## 2 Related Work
### Change Detection Based on Traditional Machine Learning  
Traditional machine learning is applied for change detection by employing feature extraction and unsupervised/supervised learning. First, land cover change features are extracted from two temporal images to be processed in a classification algorithm. There are three feature extraction approaches for change detection: image comparison, image stacking, and independent image analysis. For image comparison, a comparison operator model is used, such as \(DI\) or \(LR\). Let \(I_{1}\) and \(I_{2}\) be images for a pair of images with B bands acquired at times \(t_{1}\) and \(t_{2}\), respectively. \(DI\) is defined as follows:  
\[DI=|I_{1}-I_{2}| \tag{1}\]  
whereas \(LR\) is defined as follows:  
\[LR=\log|I_{1}/I_{2}|. \tag{2}\]  
Image stacking concatenates the multi-temporal images by extending the data dimensionality toward the channel direction. By contrast, independent image analysis extracts  
Figure 1: Change detection for reversed image order.  
a classification map for each single temporal image. In conjunction with the classification maps, the classification or clustering algorithm classifies the feature maps into a final binary decision class (changed or unchanged).  
Several algorithms have been proposed to identify land cover changes using traditional machine learning. They use unsupervised learning methods based on FCMs for change detection [1; 2; 3; 4; 5; 6]. [PERSON] et al. extracted the land cover change feature using the LR [1]. They clustered these feature vectors into two clusters (changed and unchanged) using a hybrid genetic FCM. They also employed a simple genetic algorithm to minimize the objective function of the FCM. [PERSON] et al. applied DI and LR as feature vectors of land cover change that used FCM to cluster into changed or unchanged [2]. In addition, [PERSON] et al. proposed a fusion of mean ratio and LR operators to extract land cover change features [3]. They employed simple fuzzy clustering to classify those features into binary change detection. In addition, they improved the process by combining FCM with a Markov random field (MRF) [4]. [PERSON] et al. also used FCM and MRF for change detection by employing change vector analysis as a land cover change feature [5]. They improved the change detection algorithm by incorporating local and global information [6]. 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; thus, their applications lead to overfitting for invariant changes. Furthermore, they do not yield reasonably good performance in terms of change detection rate because they do not incorporate accurate information without supervision. To overcome these problems, supervised machine learning can be an option for change detection algorithms. [PERSON] et al. extracted the land cover change features based on texture information using a gray-level co-occurrence matrix, including angular second moment, contrast, correlation, and entropy features [7]. Then, they implemented an SVM to find an optimum hyperplane for classifying those feature vectors into binary classes (changed or unchanged). In addition, [PERSON] et al. [8], [PERSON] et al. [9], and [PERSON] et al. [10] proposed SVMs to detect land cover changes. However, feature selection and feature extraction methods should be well-designed for optimal hyperplanes. Recently, DCNN algorithms have also been proposed. They can automatically learn a complex feature space from a large amount of data without heuristic feature extractions. Thus, DCNNs can achieve a better generalization model for change detection compared with traditional machine learning approaches.