## 1 Introduction  
Remote sensing images are important data for spatial information processing. Automatic analysis of feature information in images has an important application significance [1; 2; 3; 4; 5; 6]. Remote sensing image fully exploits geographic information, which is of great significance in the fields of forestry monitoring [2], water system monitoring [3] and geological exploration [4]. Remote sensing technology analysis of ground objects and their changes with time and space have great application significance in the fields of military reconnaissance, economic construction, meteorological forecasting [5; 6; 7; 8] and earthquake disaster early warning [9], which are related to the national economy and people's livelihood. A large amount of urban geographic information contained in remote sensing images, which can be used in many fields, such as digital cities, intelligent transportation, navigation maps,and urban planning [10; 11]. Therefore, automatic analysis of remote sensing images has become an important research topic for geospatial information detection. Remote sensing image contains a wealth of feature information. Rich geospatial information can be obtained through automatic classification methods to serve all aspects of human's life. The multi-objective classification of remote sensing images is to assign a label to each pixel in the image. It divides the image into different semantic regions. The researchers have carried out many explorations. In the 1980s, the statistical analysis of the features of remote sensing images is the mainstream method, which required different feature extraction methods for different tasks [12]. The development of digital image processing has promoted the advancement of image analysis, and remote sensing image classification can be realized based on artificial features. Commonly features include color histogram [13], direction gradient histogram [14] and scale-invariant feature transform [15], which contain a large amount of information. The artificial feature-based method can deal with some basic remote sensing image classification problems, but for more complex remote sensing images, it cannot achieve the desired performance. In recent years, machine learning is developing rapidly, which leads to many remote sensing image classification and semantic segmentation algorithms based on machine learning methods are proposed. For example, [PERSON] et al. [16] jointly used low-rank representation (LRR), spectral-spatial graph regularization and subspace learning to classify the objects in hyperspectral images, and proposed a self-supervised low-rank representation framework. [PERSON] et al. [17] used classification and regression tree (CART) to classify remote sensing images, which achieved the accurate classification of different land objects in remote sensing image. However, its performance still needs to be improved. [PERSON] et al. [18] used principal component analysis to preserve the main information of the image and reduce the feature's dimension. This method can obtain the invariant features in the classification task, which improves the efficiency of classification. [PERSON] et al. [19] used K-means to segment remote sensing image. In this method, the initial class number and centers are determined by the probability density function of the first principal component. The machine learning-based remote sensing image classification methods have achieved good results and strong stability. With the increasing demand for remote sensing image interpretation, the goals are becoming more complex and diverse. The algorithms based on feature engineering and shallow learning model are not suitable to solve the complex multi-class classification of remote sensing image. Deep learning has been widely used in many fields. This kind of method autonomously learns data features through massive training data, which greatly accelerates the development of image recognition.