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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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Abstract 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.
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Keywords
Hyperspectral remote sensing
multi-scale characteristic
gray-level attribute feature
local binary pattern
feature fusion
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Issue Date: 24 September 2021
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