# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 1 Introduction  
With the continuous development of remote sensing technology, a large number of high-resolution remote sensing images have been generated. How to effectively identify and classify high-resolution remote sensing images automatically has become a technical problem that needs to be solved urgently in the field of remote sensing [1]. High-resolution remote sensing image scene classification is widely used in land use, natural disaster monitoring, urban planning, computer mapping, vegetation mapping, geospatial target detection, and other applications [2; 3; 4]. In terms of abstraction level, scene classification of remote sensing images has experienced the development from pixel to object and then to the semantic scene [5]. In the early 1970s, the spatial resolution of satellite images was low, and the pixel size was usually larger and at most close to the size of the target of interest, so most remote sensing image analysis methods were based on per-pixel or even sub-pixel analysis [6]. With the development of remote sensing technology and the continuousimprovement of spatial resolution, scene classification based solely on pixel-level encountered a bottleneck [7]. Therefore, researchers described and analyzed the object-level of remote sensing images. Although the performance of the object-level classification method is better than that of the pixel-level classification method, semantic information is not involved. Therefore, researchers began to analyze the semantic-level of scenes [7]. The scene image mentioned here refers to local image patches manually extracted from large-scale remote sensing images containing explicit semantic categories. Marking images according to semantic categories is the goal of semantic-level scene classification, and it is a challenging problem, because when describing a land cover image of a given category, there may be great variability in different scales and directions. With the refinement of classification, the problem of high intra-class variability and low inter-class distance becomes more and more serious [5]. For semantic-level remote sensing image scene classification, effective feature representation plays an important role in constructing a high-performance scene classification method. According to the features used in remote sensing scene classification, the existing remote sensing image scene classification methods can be divided into three categories: the methods based on low-level visual features, the methods based on middle-level visual features, and the methods based on high-level visual features [8]. Among them, low-level visual features mainly represent spectral, texture, shape, and spatial information of remote sensing images extracted by professionals. The usual methods are color histogram [9], texture descriptor, generalized search tree (GIST) [10], scale-invariant feature transformation (SIFT) [11], histogram of oriented gradient (HOG) [12], and so on. Low-level visual features are obtained by manual feature extraction. Due to the subjectivity of the selection of features and the semantic gap between semantic categories of the scene and low-level visual features, the scene classification methods based on low-level visual features are often not so effective when the remote sensing scene images become complex. Middle-level visual features include two cases. The first case is to abstract the low-level visual features by means of quantization and coding, such as bag of visual words (BoVW) [13], spatial pyramid matching (SPM) [14], probabilistic topic models (PTM) [15], Fisher vector coding [16], latent Dirichlet allocation (LDA) [17], etc. The second case is unsupervised feature learning, which can automatically learn features from a large number of unmarked data through a specific unsupervised learning algorithm, such as principal component analysis (PCA) [18], k-means clustering, sparse coding [19], autoencoder [20], etc. Middle-level visual features are the bridge between the lower-level visual features and semantic categories of scenes.