面向多源遥感数据分类的尺度自适应融合网络

刘晓敏; 余梦君; 乔振壮; 王浩宇; 邢长达

doi:10.11999/JEIT240178

面向多源遥感数据分类的尺度自适应融合网络

doi: 10.11999/JEIT240178

中国矿业大学信息与控制工程学院徐州 221116

基金项目: 国家自然科学基金(62303468, 62303469)，江苏省自然科学基金(BK20221116, BK20221112)，中国博士后科学基金(2023M733757)，江苏省卓越博士后计划(2022ZB530)

详细信息

作者简介:
刘晓敏：女，硕士生导师，研究方向为强化学习、多模态融合、图像处理

余梦君：男，硕士生，研究方向为深度强化学习、模式识别

乔振壮：男，硕士生，研究方向为高光谱图像分类、深度学习

王浩宇：男，助理研究员，研究方向为高光谱图像分类、多模态融合、机器学习

邢长达：男，副教授，研究方向是高光谱图像智能分析

通讯作者:
王浩宇　wanghaoyucumt@163.com

中图分类号: TN911.73; TP18
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- 被引次数: 0
出版历程
- 收稿日期: 2024-03-15
- 修回日期: 2024-06-28
- 网络出版日期: 2024-07-06
- 刊出日期: 2024-09-26

Scale Adaptive Fusion Network for Multimodal Remote Sensing Data Classification

School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China

Funds: The National Natural Science Foundation of China (62303468, 62303469), The Natural Science Foundation of Jiangsu Province (BK20221116, BK20221112), China Postdoctoral Science Foundation (2023M733757), The Excellent Post Doctorate Program of Jiangsu Province (2022ZB530)

摘要

摘要: 多模态融合方法能够利用不同模态的互补特性有效提升地物分类的准确性，近年来成为各领域的研究热点。现有多模态融合方法被成功应用于面向高光谱图像(HSI)和激光雷达(LiDAR)的联合分类任务。然而，现有的研究仍面临许多挑战，包括地物间空间依赖关系难捕获，多模态数据中判别性信息难获取等。为应对上述挑战，该文将多模态、多尺度、多视角特征融合整合到一个统一的框架中，提出一种尺度自适应融合网络(SAFN)。首先，提出动态多尺度图模块以捕获地物复杂的空间依赖关系，提升模型对不规则地物以及尺度迥异地物的适应能力。其次，基于激光雷达和高光谱图像的互补特性，约束同一空间近邻区域内的地物具有相近的特征表示，获取判别性遥感特征。然后，提出多模态空-谱融合模块，建立多模态、多尺度、多视角特征间的信息交互，捕获各特征间可共享的类辨识信息，为地物分类任务提供具有判别性的融合特征。最后，将融合特征输入分类器中得到类别概率得分，对地物类别进行预测。为验证方法的有效性，该文在3个数据集(Houston, Trento, MUUFL)上进行了实验。实验结果表明，与现有主流算法相比较，SAFN在多源遥感数据分类任务中取得了最佳的视觉效果和最高精度。
- 特征融合 /
- 高光谱图像 /
- 激光雷达 /
- 分类 /
- 图学习
Abstract: The multimodal fusion method can effectively improve the ground object classification accuracy by using the complementary characteristics of different modalities, which has become a research hotspot in various fields in recent years. The existing multimodal fusion methods have been successfully applied to multi-source remote sensing classification tasks oriented to HyperSpectral Image (HSI) and Light Detection And Ranging (LiDAR). However, existing research still faces many challenges, including difficulty in capturing spatial dependencies among irregular ground objects and obtaining discriminative information in multimodal data. To address the above challenges, a Scale Adaptive Fusion Network (SAFN)is proposed in this paper, by integrating the fusion of multimodal, multiscale, and multiview features into a unified framework. First, a dynamic multiscale graph module is proposed to capture the complex spatial dependencies of ground object, enhancing the model’s adaptability to irregular and scale-dissimilar ground object. Second, the complementary properties of LiDAR and HSI are utilized to constrain ground object within the same spatial neighborhood to have similar feature representations, thereby acquiring discriminative remote sensing features. Then, a multimodal spatial-spectral graph fusion module is proposed to establish feature interactions among multimodal, multiscale, and multiview features, providing discriminative fusion features for classification tasks by capturing class-recognition information that can be shared among features. Finally, the fusion features are fed into a classifier to obtain class probability scores for predicting the ground object class. To verify the effectiveness of SAFN, experiments are conducted on three datasets (i.e., Houston, Trento, and MUUFL). The experimental results show that, SAFN achieved state-of-the-art performance in multi-source remote sensing data classification tasks when compared with existing mainstream methods.
- Feature fusion /
- HyperSpectral Image(HSI) /
- Light Detection And Ranging(LiDAR) /
- Classification /
- Graph learning

