# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 1 Introduction
It is an inevitable trend to introduce deep features, such as texture characteristics [25]. There are two classical texture feature extraction methods: local binary pattern (LBP) [26] and gray level co-occurrence matrix (GLCM) [27]. LBP describes the texture by using the relationship between the central pixel and the adjacent pixel in the local neighborhood. For example, [PERSON] et al. [28] combined LBP and speeded-up robust features (SURF) based on the fixed-size objects to complete the change detection of Google Earth images. The traditional LBP is too simple and has poor noise resistance. With this development, a series of improved LBP-based texture extraction algorithms are proposed, such as binary rotation-invariant and noise-tolerant (BRINT) [29], Locally Encoded TRansform feature hISTogram (LETRIST) [30], local directional zigzag pattern (LDZP) [31], and overlapped multi-oriented tri-scale local binary pattern (OMTLBP) [32]. They combine the mean method, scale method, and histogram method to increase the noise resistance, breadth, and wholeness of the feature description. LBP can obtain the local texture information, but it is difficult to capture the overall information of an image [33]. GLCM describes the texture through the joint conditional probability among image gray levels. It can not only take into account the overall situation of the image but also describe the texture information in multiple directions by extracting different statistics. For example, [PERSON] et al. [34] proposed a change-detection method based on spectral and GLCM texture features, which can significantly increase the robustness of noise and spectral similarity. However, the traditional texture feature extraction methods can extract information only from single-band images. For multispectral remote sensing images with multiple bands, the bands must be treated as independent, which will lose some information hidden among the bands that could be used to distinguish changing areas. Thus, the description of texture features among bands is also important and cannot be ignored. To solve the above problems, the object-oriented remote sensing image change detection method based on a color co-occurrence matrix is proposed in this paper. The color co-occurrence matrix (CCM) [35] is an extension of the GLCM, which can describe the texture features not only within the band but also among the bands. First, the band combination of the remote sensing images of two periods is implemented to ensure the spatial consistency of the objects, where the multi-scale objects are generated on the basis of the graph-based superpixel segmentation method and the fractal net evolution approach (FNEA). Second, to obtain the optimal scale to establish the optimal objects, the overall goodness F-measure (OGF) index was used to evaluate the quality of the superpixels. Then, after extracting the spectral features of the object, the texture statistical variables, including band correlation, are extracted on the basis of the CCM. To accurately find representative features to overcome the Hughes phenomenon, where too many features lead to information redundancy and affect the detection accuracy, a cumulative backward search strategy (CBSS) is further designed on the basis of the ranking of the importance of the features. The main idea of the CBSS is to remove the features with the lowest contribution in turn until the number of features reaches the minimum set value. The sorting standard is the cumulative value of the importance of features obtained through multiple rounds of training. The combination with the smallest feature number and the highest accuracy is regarded as the best feature combination. Finally, change detection results are obtained by putting the differential image of selected features into the random forest model. The main contributions of this paper are summarized as follows:  
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