基于CNN-ViT混合特征优化的小样本高光谱图像分类

doi:10.11947/j.AGCS.2025.20250293

测绘学报 ›› 2025, Vol. 54 ›› Issue (12): 2233-2246.doi: 10.11947/j.AGCS.2025.20250293

基于CNN-ViT混合特征优化的小样本高光谱图像分类

张津¹(), 冯凡¹(), 戴晨光¹, 张振超¹, 于英¹, 刘冰²

^1.信息工程大学地理空间信息学院，河南　郑州　450001
^2.信息工程大学数据与目标工程学院，河南　郑州　450001

收稿日期:2025-07-17 修回日期:2025-11-13 出版日期:2026-01-15 发布日期:2026-01-15
通讯作者: 冯凡 E-mail:zhangjrs0802@163.com;fengrs1991@163.com
作者简介:张津（1994—），女，博士生，主要研究方向为深度学习、遥感图像分类。　E-mail：zhangjrs0802@163.com
基金资助:
国家自然科学基金(42071340);嵩山实验室项目（纳入河南省重大科技专项管理体系）(221100211000-04)

Small-sample classification of hyperspectral images based on mixed CNN-ViT feature optimization

Jin ZHANG¹(), Fan FENG¹(), Chenguang DAI¹, Zhenchao ZHANG¹, Ying YU¹, Bing LIU²

^1.Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
^2.Institute of Data and Target Engineering, Information Engineering University, Zhengzhou 450001, China

Received:2025-07-17 Revised:2025-11-13 Online:2026-01-15 Published:2026-01-15
Contact: Fan FENG E-mail:zhangjrs0802@163.com;fengrs1991@163.com
About author:ZHANG Jin (1994—), female, PhD candidate, majors in deep learning and remote sensing image classification. E-mail: zhangjrs0802@163.com
Supported by:
The National Natural Science Foundation of China(42071340);Program of Song Shan Laboratory (Included in the management of Major Science and Technology Program of Henan Province)(221100211000-04)

摘要/Abstract

摘要：

高光谱图像分类是实现地物要素精细识别的关键技术。随着成像技术发展，无人机平台获取的高光谱图像空间分辨率不断提升，给地物精细分类带来了新的机遇和挑战。现有深层网络在小样本条件下对高空间分辨率高光谱图像特征学习不全面。针对以上问题，本文提出了一种针对卷积神经网络（CNN）和ViT混合特征的优化方法，包括自适应空谱特征学习、双向特征整合和多段特征交互增强3个方面。首先，将多尺度3D空谱特征和局部2D自注意力特征纳入级联残差结构，完成全局-局部多尺度空谱特征提取，增强特征的丰富性。然后，从两个方向整合空间特征和通道特征，提取两个维度的相关性，实现对CNN和ViT提取特征的补充和增强。将上述多段特征融合后，输入分解二阶池化层，解决多段特征之间差异大、缺乏交互的问题。最后，将细粒度融合特征输入全连接层，完成分类。在3个高空间分辨率高光谱图像数据集LongKou、HanChuan、HongHu上进行了小样本分类试验。每类地物仅使用5个样本训练模型，本文方法分类精度分别为94.00%、83.24%和87.63%，验证了本文方法在小样本条件下的有效性。

关键词: 高光谱图像分类, 混合卷积网络, 局部自注意力, 分解二阶池化, 多特征优化, 小样本

Abstract:

Hyperspectral image classification is a key technology for achieving fine-grained recognition of ground objects. With the advancement of imaging technology, the spatial resolution of hyperspectral images acquired by UAV platforms has significantly improved, bringing new opportunities and challenges to fine-grained land cover classification. However, existing deep neural networks still exhibit insufficiently comprehensive feature learning for high spatial-resolution hyperspectral images under small-sample conditions. To address this issue, this paper proposes a mixed feature optimization method of convolutional neural networks (CNN) and vision Transformer (ViT), including three aspects: adaptive spatial-spectral feature learning, bidirectional feature integration and multi-segment feature interaction enhancement. First, multi-scale 3D spatial-spectral features and 2D local selfattention features are incorporated into a cascaded residual structure to achieve global-local multi-scale spatial-spectral feature extraction, enhancing the feature richness. Then, spatial and channel features are integrated from two directions to extract correlations across both dimensions, thereby complementing and enhancing the features extracted by CNN and ViT. After fusing these multi-stage features, they are fed into a factorized second order pooling layer to address the issues of large discrepancies and insufficient interaction among multi-stage features. Finally, the fine-grained fused features are input into a fully connected layer for classification. Small-sample classification experiments were conducted on three hyperspectral image datasets with high spatial resolution, namely LongKou, HanChuan, and HongHu. Only five samples per land-cover class are used for model training. The proposed method achieved classification accuracies of 94.00%, 83.24%, and 87.63%, respectively, demonstrating its effectiveness under small-sample conditions.

