# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 6 Conclusions  
This study tested a new approach to obtain high-precision land-use information from remote sensing images, using a new CNN-based model (WFCNN) to obtain multi-scale image features. Experimental comparisons with the currently used SegNet and RefineNet models, along with two variants on the original WFCNN model, demonstrated the advantages of this approach. This paper discusses the influence of classification feature selection, up-sampling method, and fusion strategy on segmentation accuracy, indicating that WFCNN can also be applied to other high spatial resolution remote sensing images.  
Analyzing the data characteristics of high-resolution remote sensing images allowed the effects of spatial resolution on feature extraction to be fully considered, leading to the adoption of a hierarchical pooling strategy. In the initial stage of feature extraction, a large cell length step was used in WFCNN because the feature values were relatively scattered, allowing the pooling operation to accentuate the advantages of feature aggregation. In the subsequent stage, a smaller pooling step size was adopteddue to the large difference between the current resolution and the original image. The experimental results showed that this strategy was more conducive to generating features with good discrimination.  
The data characteristics of adjacent regions within remote sensing images were fully considered to effectively combine low-level and high-level semantic features. The proposed WFCNN model uses a variable-weight fusion strategy that adjusts features using a weight adjustment layer to ensure their stability. This strategy is more conducive to extracting effective features than those adopted by other models.  
Our method requires training images to be marked pixel-by-pixel, creating a large workload. In subsequent research, we intend to introduce a semi-supervised training method to reduce this requirement and make the model more applicable to real-world situations.  
Conceptualization, C.Z. and X.Y.; methodology, C.Z.; software, Y.C.; validation, S.G., X.Y. and F.L.; formal analysis, C.Z. and L.S.; investigation, A.K. and D.Z.; resources, F.L.; data curation, A.K.; writing--original draft preparation, C.Z.; writing--review and editing, C.Z.; visualization, S.G. and D.Z.; supervision, C.Z.; project administration, C.Z. and S.G.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science Foundation of Shandong, grant numbers ZR2017 MD018; the Key Research and Development Program of Ningxia, Grant numbers 2019 BEH03008; the National Key R and D Program of China, grant number 2017 YFA0603004; the Open Research Project of the Key Laboratory for Meteorological Disaster Monitoring, Early Warning and Risk Management of Characteristic Agriculture in Arid Regions, Grant numbers CAMF-201701 and CAMF-201803; the arid meteorological science research fund project by the Key Open Laboratory of Arid Climate Change and Disaster Reduction of CMA, Grant numbers IAM201801. The APC was funded by ZK2017 MD018.  
The authors declare no conflict of interest.