# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 1 Introduction  
With the development of society and the economy, the depth and breadth of human utilization of land resources have been greatly improved, leading to a significant increase in the frequency of land changes [1, 2, 3]. Accurately acquiring information about land changes is of great significance for understanding the relationship between humans and nature and for promoting sustainable development of the social economy and ecological environment, such as deforestation, forest fires, and landslide disaster monitoring [4, 5]. Remote sensing technology can achieve large area observations in a short time and has become one of the most important means to extract the change information of land [6, 7, 8]. However, with the development of spatial resolution of remote sensing sensors, the geometric and texture information is prominent. Although it provides rich information for change detection,it still faces new challenges due to the serious phenomenon of \"same object, different spectrum\" and \"foreign object, same spectrum\" [9; 10; 11]. The change detection methods include two main categories: detection after classification and detection before classification [12]. In the first one, the accuracy of detection depends on the classification result, and errors can accumulate and propagate. In addition, the computing efficiency of change detection will be affected by the classification operation when the image size is too large. The other one detects the changing area according to its characteristics, which can enable the detection of the targeted areas and improve efficiency. It includes the difference method, ratio method, change vector analysis (CVA), and model-based methods [13; 14; 15]. The difference and ratio methods are usually used for the single-band images, and CVA can be regarded as a multi-band extension of the difference method. However, they have some common problems; for example, the threshold is difficult to determine, and using only the threshold as the judgment criterion will inevitably lead to error detection. Model-based methods train the detection model according to the samples, which can continually optimize the model parameters and obtain the optimal change detection result. Thus, model-based methods have become the widely used methods, wherein the most popular models are deep learning and random forest. For example, [PERSON] et al. [16] proposed a spatial-spectral attention network to inhibit the pseudo-change noise, preserve the shape of the changed areas, and improve the change detection accuracy. However, deep learning often requires a large number of samples to train the model well, and it has poor transferability. In the case of small samples in specific areas, the random forest is also considered to be a good tool [17]. From another perspective, change detection methods can be divided into two types according to the basic units of operation: pixel-based and object-oriented. Traditional pixel-based algorithms take a single pixel as the basic unit, which can easily cause a \"salt and pepper\" phenomenon in the detection results due to the absence of context information [18; 19]. Object-oriented algorithms take a homogeneous region as the basic unit. The homogeneous region is composed of a group of connected pixels with the same characteristics, and its shape can be irregular. This special characteristic makes it possible to describe spatial relevance information, which can improve the \"salt and pepper\" phenomenon [20; 21]. [PERSON] et al. [22] demonstrated that object-oriented methods are more accurate than pixel-based methods by testing four common unsupervised change detection methods on the basis of the spectral information. [PERSON] et al. [23] proposed the object-oriented sorted-histogram comparison methods, which achieved satisfactory results. The characteristics that are extracted based on objects possess a higher-level description of the image [24]. False detection and missing detection can easily occur when relying only on spectral features for change detection.