基于混合光谱增强与多尺度空间聚合的高光谱图像分类方法

doi:10.13229/j.cnki.jdxbgxb.20240252

Abstract

Abstract:

Due to the presence of the same object with different spectrum and the same spectrum with different objects in hyperspectral images， spectral information alone cannot fully reflect the features of hyperspectral images， and spatial information can be introduced to capture the features of objects more accurately. Therefore， a hyperspectral image classification method based on hybrid spectral enhancement and multi-scale spatial aggregation is proposed in this paper. In this method， a hybrid spectral enhancement module is designed， multi-scale local features of the spectrum are constructed using wavelet transform， and global features of the spectrum are generated by Transformer architecture， so as to enhance the intra-class consistency of the spectral features. At the same time， a multi-scale spatial aggregation module is designed to extract the inherent multi-scale information of spatial features and establish the interaction between different scales， so as to generate a more robust land cover representation， thereby further improve the classification performance. The experimental results show that the proposed method is superior to other advanced networks， indicating that the method can effectively obtain more abundant spectral information and spatial feature representation.

Key words: hyperspectral image classification, hybrid spectral enhancement module, wavelet transform, multi-scale spatial aggregation module

CLC Number:

TP753

Ning OUYANG,Chen-yu HUANG,Le-ping LIN. Hyperspectral image classification based on hybrid spectral enhancement and multi-scale spatial aggregation[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(11): 3727-3735.

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References 21

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基线	小波变换	多尺度空间聚合模块	OA/%	AA/%	Kappa×100
SpectralFormer	×	×	80.46	72.49	77.88
	√	×	82.48	73.36	80.19
	×	√	87.72	83.31	86.00
	√	√	94.80	91.90	94.04

类别	ABLSTM	CNN3D	ViT	SpectralFormer	SSFTT	HEMA
1	26.19	57.14	68.96	74.19	100.00	100.00
2	59.92	80.73	67.68	75.84	83.33	96.16
3	48.90	66.48	66.75	63.25	74.96	87.59
4	38.63	39.08	52.04	46.04	74.10	80.76
5	84.86	95.39	80.94	91.00	92.45	97.43
6	90.94	98.30	93.56	92.93	98.10	98.88
7	18.75	34.28	30.76	50.00	48.00	92.30
8	84.84	98.98	92.82	97.75	99.49	100.00
9	4.54	10.86	13.33	20.83	100.00	83.33
10	69.80	77.44	63.88	76.57	73.29	86.22
11	73.08	78.49	82.11	90.50	87.30	97.53
12	50.00	48.94	42.17	63.05	56.38	98.52
13	92.00	94.59	83.13	88.60	90.90	97.90
14	97.69	97.46	98.07	97.66	97.18	97.83
15	69.84	72.93	71.15	71.97	81.86	95.32
16	60.00	81.63	64.51	59.70	64.51	60.60
OA/%	69.90	77.89	74.96	80.46	83.77	94.80
AA/%	60.62	70.80	66.99	72.49	83.62	91.90
Kappa×100	65.84	74.89	71.58	77.88	91.54	94.04

类别	ABLSTM	CNN3D	ViT	SpectralFormer	SSFTT	HEMA
1	94.15	99.04	96.16	93.34	99.61	99.21
2	75.67	68.80	70.93	87.46	81.12	97.19
3	25.96	39.53	49.13	91.15	53.96	92.15
4	97.65	98.51	98.45	98.66	98.72	99.28
5	30.99	56.97	30.10	41.39	57.08	48.84
6	91.01	84.21	96.77	96.86	98.61	99.68
7	98.92	98.66	99.92	99.97	99.96	99.95
8	81.01	82.22	84.56	90.22	91.11	94.33
9	28.37	68.43	59.33	90.53	86.68	85.34
OA/%	84.94	91.47	91.06	93.69	95.38	96.75
AA/%	69.30	77.37	76.15	85.51	85.20	90.67
Kappa×100	80.80	89.01	88.44	91.79	93.99	95.76

数据集		ABLS-TM	CNN-3D	ViT	SpectralFormer	SSFTT	HEMA
IN	训练	58.76	24.61	45.99	86.25	22.16	62.23
IN	测试	1.94	0.54	1.69	3.94	0.58	2.28
LK	训练	58.63	13.25	43.44	73.77	20.33	49.47
LK	测试	59.49	16.19	56.50	123.47	18.96	64.92

Hyperspectral image classification based on hybrid spectral enhancement and multi-scale spatial aggregation

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