# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 1 Introduction
The biggest advantage of CNNs lies in their ability to simultaneously extract specific pixel features as well as spatial features of pixel blocks. This approach can reasonably set the structure of the convolutional layer according to the characteristics of the image and the extraction target, so as to extract features that meet the requirements, achieving good results in image processing [34; 36]. The most widely used CNNs in the field of camera image processing include fully convolutional networks (FCNs) [38], SegNet [39], DeepLab [40], RefineNet [41], and U-NET [42]. DeepLab expands the convolution for rich features in camera images, enabling the model to effectively expand the receptive field without increasing the calculations required. SegNet and UNET can establish a symmetrical network structure and segment camera images, resulting in an efficient utilization of high-level semantic features. RefineNet uses a multipath structure to combine coarse high-level semantic features and relatively fine low-level semantic features by equal-weight fusion. As CNNs have outstanding advantages in feature extraction, they have been widely used in other fields, such as real-time traffic sign recognition [43], pedestrian recognition [44], apple target recognition [45], plant disease detection [46], and pest monitoring [47]. Researchers have established a method to extract coordinate information of an object from street view imagery [48] using CNNs. Compared to camera images, remote sensing images have fewer details and more mixed pixels. When using a CNN to extract information from remote sensing images, the influence of the convolution structure on feature extraction must be considered [49]. Existing CNN structures are mainly designed for camera images with a high level of detail; therefore, a more ideal result can be obtained through adjustments that consider the specific characteristics of remote sensing images [50; 51]. Researchers have proposed a series of such adjustments for applying CNNs to remote sensing image processing [3] and some classic CNNs have been widely applied in this field [52; 53]. Based on the characteristic analysis of target objects and specific remote sensing imagery, researchers have established a series of CNNs such as two-branch CNN [49], WFS-NET [54], patch-based CNN [55], and hybrid MLP-CNN [56]. Researchers have also used remote sensing images to create many benchmark datasets, such as EuroSAT [57] or the Inria Aerial Image dataset [58], to test the performance of CNNs. In this study, we established a CNN structure based on variable weight fusion, the weight feature value convolutional neural network (WFCNN), and experimentally assessed its performance. The main contributions of this work are as follows. * Based on the analysis of the data characteristics of remote sensing images, fully considering the impact of image spatial resolution on feature extraction, we establish a suitable network convolution structure;
* The proposed approach can effectively fuse low-level semantic features with high-level semantic features and fully considers the data characteristics of adjacent areas around objects on remote sensing images. Compared with the strategies adopted by other models, our approach is more conducive to effective features.