HTML全文

图 1 SAFN流程图

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图 2 使用不同算法获得的Trento数据的t-SNE图

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图 3 各消融模型的t-SNE图

下载: 全尺寸图片幻灯片

表 1 Houston数据集分类精度(%)

类别	CNN	HRWN	EndNet	CCR-Net	FGCN	CNN-DF-S	MEDFN	SAFN
Healthy grass	98.62	89.60	99.35	97.48	95.42	92.20	94.96	81.48
Stressed grass	94.41	97.08	99.51	86.14	92.11	99.35	83.47	97.25
Synthetic grass	94.39	99.85	99.85	97.49	98.92	98.23	97.49	99.71
Trees	98.20	92.08	90.28	99.10	96.39	98.20	96.49	97.30
Soil	99.02	99.51	97.71	99.67	99.74	99.76	98.53	97.71
Water	82.62	90.49	95.74	96.72	94.18	93.44	98.69	85.57
Residential	69.39	79.41	83.89	89.58	78.90	77.89	86.22	94.15
Commercial	67.40	79.49	51.63	69.85	80.74	75.49	83.99	89.30
Road	74.76	53.90	73.54	96.40	76.37	64.69	76.22	86.04
Highway	79.79	86.00	87.57	88.57	87.94	90.89	94.12	87.99
Railway	75.89	64.28	84.77	80.82	83.38	77.04	89.71	85.93
Parking lot 1	62.41	77.41	73.54	81.62	78.87	85.24	94.23	95.30
Parking lot 2	83.30	85.52	85.75	86.86	83.05	93.32	95.10	99.56
Tennis court	98.78	99.02	100	99.27	98.41	99.76	99.51	99.76
Running track	97.50	99.06	99.38	96.41	98.03	100	98.28	95.00
OA	83.75	84.22	86.30	87.79	88.32	87.98	91.11	92.17
AA	85.09	86.18	88.17	89.27	89.50	89.70	92.47	92.80
Kappa	82.45	82.93	86.21	86.71	87.36	87.00	90.39	91.54

下载: 导出CSV

表 2 Trento数据集分类精度(%)

类别	CNN	HRWN	EndNet	CCR-Net	FGCN	CNN-DF-S	MEDFN	SAFN
Apple trees	95.04	96.01	95.37	96.11	97.09	98.96	97.78	99.53
Buildings	77.77	81.93	94.03	96.53	92.02	92.08	97.82	97.85
Ground	97.82	100	99.78	96.95	97.82	99.47	99.13	96.30
Woods	99.78	99.50	99.53	99.96	99.98	99.57	99.97	99.90
Vineyard	98.57	98.51	99.04	99.74	99.95	98.57	99.96	99.46
Roads	79.04	86.21	81.20	81.26	87.83	90.05	94.07	97.78
OA	94.41	95.62	96.35	97.03	97.51	97.44	98.84	99.22
AA	91.34	93.69	94.82	59.09	95.78	96.45	98.12	98.47
Kappa	92.56	94.16	95.15	96.04	96.68	96.57	98.45	98.96

下载: 导出CSV

表 3 MUUFL数据集分类精度(%)

类别	CNN	HRWN	EndNet	CCR-Net	FGCN	CNN-DF-S	MEDFN	SAFN
Trees	81.82	81.85	80.01	83.16	82.39	83.63	80.94	82.63
Mostly grass	65.47	61.94	80.90	71.52	76.71	76.19	76.33	84.99
Mixed ground surface	55.96	59.40	57.51	58.18	67.20	50.25	71.31	70.72
Dirt and sand	78.78	79.90	79.62	86.54	87.22	72.81	90.77	86.93
Road	75.76	71.21	79.49	72.79	76.96	81.87	85.63	86.85
Water	98.32	98.08	92.55	98.56	97.84	96.86	91.83	98.21
Building shadow	82.50	87.36	84.52	78.24	77.74	79.49	82.68	92.23
Building	72.71	77.16	71.65	80.24	76.70	84.57	83.93	87.25
Sidewalk	45.62	63.52	61.65	67.34	64.49	59.41	77.90	73.33
Yellow curb	57.14	66.92	77.44	77.44	81.20	64.42	88.72	96.93
Cloth panels	98.17	96.35	95.43	99.09	99.54	90.76	99.54	93.57
OA	74.52	75.36	76.25	77.07	78.35	77.57	80.77	82.88
AA	73.81	76.70	78.43	79.37	80.73	76.39	84.51	86.70
Kappa	67.63	68.63	69.90	70.87	72.39	71.49	75.57	78.19

