## 1 Introduction  
With the continuous collection of massive multi-temporal remote sensing images, such as multi-spectral [1, 2], synthetic aperture radar (SAR) [3], hyperspectral [4], and unmanned aerial vehicle (UAV) images [5], these multi-temporal remote sensing images have been promoted in practical applications. In this data context, change detection (CD) is one of the most meaningful technologies, which aims to quantitatively and qualitatively obtain the change information of ground objects by analyzing bitemporal remote sensing images. In many practical situations, these changes have potential significance, such as urban development planning, natural disaster assessment, dynamic monitoring of ecological environment, and natural resource management [6, 7, 8]. In the early stages, in order to obtain land cover change information, the traditional CD technology usually includes the following steps. First, the bitemporal image needs to be preprocessed, including radiation correction, ensemble correction, and spatial registration [9, 10]. Second, a difference image (DI) between bitemporal images can be acquired by image ratio [11], image difference [12], change vector analysis [13, 14], etc. Finally, a threshold or a clustering algorithm is applied to segment DI into binary change map (BCM), such as Otsu [15, 16], double-window flexible pace search [17, 18], K-means [19], fuzzy c-means [20, 21], and so on. Since bitemporal images are usually collected under different imaging conditions, such as illumination, season, etc., the different images may contain a large number of spurious differences [22]. Moreover, these methods usually use pixels as processing units, and either thresholding or clustering can cause a lot of noise in the results of these methods. To address the limitations of pixel-level methods, many scholars have made great efforts in CD and proposed various object-based CD methods [23, 24]. In general, object-based methods first need to segment the image to obtain multi-scale objects. Universal image segmentation techniques include fractal net evolution segmentation approach [25], simple linear iterative clustering (SLIC) superpixel segmentation [26], etc. These approaches frequently generate multi-scale objects or superpixels through region growing, i.e., objects or superpixels are obtained by gradual pixel binning with similar spectral values. Therefore, each superpixel or object is composed of a homogeneous set of pixels. The CD can then be achieved by the object for the image analysis and processing unit. For example, in the early stages, [PERSON] et al. proposed an object-based CD based on correlation image analysis and image segmentation [27]. An object-based approach is based on multiple classifiers and multi-scale uncertainty analysis for CD with high-resolution (HR) remote sensing images [28]. Recently, some novel object-based approaches have made some efforts. For instance, [PERSON] et al. promoted an object-oriented key point vector distance to obtain binary CD [29]. This method can significantly improve the performance of the difference image, as it measures the difference between the key-points vectors of two objects in bitemporal images. Similar methods are available in [30, 31, 32]. Although the aforementioned approaches have made remarkable progress, some limitations are still unavoidable. These limitations mainly include the following three aspects:  
* Traditional methods are difficult to deal with and analyze irregular objects effectively as the multi-scale objects or the superpixels represent unstructured data. Therefore, there is still a lack of effective representative feature extraction approach for unstructured data. * Image segmentation itself is a challenging task, and usually some parameters need to be adjusted to obtain better segmentation results. Moreover, the error of image segmentation may accumulate in the change detection task to some extent. Therefore, object-based change detection is severely limited by the performance of image segmentation.