# Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
## 1 Introduction  
Change detection is a challenging task in remote sensing, used to identify areas of change between two images acquired at different times for the same geographical area. Such detection has been widely used in both civilian and military fields, including agricultural monitoring, urban planning, environment monitoring, and reconnaissance. In general, change detection involves a preprocessing step, feature extraction, and classification or clustering algorithm to distinguish changed and unchanged pixels. To obtain a good performance, the selected classification or clustering algorithm plays an important role in the field of change detection. In prior studies, statistical approaches have been proposed to identify a change [1; 2; 3]. A corresponding maximal invariant statistic is obtained by analyzing a suitable group of transformations leaving problem invariant [2]. Then, a general problem of testing equality among \(M\) covariance metrices in the complex-valued Gaussian case is analyzed for synthetic aperture radar (SAR) change detection. A sample coherence magnitude as a change metric has been proposed by [3]. A new maximum-likelihood temporal change estimation and complex reflectance change detection is used for SAR coherent temporal change detection. Currently, a classification or clustering is becoming one approach to be used for change detection in remote sensed images by employing supervised or unsupervised learning, respectively. Feature selection and feature extraction are important aspects in this approach. Several detection algorithms using two images have been proposed with different features for different types of applications [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19]. The methods used for change detection have mostly been designed to extract changed features such as in a difference image (DI) [3; 4; 5; 6; 7; 8; 9], a local change vector [10], or a texture vector [11; 12; 13]. A DI is a common feature usedto represent a change in information through the subtraction of temporal images. Local change vectors have also been used by applying neighbor pixels to avoid a direct subtraction based on the log ratio. This method computes a mean value of the log ratio of temporal neighbor pixels. A texture vector [11; 12; 13] is employed to extract statistical characteristics. These changed features are then fed into a classification or clustering algorithm to determine changed/unchanged pixels. Some unsupervised change detection methods have been proposed based on the fuzzy c-mean (FCM) algorithm [14; 16]. Such approaches are useful when labels in the training stages are unavailable. The learning algorithms in the aforementioned studies are based on observed data without any additional information, therefore, their application leads to overfitting for invariant changes. Furthermore, they do not yield a reasonably good performance in the change detection rates because they do not incorporate accurate information without supervision. Therefore, supervised change detection methods, such as a support vector machine (SVM) [11; 16; 17; 18], have been proposed. The basic SVM can apply a binary classification to changed or unchanged pixels with texture information or using a change vector analysis. These algorithms are not perfect in terms of incorporating accurate and full statistical characteristics for large multi-dimensional data. Furthermore, they do not yield the best detection performance for new datasets. Recently, a deep convolution neural network (DCNN) was developed to produce a hierarchy of feature-maps such as learned filters. The aforementioned DCNN can automatically learn a complex feature space from a huge amount of image data. A DCNN can achieve a superior performance compared to conventional classification algorithms. A restricted Boltzmann machine (RBM) [19], a convolutional neural network (CNN) [20; 21; 22], and deep belief networks (DBNs) [23] have been proposed for use in change detection.