###### Abstract
The concept underlying technology that performs specific target segmentation in high-resolution remote sensing images is to construct the object-relational map between remote sensing images and the target space [29; 30; 31; 32]. The process of increasing the resolution of remote sensing images from low to high is also a process of obtaining richer surface information [33]. This means that the same target in the remote sensing image is more abundant in terms of characterization information [34; 35; 36]. On the one hand, it shows that the characteristics of the target can be obtained using an increased amount of information, and on the other hand, it requires the segmentation algorithm to be more complex and accurate. In glacier classification based on remote sensing images, there is a certain nonlinear mapping between the target and the feature space, and the glacier classification algorithm essentially establishes the explicit nonlinear mapping model [37]. CNN image classification [38] divides remote sensing images into uniformly sized image blocks for sequential recognition and classification [39]. The CNN algorithm has outstanding performance in terms of data local feature extraction. [PERSON] et al. [40] used CNN integration to realize the automatic mapping of the border of the debris area and achieved good results. [PERSON] et al. [37] used a CNN model to automatically obtain pixel-level glacier information. The CNN (GlacierNet) proposed by [PERSON] [41] was applied to glaciers, and [PERSON] et al. [42; 43] then developed a CNN method suitable for the surface debris area; the classification results of this method delineated accurate debris-covered glacier boundaries and significantly reduced the training time. In the classification of remote sensing images using CNN, the key issue is the number of convolutional layers. The higher the number of layers of convolution, the higher the number of features that can be obtained. However, owing to limitations regarding the number of sample points, an excessive number of layers can cause overfitting. Furthermore, CNNs cannot classify hyperspectral images directly, so dimensionality reduction is required for CNNs to obtain better image texture, spatial features, and other information. Therefore, the classification of high-resolution remote sensing images carried out by CNN can make full use of the texture and spatial context semantics of remote sensing images. However, CNN classifiers based on contextual semantics may introduce uncertainties at object boundaries, which leads to excessive smoothing to some extent [44]. Further, objects with less spatial information are likely to be misclassified [45; 46]. RF [16], which can accept high-dimensional data with different attributes, is a pixel-based classification method; pixel-by-pixel classification makes the boundaries of features clearer [47; 48; 49]. [PERSON] et al. [25] used multi-temporal data based on the RF method to realize the automatic classification of the borders of non-debris-covered glaciers and debris-covered glaciers. [PERSON] et al. [11] added the characteristics of glacier movement based on the aforementioned study [25] and optimized the RF algorithm to extract debris-covered glaciers. [PERSON] et al. [50] compared three machine learning algorithms--support vector machines, artificial neural networks, and random forests--to identify debris-covered glaciers; the results showed that the RF classification accuracy was the highest. [PERSON] et al. [51] compared the classification results of six classifiers--K-nearest neighbor, support vector machine, gradient boosting tree, decision tree, random forest, and multi-layer perceptron--showing that the use of RF classifiers can obtain the highest classification accuracy and accurately obtain information regarding debris-covered glaciers. The RF method is an advanced version of the decision tree model [47]. The essence of the decision tree method is to measure the uncertainty of various information, and its core step is 'counting' rather than 'training'.