Key words: hyperspectral image classification, mixed convolutional network, local selfattention, factorized second order pooling, multi-feature optimization, small sample

中图分类号:

P237

张津, 冯凡, 戴晨光, 张振超, 于英, 刘冰. 基于CNN-ViT混合特征优化的小样本高光谱图像分类[J]. 测绘学报, 2025, 54(12): 2233-2246.

Jin ZHANG, Fan FENG, Chenguang DAI, Zhenchao ZHANG, Ying YU, Bing LIU. Small-sample classification of hyperspectral images based on mixed CNN-ViT feature optimization[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(12): 2233-2246.

图/表 20

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参考文献 30

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类别	模块名	输出维度	核尺寸	个数
模型	Input	15×15×16
输入	Conv3D-BN-ReLU	15×15×16，24	3×3×3	24
3D空谱特征学习-1	MultiRes3D-1	15×15×16，24	1×1×7	24
	MultiRes3D-1	15×15×16，24	1×1×3	4，8，12
	LWSA-1	15×15×16，24
	Add-1	15×15×16，24
	Reshape-1	15×15×384
3D空谱特征学习-2	MultiRes3D-2	15×15×16，24	7×7×1	24
	MultiRes3D-2	15×15×16，24	3×3×1	4，8，12
	LWSA-2	15×15×16，24
	Add-2	15×15×16，24
	Reshape-2	15×15×384
2D重组特征整合	2D-Mixer	225×128
	Reshape-3	15×15×128
	Concatenate	15×15×896
多段特征交互	FSOP	2664
模型	Flatten	2664
输出	FC-Softmax	地物类别数

项目	LK数据集	HC数据集	HH数据集
图像大小/像素	550×440	1217×303	940×475
光谱范围/μm	0.4~1.0	0.4~1.0	0.4~1.0
波段数	270	274	270
空间分辨率/m	0.463	0.109	0.043
类别数	9	16	22
标记样本数	204 542	257 530	386 693

序号	地物名称	标记样本数	训练样本数	测试样本数
1	玉米	34 511	5	34 506
2	棉花	8374	5	8369
3	芝麻	3031	5	3026
4	宽叶大豆	63 212	5	63 207
5	窄叶大豆	4151	5	4146
6	水稻	11 854	5	11 849
7	水体	67 056	5	67 051
8	道路和房屋	7124	5	7119
9	混合杂草	5229	5	5224
	总数	204 542	45	204 497

序号	地物名称	标记样本数	训练样本数	测试样本数
1	草莓	44 735	5	44 730
2	豇豆	22 753	5	22 748
3	大豆	10 287	5	10 282
4	高粱	5353	5	5348
5	空心菜	1200	5	1195
6	西瓜	4533	5	4528
7	绿色植物	5903	5	5898
8	树	17 978	5	17 973
9	草	9469	5	9464
10	红色屋顶	10 516	5	10 511
11	灰色屋顶	16 911	5	16 906
12	塑料	3679	5	3674
13	裸土	9116	5	9111
14	道路	18 560	5	18 555
15	明亮物体	1136	5	1131
16	水	75 401	5	75 396
	总数	257 530	80	257 450

序号	地物名称	标记样本数	训练样本数	测试样本数
1	红色屋顶	14 041	5	14 036
2	路	3512	5	3507
3	裸土	21 821	5	21 816
4	棉花	163 285	5	163 280
5	棉花柴	6218	5	6213
6	油菜	44 557	5	44 552
7	白菜	24 103	5	24 098
8	小白菜	4054	5	4049
9	卷心菜	10 819	5	10 814
10	榨菜	12 394	5	12 389
11	菜心	11 015	5	11 010
12	青菜	8954	5	8949
13	上海青	22 507	5	22 502
14	莴苣	7356	5	7351
15	莴笋	1002	5	997
16	生菜	7262	5	7257
17	罗马生菜	3010	5	3005
18	胡萝卜	3217	5	3212
19	白萝卜	8712	5	8707
20	蒜薹	3486	5	3481
21	蚕豆	1328	5	1323
22	柿子树	4040	5	4035
	总数	386 693	110	386 583

