基于多尺度近端特征拼接网络的高光谱图像分类方法

doi:10.11959/j.issn.1000-436x.2021024

Abstract

Abstract:

Aiming at the phenomenon that the hyperspectral classification algorithm based on traditional CNN model was not expressive enough in detail and the network structure was too complex, a hyperspectral image classification method based on multi-scale proximal feature concatenate network (MPFCN) was designed.By introducing multi-scale filter and cavity convolution, the model could be kept light and the discriminative features of the space spectrum could be obtained, and the correlation between the proximal features of the CNN was proposed to further enhance the detail expression.Experimental results on three benchmark hyperspectral image data sets show that the proposed method is superior to other classification models.

Key words: convolutional neural network, hyperspectral image classification, feature concatenate, multi-scale filter, di-lated convolution

CLC Number:

TP302

Hongmin GAO, Xueying CAO, Zhonghao CHEN, Zaijun HUA, Chenming LI, Yue CHEN. Hyperspectral image classification method based on multi-scale proximal feature concatenate network[J]. Journal on Communications, 2021, 42(2): 92-102.

Figures/Tables 22

References 23

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数据集	类别	名称	样本数/个	样本总数/个
	C1	Alfalfa	46
	C2	Corn-notill	1 428
	C3	Corn-mintill	830
	C4	Corn	237
	C5	Grass-pasture	483
	C6	Grass-trees	730
	C7	Grass-pasture-mowed	28
IP	C8	Hay-windrowed	478	10 249
	C9	Oats	20
	C10	Soybean-notill	972
	C11	Soybean-mintill	2 455
	C12	Soybean-clean	593
	C13	Wheat	205
	C14	Woods	1 265
	C15	Buildings-Grass-Trees_Drives	386
	C16	Stone-Steel-Towers	93
	C1	Asphalt	6 631
	C2	Meadows	18 649
	C3	Gravel	2 099
	C4	Trees	3 064
PU	C5	Painted metal sheets	1 345	42 776
	C6	Bare Soil	5 029
	C7	Bitumen	1 330
	C8	Self-Blocking Bricks	3 682
	C9	Shadows	947
	C1	Brocoli_green_weeds_1	2 009
	C2	Brocoli_green_weeds_2	3 726
	C3	Fallow	1 976
	C4	Fallow_rough_plow	1 394
	C5	Fallow_smooth	2 678
	C6	Stubble	3 959
	C7	Celery	3 579
SA	C8	Grapes_untrained	11 271	54 129
	C9	Soil_vinyard_develop	6 203
	C10	Corn_senesced_green_weeds	3 278
	C11	Lettuce_romaine_4wk	1 068
	C12	Lettuce_romaine_5wk	1 927
	C13	Lettuce_romaine_6wk	916
	C14	Lettuce_romaine_7wk	1 070
	C15	Vinyard_untrained	7 268
	C16	Vinyard_vertical_trellis	1 807

多尺度滤波器模块数/个	patch尺寸	OA	AA	KA
5	27×27	98.51%	98.50%	98.31%
4	27×27	98.48%	98.42%	98.27%
	13×13	95.89%	95.65%	95.31%
	27×27	98.43%	97.86%	98.22%
3	13×13	96.43%	96.11%	95.94%
	7×7	83.13%	82.51%	79.66%
	27×27	98.03%	96.31%	97.75%
2	13×13	95.85%	95.69%	92.26%
	7×7	85.86%	84.32%	83.93%

多尺度滤波器模块数/个	patch尺寸	OA	AA	KA
5	27×27	99.57%	99.14%	99.43%
4	27×27	99.50%	98.84%	99.34%
	13×13	98.29%	97.65%	97.74%
	27×27	99.40%	98.62%	99.21%
3	13×13	98.56%	97.70%	98.10%
	7×7	92.49%	90.46%	89.91%
	27×27	99.08%	97.83%	98.78%
2	13×13	98.30%	97.67%	97.74%
	7×7	94.28%	92.59%	92.36%

