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Remote Sensing for Land & Resources    2019, Vol. 31 Issue (3) : 59-64     DOI: 10.6046/gtzyyg.2019.03.08
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Spatial and spectral feature hierarchical fusion for hyperspectral image feature extraction
Benzuo YAO1, Fang HE2
1. Anhui Police College, Hefei 230088, China
2. School of Nuclear Engineering,Rocket Force Engineering University, Xi’an 710025, China;
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Abstract  

In this paper, the multi-dimensional adaptive weighted filter (AWF) is used to filter the hyperspectral image with a certain dimension which are reduced by the feature extraction method based on spectral dimension. Then, the filter results obtained on all scales are hierarchical fusion into a new image, and the hierarchical fusion framework is designed. These treatments make the essential spatial and spectral features in hyperspectral images extracted effectively, so the classification accuracy is improved. The principal component analysis (PCA) algorithm is integrated into the framework, and a hierarchical fusion-principal component analysis (HF-PCA) algorithm is proposed. This method not only reduces the redundancy between bands, but also weakens the internal differences of the samples and improves the classification accuracy of hyperspectral images. Experimental results on the Indian Pines and Salinas databases demonstrate that the classification accuracy obtained by the HF-PCA algorithm is significantly higher than that of other algorithms, even when the number of training samples is small, and the maximum value of the overall classification accuracy is 86.73% and 95.01%, respectively. The classification accuracy of hyperspectral images is improved effectively.
Keywords spatial and spectral feature      hierarchical fusion      hierarchical fusion-principal component analysis      hyperspectral image classification     
:  TP751  
Issue Date: 30 August 2019
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Benzuo YAO
Fang HE
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Benzuo YAO,Fang HE. Spatial and spectral feature hierarchical fusion for hyperspectral image feature extraction[J]. Remote Sensing for Land & Resources, 2019, 31(3): 59-64.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.03.08     OR     https://www.gtzyyg.com/EN/Y2019/V31/I3/59
Fig.1  Adaptive weighted filters
Fig.2  Frame of HF-PCA
Fig.3  Indian Pines hyperspectral image
Fig.4  Salinas hyperspectral image
Fig.5  Classification results of different algorithms in Indian Pines dataset
地物类别 KNN PCA HF-PCA 地物类别 KNN PCA HF-PCA
Alfalfa 80.56 80.56 97.78 Oats 80.00 70.00 100
Corn-notill 46.57 45.84 84.32 Soybeans-notill 61.21 60.46 87.06
Corn-mintill 57.49 56.60 79.06 Soybeans-mintill 66.21 64.71 90.21
Corn 28.00 29.33 47.20 Soybeans-clean 44.40 43.16 70.05
Grass/pasture 78.87 78.21 87.93 Wheat 94.36 93.33 98.15
Grass/trees 94.37 94.52 97.63 Woods 88.02 88.69 95.97
Grass/pasture-mowed 100 100 97.78 Buildings-grass-tree-drives 41.14 39.78 62.94
Hay-windrowed 94.71 94.05 98.37 Stone-steel-towers 85.54 85.54 92.53
Tab.1  Classification accuracy of different types of features in Indian Pines dataset by different algorithms(%)
Fig.6  Classification results of different algorithms in Salinas dataset
地物类别 KNN PCA HF-PCA 地物类别 KNN PCA HF-PCA
Brocoli-green-weeds_1 98.54 98.54 99.75 Soil-vinyard-develop 97.49 97.39 99.56
Brocoli-green-weeds_2 98.54 98.43 99.70 Corn-senesced-green-weeds 83.94 83.20 92.49
Fallow 80.88 79.09 96.62 Lettuce-romained-4wk 88.93 88.93 89.76
Fallow-rough-plow 98.70 98.70 99.07 Lettuce-romained-5wk 100 100 99.97
Fallow-smooth 95.36 96.27 97.83 Lettuce-romained-6wk 97.57 96.91 99.05
Stubble 98.85 98.85 99.78 Lettuce-romained-7wk 87.54 87.44 96.83
Celery 98.93 98.93 99.60 Vinyard_untrained 59.65 59.32 88.25
Grapes-untrained 70.39 69.30 89.57 Vinyard_vertical-trellis 89.66 89.60 96.18
Tab.2  Classification accuracy of different types of features in Salinas dataset by different algorithms(%)
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