计算机工程与应用 ›› 2024, Vol. 60 ›› Issue (7): 192-203.DOI: 10.3778/j.issn.1002-8331.2211-0139
马亚美,王双亭,都伟冰
出版日期:
2024-04-01
发布日期:
2024-04-01
MA Yamei, WANG Shuangting, DU Weibing
Online:
2024-04-01
Published:
2024-04-01
摘要: 为改善高光谱图像小样本类别的分类性能,提高模型特征表达的稳健性,提出了双分支多维注意力特征融合的神经网络分类模型(DBMD)。DBMD采用两个分支分别进行光谱特征提取和混合特征提取。光谱分支通过密集连接的扩张卷积逐级提取特征,然后融合低、中、高级语义信息作为特征输出。混合分支采用3D-2D网络架构,并通过改进的Inception块提取空间尺度特征。此外,注意力机制分别应用于光谱、空间和空谱特征,进行特征细化,增强重要区域的特征响应。最后,将不同维度的细化特征联合输入至分类器进行分类。在Indian Pines和Salinas Valley数据集上利用5%和1%的样本进行实验,分别取得了98.40%和99.78%的总体精度,与其他六种网络架构相比,该模型在准确性和稳定性上都具有更优的表现。
马亚美, 王双亭, 都伟冰. 双分支多维注意特征融合的高光谱图像分类[J]. 计算机工程与应用, 2024, 60(7): 192-203.
MA Yamei, WANG Shuangting, DU Weibing. Hyperspectral Image Classification Based on Double Branch Multidimensional Attention Feature Fusion[J]. Computer Engineering and Applications, 2024, 60(7): 192-203.
[1] ACOSTA I C C, KHODADADZADEH M, TUSA L, et al. A machine learning framework for drill-core mineral mapping using hyperspectral and high-resolution mineralogical data fusion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(12): 4829-4842. [2] LU B, DAO P, LIU J G, et al. Recent advances of hyperspectral imaging technology and applications in agriculture[J]. Remote Sensing, 2020, 12(16): 2659. [3] 谢东津, 吕呈龙, 祖梅, 等. 绿色植被可见-近红外反射光谱模拟材料研究进展[J]. 光谱学与光谱分析, 2021, 41(4): 1032-1038. XIE D J, LV C L, ZU M, et al. Research progress of bionic materials simulating vegetation visible-near infrared reflectance spectra[J]. Spectroscopy and Spectral Analysis, 2021, 41(4): 1032-1038. [4] KARALAS K, TSAGKATAKIS G, ZERVAKIS M, et al. Land classification using remotely sensed data: going multilabel[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6): 3548-3563. [5] BIOUCAS-DIAS J M, PLAZA A, CAMPS-VALLS G, et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Geoscience and Remote Sensing Magazine, 2013, 1(2): 6-36. [6] 叶珍, 白璘, 何明一. 高光谱图像空谱特征提取综述[J]. 中国图象图形学报, 2021, 26(8): 1737-1763. YE Z, BAI L, HE M Y. Review of spatial-spectral feature extraction for hyperspectral image[J]. Journal of Image and Graphics, 2021, 26(8): 1737-1763. [7] 高伟, 李维良, 林妍. 面向高光谱影像分类的高性能计算及存储优化[J]. 计算机工程与应用. 2015, 51(16): 171-177. GAO W, LI W L, LIN Y. High performance computing and its storage optimization strategies oriented to hyperspectral image classification[J]. Computer Engineering and Applications, 2015, 51(16): 171-177. [8] MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790. [9] LI J, BIOUCAS-DIAS J M, PLAZA A. Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3): 809-823. [10] ZHAO J, JIANG Q. Probabilistic PCA for t distributions[J]. Neurocomputing, 2006, 69(16): 2217-2226. [11] KUN Z, LAI-WAN C. Dimension reduction as a deflation method in ICA[J]. IEEE Signal Processing Letters, 2006, 13(1): 45-48. [12] JIANG H, DONG Y. Dimension reduction based on a penalized kernel support vector machine model[J]. Knowledge-Based Systems, 2017, 138: 79-90. [13] LAOHAKIAT S, PHIMOLTARES S, LURSINSAP C. A clustering algorithm for stream data with LDA-based unsupervised localized dimension reduction[J]. Information Sciences, 2017, 381: 104-123. [14] 吴昊. 综合纹理特征的高光谱遥感图像分类方法[J]. 计算机工程与设计, 2012, 33(5): 1993-1996. WU H. Classification methodology combined with texture feature for hyperspectral remote sensing image[J]. Computer Engineering and Design, 2012, 33(5): 1993-1996. [15] MA L, MA A D, JU C, et al. Graph-based semi-supervised learning for spectral-spatial hyperspectral image classification[J]. Pattern Recognition Letters, 2016, 83: 133-142. [16] 李垒, 任越美. 基于随机森林的高光谱遥感图像分类[J]. 计算机工程与应用, 2016, 52(24): 189-193. LI L, REN Y M. Classification of hyperspectral data based on random forest[J]. Computer Engineering and Applications, 2016, 52(24): 189-193. [17] ZHANG X R, SONG Q, GAO Z Y, et al. Spectral-spatial feature learning using cluster-based group sparse coding for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(9): 4142-4159. [18] 安凤平, 李晓薇, 曹翔. 权重初始化-滑动窗口CNN的医学图像分类[J]. 计算机科学与探索, 2022, 16(8): 1885-1897. AN F P, LI X W, CAO X. Medical image classification algorithm based on weight initialization-sliding window CNN[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(8): 1885-1897. [19] 朱炳宇, 刘朕, 张景祥. 融合Grad-CAM和卷积神经网络的COVID-19检测算法[J]. 计算机科学与探索, 2022, 16(9): 2108-2120. ZHU B Y, LIU Z, ZHANG J X. COVID-19 detection algorithm combining Grad-CAM and convolutional neural network[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(9): 2108-2120. [20] AL-GARADI M A, YANG Y C, SARKER A. The role of natural language processing during the COVID-19 pandemic: health applications, opportunities, and challenges[J]. Healthcare, 2022, 10(11): 2270. [21] 焦磊, 云静, 刘利民, 等. 封闭域深度学习事件抽取方法研究综述[J]. 计算机科学与探索, 2023, 17(3): 533-548. JIAO L, YUN J, LIU L M, et al. Overview of closed-domain deep learning event extraction methods[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(3): 533-548. [22] CHEN Y S, LIN Z H, ZHAO X, et al. Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2094-2107. [23] LI T, ZHANG J P, ZHANG Y. Classification of hyperspectral image based on deep belief networks[C]//IEEE International Conference on Image Processing (ICIP), 2014: 5132-5136. [24] HU W, HUANG Y Y, LI W, et al. Deep convolutional neural networks for hyperspectral image classification[J]. Journal of Sensors, 2015: 1-12. [25] MOU L C, GHAMISI P, ZHU X X. Deep recurrent neural networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(7): 3639-3655. [26] HE Z, LIU H, WANG Y W, et al. Generative adversarial networks-based semi-supervised learning for hyperspectral image classification[J]. Remote Sensing, 2017, 9(10): 1042. [27] CHEN Y S, JIANG H L, LI C Y, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6232-6251. [28] LEE H, KWON H. Going deeper with contextual CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 2017, 26(10): 4843-4855. [29] ZHONG Z L, LI J, LUO Z M, et al. Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 847-858. [30] ROY S K, KRISHNA G, DUBEY S R, et al. HybridSN: exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(2): 277-281. [31] LI Z K, WANG T N, LI W, et al. Deep multilayer fusion dense network for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 1258-1270. [32] 魏祥坡, 余旭初, 管凌霄. 利用残差通道注意力网络的高光谱图像分类[J]. 测绘科学技术学报, 2019, 36(2): 161-166. WEI X P, YU X C, GUAN L X. Hyperspectral image classification using residual channel attention network[J]. Journal of Geomatics Science and Technology, 2019, 36(2): 161-166. [33] MA W P, YANG Q F, WU Y, et al. Double-branch multi-attention mechanism network for hyperspectral image classification[J]. Remote Sensing, 2019, 11(11): 1307. [34] LI W, WU G D, ZHANG F, et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 844-853. [35] LI W, CHEN C, ZHANG M M, et al. Data augmentation for hyperspectral image classification with deep CNN[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 16(4): 593-597. [36] ZHU L, CHEN Y S, GHAMISI P, et al. Generative adversarial networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(9): 5046-5063. [37] FANG B, LI Y, ZHANG H K, et al. Collaborative learning of lightweight convolutional neural network and deep clustering for hyperspectral image semi-supervised classification with limited training samples[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 161: 164-178. [38] WANG Q Y, CHEN M, ZHANG J P, et al. Improved active deep learning for semi-supervised classification of hyperspectral image[J]. Remote Sensing, 2022, 14(1): 171. [39] SUN J G, WANG W L, WEI X, et al. Deep clustering with intraclass distance constraint for hyperspectral images[J]. IEEE Transactions on Geoscience and Remote sensing, 2021, 59(5): 4135-4149. [40] 石延新, 何进荣, 李照奎, 等. 3D卷积自编码器高光谱图像分类模型[J]. 中国图象图形学报, 2021, 26(8): 2021-2036. SHI Y X, HE J R, LI Z K, et al. Hyperspectral image classification model based on 3D convolutional auto-encoder[J]. Journal of Image and Graphics, 2021, 26(8): 2021-2036. [41] FENG F, ZHANG Y S, ZHANG J, et al. Small sample hyperspectral image classification based on cascade fusion of mixed spatial-spectral features and second-order pooling[J]. Remote Sensing, 2022, 14(3): 505. [42] LIU J X, ZHANG K F, WU S Q, et al. An investigation of a multidimensional CNN combined with an attention mechanism model to resolve small-sample problems in hyperspectral image classification[J]. Remote Sensing, 2022, 14(3): 785. [43] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision & Pattern Recognition, Las Vegas, NV, USA, 2016: 770-778. [44] HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017: 2261-2269. [45] YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions[C]//Proceedings of International Conference on Learning Representation (ICLR), San Diego, CA, USA, 2016: 7-9. [46] SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015: 1-9. [47] HOWARD A, ZHU M L, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[J]. arXiv:1704.04861, 2017. [48] DONG H W, ZHANG L M, ZOU B. Band attention convolutional networks for hyperspectral image classification[J]. arXiv:1906.04379, 2019. [49] SONG W W, LI S T, FANG L Y, et al. Hyperspectral image classification with deep feature fusion network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6): 3173-3184. [50] WANG W J, DOU S G, JIANG Z M, et al. A fast dense spectral-spatial convolution network framework for hyperspectral images classification[J]. Remote Sensing, 2018, 10(7): 1068. [51] LI Z K, ZHAO X D, XU Y M, et al. Hyperspectral image classification with multiattention fusion network[J]. IEEE Geoscience and Remote Sensing Letters, 2020: 1-5. |
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