNN has been proposed for high-resolution images [32]. This work detects changed areas with bounding boxes. The DI is extracted and then fed into faster R-CNN to detect changed locations. Each of these deep learning algorithms tackles the change detection problem using a front-end differential network. This network identifies changes by observing low-level feature such as the DI, which is sensitive to various distortions, including geometric and radiometric distortions, and different viewing angles. Another approach of FDN to detect the changes has been proposed by joining feature inputs (JF) [23]. Two temporal images are concatenated and they are fed into DBN to avoid a DI for change detection. However, by joining the features in the early network causes both low-level differential inputs to be dependently learned in the single network. It is for global change detection, resulting in more false positives.