基于CNN-ViT混合特征优化的小样本高光谱图像分类

Small-sample classification of hyperspectral images based on mixed CNN-ViT feature optimization

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图/表 20

参考文献 30

相关文章 5

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类别	DGPF^[23]	A2S2K^[7]	LAMFN^[8]	SGUMLP^[19]	Swin-HSI^[24]	SSFTT^[10]	LWCT
1	96.07	96.07	96.71	92.53	88.66	94.00	97.38
2	92.31	93.13	93.87	90.00	87.81	90.43	90.33
3	95.62	93.71	96.79	97.99	96.91	99.00	97.80
4	75.89	85.71	88.11	76.73	73.53	72.79	92.36
5	87.20	83.69	98.15	87.08	89.45	92.07	98.64
6	94.18	97.74	95.40	94.67	93.10	96.44	97.40
7	95.21	99.38	95.73	93.58	88.40	95.73	96.41
8	70.85	74.53	69.48	72.96	61.78	72.55	69.98
9	85.44	82.07	85.97	81.70	76.36	85.59	85.58
Kappa系数	84.68±4.78	90.38±3.65	90.16±3.46	83.56±3.93	78.75±6.86	83.58±4.36	92.23±2.84
OA	87.95±3.90	92.53±2.92	92.35±2.75	87.02±3.27	83.01±5.90	87.08±3.66	94.00±2.23
AA	88.08±2.71	89.56±3.47	91.13±2.92	87.47±2.16	84.00±3.04	88.73±2.02	91.77±2.86

类别	DGPF^[23]	A2S2K^[7]	LAMFN^[8]	SGUMLP^[19]	Swin-HSI^[24]	SSFTT^[10]	LWCT
1	70.82	57.35	77.80	50.87	80.11	65.65	79.02
2	57.48	41.02	68.86	48.44	57.38	58.80	69.31
3	80.32	35.75	88.75	62.57	78.97	66.94	90.25
4	94.82	59.70	99.01	96.85	96.17	94.29	98.01
5	89.25	60.08	97.46	80.55	96.45	81.51	97.89
6	47.86	34.02	72.79	43.14	71.11	45.56	72.69
7	91.23	75.29	93.68	76.91	87.96	88.12	92.52
8	37.72	34.75	60.80	42.95	59.03	51.71	59.53
9	44.19	35.95	72.65	45.28	64.68	62.35	70.85
10	74.12	40.91	91.92	88.60	83.64	81.12	94.77
11	68.77	46.86	84.12	68.97	82.65	72.41	85.59
12	67.60	34.12	89.29	51.91	80.36	68.97	88.28
13	49.52	27.22	63.00	35.93	45.63	45.78	62.12
14	55.31	42.38	75.49	46.22	56.65	56.67	76.89
15	67.63	61.44	86.35	70.46	72.61	75.76	83.19
16	89.05	81.26	96.52	89.63	93.17	90.22	96.56
Kappa系数	66.41±4.74	49.55±9.39	80.16±3.23	60.34±3.80	74.17±2.92	67.63±3.90	80.60±3.03
OA	70.79±4.39	55.93±8.61	82.86±2.87	65.23±3.54	77.64±2.62	71.84±3.76	83.24±2.70
AA	67.85±3.00	48.01±8.42	82.41±1.81	62.46±2.48	75.41±1.63	69.12±2.16	82.34±1.48

