# Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
## 1 Introduction  
Change detection is a challenging task in remote sensing for identifying changed areas between two images acquired at different times from the same geographical area. It has been widely used in both civil and military fields such as agricultural monitoring, urban planning, environment monitoring, and reconnaissance. In general, change detection is performed in three steps. First, a preprocessing step is commonly used to conduct registration of two images and to correct geometric and radiometric distortions. In the second step, a feature map is extracted, for example, a difference image is computed in order to generate change features with the assumption that two images are not perfectly registered for all of the pixels. Lastly, a classification or clustering algorithm is driven in order to distinguish changed pixels and unchanged pixels based on statistical characteristics. For change detection, many manually designed features such as a difference image (DI) [1; 2; 3; 4; 5; 6; 7], local change vector [8], and texture vector [9; 10; 11] have been proposed. In further classification analysis, an unsupervised change detection was proposed based on fuzzy c-mean (FCM) clustering [12; 13]. The optimization algorithm based on Markov random field (MRF) and genetic algorithm was employed so as to optimize the FCM. On the other hand, a supervised learning algorithm was presented based on an active learning and MRF in order to detect change areas [14]. In addition, a support vector machine (SVM) has widely been used to perform binary classification based on texture information and change vector analysis [9; 15; 16; 17]. Since the classification process mainly depends on extracted features, the selection of handcrafted features for effective image representation is known to be crucial. In general, handcrafted features in change detection are sensitive due to geometric and radiometricdistortions, as well as imperfect registration. All of those mentioned classification algorithms would be reasonably good for training data sets. However, those algorithms are not able to incorporate accurate and reliable statistical characteristics for a huge amount of data sets, and thus would not yield good detection performance for new data sets. Recently, a deep convolution neural network (DCNN) was developed to produce a hierarchy feature-maps via learned filters, and it can automatically learn a complex feature space from a huge amount of image data. The DCNN can achieve superior performance compared to conventional classification algorithms with handcrafted features. Recently, several change detection methods using deep learning algorithms have been proposed [18; 19; 20]. A difference image is fed into the deep neural networks as input data [18]. In addition, the neighboring features on each pixel on the difference image are taken as inputs. The restricted Boltzmann machine (RBM) is used for pre-training and is then unrolled in order to create a deep neural network. On the other hand, the change detection is performed by combining a sparse autoencoder, convolutional neural network (CNN), and unsupervised clustering algorithm [19]. In addition, a log-ratio map was used and transformed by a sparse autoencoder into a suitable feature space. A change detection map is directly extracted from the two images using a pre-trained CNN [20]. A unique higher dimensional feature map is produced by the CNN through different convolutional layers. The change map is computed using pixel-wise Euclidean distance of hyper dimensional features. Another change detection algorithm has also been proposed that adopts a log-ratio difference [21]. It is used as a feature input for detecting changes between multi-temporal synthetic aperture radar (SAR) images. In addition, a deep neural network was developed by stacking RBMs to learn and recognize changed pixels and unchanged pixels. In addition, a combined algorithm with the deep belief networks (DBNs) and change analysis are presented to highlight changes [22].