多尺度滤波器模块数/个	patch尺寸	OA	AA	KA
5	27×27	99.70%	99.71%	99.66%
4	27×27	99.78%	99.73%	99.76%
	13×13	95.97%	97.77%	95.51%
	27×27	99.70%	99.70%	99.67%
3	13×13	95.09%	98.03%	95.44%
	7×7	90.44%	93.58%	89.34%
	27×27	99.51%	99.49%	99.45%
2	13×13	95.05%	97.45%	94.48%
	7×7	90.77%	93.32%	89.75%

类别	SVM	DCNN	ResNet	MCNN	MPFCN
Alfalfa	37.21%	100%	96.34%	95.93%	97.93%
Corn-notill	77.60%	98.17%	97.20%	97.17%	98.52%
Corn-mintill	57.54%	93.04%	97.05%	94.24%	96.90%
Corn	60.29%	100%	99.77%	98.59%	99.84%
Grass-pasture	90.40%	97.01%	99.20%	96.55%	96.27%
Grass-trees	92.89%	97.49%	97.95%	99.49%	99.59%
Grass-pasture-mowed	86.36%	94.00%	100%	94.67%	99.8%
Hay-windrowed	97.90%	99.88%	97.90%	99.84%	100%
Oats	55.05%	96.875%	100%	97.92%	100%
Soybean-notill	50.45%	96.34%	96.80%	96.42%	97.75%
Soybean-mintill	72.16%	98.44%	99.30%	98.43%	98.30%
Soybean-clean	50.56%	93.16%	92.98%	95.94%	96.50%
Wheat	93.86%	100%	98.91%	95.83%	98.73%
Woods	89.00%	99.12	99.52%	98.60%	99.94%
Buildings-Grass-Trees-Drives	48.02%	99.14%	99.86%	99.90%	98.27%
Stone-Steel-Towers	91.67%	91.67%	88.71%	96.03%	97.62%

Hyperspectral image classification method based on multi-scale proximal feature concatenate network

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方法	OA	AA	KA	训练参数数量/个
SVM	75.07%	71.65%	71.52%	—
DCNN	97.50%	97.15%	97.15%	1 109 776
ResNet	97.97%	97.41%	97.69%	11 002 320
MCNN	97.62%	97.22%	97.29%	43 402 288
MPFCN	98.51%	98.50%	98.31%	12 593 104

方法	OA	AA	KA	训练参数数量/个
SVM	87.52%	82.90%	83.28%	—
DCNN	98.54%	96.47%	98.06%	1 109 776
ResNet	98.66%	96.53%	98.22%	10 989 129
MCNN	98.98%	98.68%	98.65%	43 396 841
MPFCN	99.56%	99.14%	99.43%	12 583 497

方法	OA	AA	KA	训练参数数量/个
SVM	89.21%	93.65%	87.94%	—
DCNN	98.91%	99.22%	98.79%	1 109 776
ResNet	98.36%	98.96%	98.18%	10 994 640
MCNN	98.95%	99.25%	98.84%	43 402 288
MPFCN	99.70%	99.71%	99.66%	12 593 104

分类方法	训练时间/s	测试时间/s
DCNN	336.95	35.90
ResNet	335.24	35.83
MCNN	388.32	31.50
MPFCN	131.75	11.80

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类别	SVM	DCNN	ResNet	MCNN	MPFCN
Asphalt	91.66%	99.68%	99.99%	99.35%	99.64%
Meadows	94.91%	99.92%	99.93%	99.20%	99.92%
Gravel	71.37%	98.81%	97.94%	96.67%	99.06%
Trees	85.00%	90.38%	92.39%	98.22%	98.07%
Painted metal sheets	99.00%	96.39%	95.91%	99.77%	99.76%
Bare Soil	68.09%	100%	99.81%	99.15%	100%
Bitumen	49.41%	99.40%	99.78%	98.72%	99.10%
Self-Blocking Bricks	86.76%	97.90%	97.93%	98.92%	99.24%
Shadows	99.89%	85.77%	85.12%	98.17%	97.52%