# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 1 Introduction
The scene classification methods based on middle-level visual features have more advantages than the classification methods based on low-level visual features and still have great potential for the expression and abstraction of complex scene content features. High-level visual features refer to the use of the deep learning method to extract abstract semantic information of images. It is well known that deep learning, like the mechanism of the human brain, is a process of abstraction layer by layer. The low-level features of deep learning are features such as edges or colors, while higher level features are the abstraction of lower-level features, and the features extracted by top-level of the deep learning methods are the abstract semantic information of the previously mentioned features. At present, there are many deep learning models, such as deep belief network (DBN) [21], deep Boltzmann machine (DBM) [22], stacked autoencoder (SAE) [23], convolutional neural network (CNN) [24], and so on. The deep learning methods can fully mine the correlation between data, automatically learn the complex structure of high-dimensional data. Top-level features of deep learning also have the characteristic of semantic abstraction, which can establish direct mapping with semantic categories of scenes. All these characteristics make high-level visual features more suitable for semantic scene classification. The methods based on high-level visual features have achieved the best results in scene classification of high-resolution remote sensing images. As the most important learning framework in scene classification, the convolutional neural network shows strong learning ability in scene classification of remote sensing images. Training new convolutional neural networks often requires a large amount of data. However, the number of images in the shared datasets of high-resolution remote sensing images is very limited, which fails to meet the requirements of designing an ideal model of convolutional neural network. In addition, it is difficult to train a new convolutional neural network, which requires high-performance computing devices and long training time. Therefore, there are relatively few related articles about training new convolutional neural networks with shared datasets of high-resolution remote sensing images. In order to further develop the deep learning methods in remote sensing image scene classification, it is necessary to construct new convolutional neural networks and conduct relevant researches. In order to solve the problems existing in the field of scene classification of high-resolution remote sensing images, this paper proposes a scene classification method of high-resolution remote sensing images based on RFPNet. The main contributions of the paper are as follows:  
1. The paper proposes a multi-view scaling strategy, which is a data amplification strategy, aiming to solve the problem of the limited number of images in the current high-resolution remote sensing image datasets. The difference between the multi-view scaling strategy and other dataset amplification methods is that different parts of the labeled boxes can be cropped randomly, and four interpolation algorithms are selected randomly to stretch the scale of the image, so as to serve as the input of the neural network. This method can not only enlarge the number of datasets, but also introduce noise through interpolation, thus improving the generalization ability of the convolutional neural network constructed. 2. This paper proposes the structure of RFPNet to learn high-level visual features of high-resolution remote sensing images for scene classification. The main characteristic of the RFPNet structure is to adopt the residual block, so that the accuracy will not reduce when adding more layers to the model constructed; The fusion strategy of pooling layer feature maps is proposed to ensure the integrity of information by solving the problem of information loss in the process of pooling.