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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 36-44     DOI: 10.6046/zrzyyg.2020303
Classification of remote sensing images based on multi-scale feature fusion using local binary patterns
JIANG Yanan1(), ZHANG Xin2, ZHANG Chunlei3, ZHONG Chengcheng1, ZHAO Junfang1
1. School of Science, China University of Geosciences(Beijing), Beijing 100083, China
2. School of Statistics, Beijing Normal University, Beijing 100875, China
3. Beijing Zhongdirunde Petroleum Technology Co.Ltd., Beijing 100083, China
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For the classification of remote sensing images, traditional feature extraction methods frequently ignore their intrinsic properties and the multi-scale local characteristics of the images. As a result, only a small amount of image information can be acquired. Given this, this study proposed a model of multi-scale gray level and texture feature fusion (Ms_GTSFF ) for the feature extraction of remote sensing images, and the extraction steps are as follows. Firstly, extract the gray-level features of the images at different scales. Then obtain the local texture features of the images using the local binary pattern (LBP) algorithm and meanwhile, obtain the image features of a larger receptive field using a multi-scale method. Afterward, obtain the gray-level attributes corresponding to various codes using the obtained multi-scale LBP histograms. Finally, code and fuse multi-scale feature information obtained from the above steps to constitute the Ms_GTSFF feature extraction model, to which multiple machine learning classifiers are connected for classification and recognition. Taking the aerial hyperspectral remote sensing images of Xiongan New Area (Matiwan Village) as the test dataset, the feature extraction and classification tests were performed following the data preprocessing by blocks. The classification accuracy was up to 99.44%, indicating a great improvement in the recognition capability compared with traditional methods. This verified the effectiveness of the proposed model in enhancing the feature extraction capability and improving the classification and reorganization performance of remote sensing images.

Keywords Hyperspectral remote sensing      multi-scale characteristic      gray-level attribute feature      local binary pattern      feature fusion     
ZTFLH:  TP79  
Issue Date: 24 September 2021
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Chunlei ZHANG
Chengcheng ZHONG
Junfang ZHAO
Cite this article:   
Yanan JIANG,Xin ZHANG,Chunlei ZHANG, et al. Classification of remote sensing images based on multi-scale feature fusion using local binary patterns[J]. Remote Sensing for Natural Resources, 2021, 33(3): 36-44.
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Fig.1  Local Binary Pattern feature extraction schematic
Fig.2  Multi-scale LBP feature extraction method
00000000 0 0
00000001 1 1,2,4,8,16,32,64,128
…… …… ……
11011111 34 127,191,223,239,247,251,253,254
11111111 35 255
Tab.1  Rotation invariant LBP coding and original LBP coding correspondence table
Fig.3  Model of Ms_GTSFF method
Fig.4  Diagram of the dataset
Fig.5  Histogram of the area of pear by LB P 8,1 ri 36 feature extraction
Fig.6  Grayscale image and LB P 8,1 ri 36 histogram of 20 types of ground objects under PCA01
Fig.7  Gray-scale feature maps and histogram of MsLB P 8,1 ri 36 of images in scales
分类器 Original PCA20 LBP Ms_GTSFF
Bayes 29.16 56.81
KNN 50.88 72.53
DT 43.75 61.17
BP 75.83 81.81 93.28 99.44
SVM 48.75 77.55
RF 56.52 77.54 93.54 98.94
XGB 61.27 79.52 91.70 99.20
LightGBM 61.32 79.30 92.44 99.17
LeNet5 94.72
GoogLeNet 95.59
Tab.2  Comparison of accuracy of classifiers(%)
Fig.8  Comparison of classification results of various scenes in remote sensing images
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