下载: 导出CSV

表 4 不同组件对总体精度的影响(%)

数据集	SAFN-A	SAFN-B	SAFN-C	SAFN
Houston 2013	87.89	89.23	90.35	92.17
Trento	96.73	97.54	98.48	99.22
MUUFL	78.13	80.15	81.76	82.88

下载: 导出CSV

参考文献(31)

[1]	WANG Leiquan, ZHU Tongchuan, KUMAR N, et al. Attentive-adaptive network for hyperspectral images classification with noisy labels[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5505514. doi: 10.1109/TGRS.2023.3254159.
[2]	HANG Renlong, LI Zhu, GHAMISI P, et al. Classification of hyperspectral and LiDAR data using coupled CNNs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4939–4950. doi: 10.1109/TGRS.2020.2969024.
[3]	王成龙, 赵倩, 赵琰, 等. 基于深度可分离卷积的实时遥感目标检测算法[J]. 电光与控制, 2022, 29(8): 45–49. doi: 10.3969/j.issn.1671-637X.2022.08.009. WANG Chenglong, ZHAO Qian, ZHAO Yan, et al. A real-time remote sensing target detection algorithm based on depth separable convolution[J]. Electronics Optics & Control, 2022, 29(8): 45–49. doi: 10.3969/j.issn.1671-637X.2022.08.009.
[4]	AHMAD M, KHAN A M, MAZZARA M, et al. Multi-layer extreme learning machine-based autoencoder for hyperspectral image classification[C]. The 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Prague, Czech, 2019: 75–82. doi: 10.5220/0007258000750082.
[5]	CUI Ying, SHAO Chao, LUO Li, et al. Center weighted convolution and GraphSAGE cooperative network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5508216. doi: 10.1109/TGRS.2023.3264653.
[6]	LI Mingsong, LI Wei, LIU Yikun, et al. Adaptive mask sampling and manifold to Euclidean subspace learning with distance covariance representation for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5508518. doi: 10.1109/TGRS.2023.3265388.
[7]	OU Xianfeng, WU Meng, TU Bing, et al. Multi-objective unsupervised band selection method for hyperspectral images classification[J]. IEEE Transactions on Image Processing, 2023, 32: 1952–1965. doi: 10.1109/TIP.2023.3258739.
[8]	XUE Zhixiang, TAN Xiong, YU Xuchu, et al. Deep hierarchical vision transformer for hyperspectral and LiDAR data classification[J]. IEEE Transactions on Image Processing, 2022, 31: 3095–3110. doi: 10.1109/TIP.2022.3162964.
[9]	赵伍迪, 李山山, 李安, 等. 结合深度学习的高光谱与多源遥感数据融合分类[J]. 遥感学报, 2021, 25(7): 1489–1502. doi: 10.11834/jrs.20219117. ZHAO Wudi, LI Shanshan, LI An, et al. Deep fusion of hyperspectral images and multi-source remote sensing data for classification with convolutional neural network[J]. National Remote Sensing Bulletin, 2021, 25(7): 1489–1502. doi: 10.11834/jrs.20219117.
[10]	ZHAO Xudong, ZHANG Mengmeng, TAO Ran, et al. Fractional Fourier image transformer for multimodal remote sensing data classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(2): 2314–2326. doi: 10.1109/TNNLS.2022.3189994.
[11]	ROY S K, DERIA A, HONG Danfeng, et al. Hyperspectral and LiDAR data classification using joint CNNs and morphological feature learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5530416. doi: 10.1109/TGRS.2022.3177633.
[12]	孙强, 陈远. 多层次时空特征自适应集成与特有-共享特征融合的双模态情感识别[J]. 电子与信息学报, 2024, 46(2): 574–587. doi: 10.11999/JEIT231110. SUN Qiang and CHEN Yuan. Bimodal emotion recognition with adaptive integration of multi-level spatial-temporal features and specific-shared feature fusion[J]. Journal of Electronics & Information Technology. 2024, 46(2): 574–587. doi: 10.11999/JEIT231110.
[13]	雷大江, 杜加浩, 张莉萍, 等. 联合多流融合和多尺度学习的卷积神经网络遥感图像融合方法[J]. 电子与信息学报, 2022, 44(1): 237–244. doi: 10.11999/JEIT200792. LEI Dajiang, DU Jiahao, ZHANG Liping, et al. Multi-stream architecture and multi-scale convolutional neural network for remote sensing image fusion[J]. Journal of Electronics & Information Technology, 2022, 44(1): 237–244. doi: 10.11999/JEIT200792.
[14]	JIA Sen, ZHAN Zhangwei, ZHANG Meng, et al. Multiple feature-based superpixel-level decision fusion for hyperspectral and LiDAR data classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(2): 1437–1452. doi: 10.1109/TGRS.2020.2996599.