类别	DGPF^[23]	A2S2K^[7]	LAMFN^[8]	SGUMLP^[19]	Swin-HSI^[24]	SSFTT^[10]	LWCT
1	72.59	76.81	81.76	78.79	79.34	78.32	82.62
2	68.39	78.85	75.34	62.97	68.56	69.77	78.91
3	69.90	79.81	84.00	68.73	80.70	83.26	84.80
4	86.66	74.38	94.61	72.93	94.66	81.55	95.67
5	87.84	70.64	92.09	68.03	93.01	76.56	93.07
6	87.61	87.79	92.07	88.44	90.92	87.19	91.66
7	41.51	47.81	63.32	45.86	52.70	44.06	61.95
8	43.83	39.05	65.32	64.34	73.82	59.32	63.58
9	88.30	92.08	93.62	92.09	87.67	91.41	92.77
10	34.83	66.03	84.43	35.48	59.49	43.18	85.15
11	47.82	44.94	72.78	28.27	54.31	38.69	74.28
12	36.24	57.66	67.63	37.20	54.63	43.32	69.38
13	39.73	51.81	61.65	36.15	49.96	45.72	64.54
14	54.69	69.88	84.31	71.30	77.84	72.87	83.35
15	96.72	92.55	99.22	92.82	95.40	98.07	98.93
16	80.63	87.71	97.19	73.76	89.62	89.38	96.92
17	72.51	71.08	83.90	71.46	76.78	79.62	83.71
18	81.62	82.12	96.53	92.85	92.67	91.26	97.48
19	61.92	77.36	91.85	80.22	80.28	72.71	93.08
20	56.70	77.32	87.54	57.69	75.47	55.43	87.14
21	64.64	88.95	99.00	57.50	89.86	87.17	97.88
22	79.21	81.02	94.79	70.51	88.04	86.39	95.60
Kappa系数	67.50±4.23	67.14±4.33	83.72±1.94	62.01±5.69	78.38±2.60	68.49±4.07	84.53±0.96
OA	73.45±3.99	72.43±4.16	86.95±1.63	67.98±5.69	82.64±2.22	74.00±3.71	87.63±0.82
AA	66.09±2.40	72.53±2.05	84.68±1.58	65.79±2.06	77.53±1.63	71.60±2.85	85.11±1.24

方法	LK数据集	HC数据集	HH数据集
方法a	93.20	82.80	87.14
方法b	93.57	82.35	87.62
方法c	93.79	82.78	87.39
本文方法	94.00	83.24	87.63

方法	训练时间/s	测试时间/s	FLOPs/M	参数量/K
DGPF^[23]	13.96	53.13	314.08	4 225.90
A2S2K^[7]	194.03	77.21	171.60	370.58
LAMFN^[8]	16.53	44.24	138.61	150.65
SGUMLP^[19]	5.03	41.38	18.60	254.95
Swin-HSI^[24]	5.59	56.60	9.65	101.54
SSFTT^[10]	3.34	18.78	11.40	148.03
LWCT	36.85	180.68	286.20	776.89

方法	LK数据集	HC数据集	HH数据集
基线方法	92.09±3.24	81.73±3.04	86.86±1.89
方法1	91.50±3.00	78.69±3.94	86.86±1.46
方法2	91.40±2.99	82.97±2.77	87.96±1.41
方法3	92.54±2.45	83.02±2.43	87.03±1.87
本文方法	94.00±2.23	83.24±2.70	87.63±0.82

方法	LK数据集					HC数据集					HH数据集
方法	5	10	15	20	25	5	10	15	20	25	5	10	15	20	25
DGPF^[23]	87.95	94.89	94.76	96.61	91.90	70.79	81.62	85.01	87.88	88.59	73.45	83.83	86.34	89.00	89.44
A2S2K^[7]	92.53	84.32	91.34	94.85	88.54	55.93	67.34	80.97	77.52	78.50	72.43	73.64	70.60	86.75	82.94
LAMFN^[8]	92.35	95.37	96.63	97.22	96.54	82.86	89.42	92.58	93.61	94.52	86.95	90.71	92.77	94.11	94.52
SGUMLP^[19]	87.02	92.29	94.16	94.97	95.92	65.23	72.90	77.65	80.74	81.61	67.98	76.36	79.71	81.82	82.41
Swin-HSI^[24]	83.01	88.82	91.61	92.33	93.62	77.64	83.79	86.37	88.16	88.37	82.64	86.48	87.44	89.15	90.19
SSFTT^[10]	87.08	94.15	95.03	96.41	96.77	71.84	81.50	85.96	85.96	86.34	74.00	81.55	81.62	87.87	88.98
LWCT	94.00	96.07	96.82	97.35	97.60	83.24	89.71	92.37	93.63	94.33	87.63	91.01	93.07	93.99	94.64

方法	LK	HC
DFSL^[16]	85.90	57.60
DCFSL^[27]	91.37	62.70
CTFSL^[28]	90.40	66.46
DBDA-FSL^[29]	92.30	67.17
DMVL^[30]	87.83	61.12
SS-MFN^[15]	92.97	82.31
LWCT	94.00	83.24

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