[15]	ZHAO Guangrui, YE Qiaolin, SUN Le, et al. Joint classification of hyperspectral and LiDAR data using a hierarchical CNN and transformer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5500716. doi: 10.1109/TGRS.2022.3232498.
[16]	LI Hengchao, HU Wenshuai, LI Wei, et al. A³ CLNN: Spatial, spectral and multiscale attention ConvLSTM neural network for multisource remote sensing data classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(2): 747–761. doi: 10.1109/TNNLS.2020.3028945.
[17]	ZHANG Mengmeng, LI Wei, ZHANG Yuxiang, et al. Hyperspectral and LiDAR data classification based on structural optimization transmission[J]. IEEE Transactions on Cybernetics, 2023, 53(5): 3153–3164. doi: 10.1109/TCYB.2022.3169773.
[18]	LI Jiaojiao, MA Yinle, SONG Rui, et al. A triplet semisupervised deep network for fusion classification of hyperspectral and LiDAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5540513. doi: 10.1109/TGRS.2022.3213513.
[19]	DONG Wenqian, ZHANG Tian, QU Jiahui, et al. Multibranch feature fusion network with self- and cross-guided attention for hyperspectral and LiDAR classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5530612. doi: 10.1109/TGRS.2022.3179737.
[20]	马梁, 苟于涛, 雷涛, 等. 基于多尺度特征融合的遥感图像小目标检测[J]. 光电工程, 2022, 49(4): 210363. doi: 10.12086/oee.2022.210363. MA Liang, GOU Yutao, LEI Tao, et al. Small object detection based on multi-scale feature fusion using remote sensing images[J]. Opto-Electronic Engineering, 2022, 49(4): 210363. doi: 10.12086/oee.2022.210363.
[21]	ZHANG Zhongqiang, LIU Danhua, GAO Dahua, et al. A novel spectral-spatial multi-scale network for hyperspectral image classification with the Res2Net block[J]. International Journal of Remote Sensing, 2022, 43(3): 751–777. doi: 10.1080/01431161.2021.2005840.
[22]	XU Kejie, ZHAO Yue, ZHANG Lingming, et al. Spectral–spatial residual graph attention network for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 5509305. doi: 10.1109/LGRS.2021.3111985.
[23]	TAN Xiong and XUE Zhixiang. Spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification[J]. European Journal of Remote Sensing, 2022, 55(1): 409–419. doi: 10.1080/22797254.2022.2087540.
[24]	HONG Danfeng, HAN Zhu, YAO Jing, et al. SpectralFormer: Rethinking hyperspectral Image classification with transformers[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5518615. doi: 10.1109/TGRS.2021.3130716.
[25]	HAMILTON W L, YING R, and LESKOVEC J. Inductive representation learning on large graphs[C]. The 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 1025–1035.
[26]	YU Haoyang, ZHANG Hao, LIU Yao, et al. Dual-channel convolution network with image-based global learning framework for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 6005705. doi: 10.1109/LGRS.2021.3139358.
[27]	ZHAO Xudong, TAO Ran, LI Wei, et al. Joint classification of hyperspectral and LiDAR data using hierarchical random walk and deep CNN architecture[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(10): 7355–7370. doi: 10.1109/TGRS.2020.2982064.
[28]	HONG Danfeng, GAO Lianru, HANG Renlong, et al. Deep encoder–decoder networks for classification of hyperspectral and LiDAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 5500205. doi: 10.1109/LGRS.2020.3017414.
[29]	WU Xin, HONG Danfeng, and CHANUSSOT J. Convolutional neural networks for multimodal remote sensing data classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5517010. doi: 10.1109/TGRS.2021.3124913.
[30]	ZHAO Xudong, TAO Ran, LI Wei, et al. Fractional Gabor convolutional network for multisource remote sensing data classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5503818. doi: 10.1109/TGRS.2021.3065507.
[31]	WANG Haoyu, CHENG Yuhu, LIU Xiaomin, et al. Reinforcement learning based Markov edge decoupled fusion network for fusion classification of hyperspectral and LiDAR[J]. IEEE Transactions on Multimedia, 2024, 26: 7174–7187. doi: 10.1109/TMM.2024.3360717.