计算机科学 ›› 2018, Vol. 45 ›› Issue (12): 243-250.doi: 10.11896/j.issn.1002-137X.2018.12.040
任守纲1, 万升1, 顾兴健1, 王浩云1, 袁培森1, 徐焕良1,2
REN Shou-gang1, WAN Sheng1, GU Xing-jian1, WANG Hao-yun1, YUAN Pei-sen1, XU Huan-liang1,2
摘要: 为了应对高光谱图像同质区域面积分布不均的问题,同时更充分地挖掘空间和光谱信息之间的内在联系,提出了一种基于多尺度空谱鉴别特征的高光谱图像分类方法。该算法首先对图像进行不同尺度的滤波操作,接着分别从得到的多幅图像中提取鉴别的空谱特征,并使用支持向量机(SVM)进行分类。最后,该算法采取“决策级融合”的策略,来综合不同滤波尺度图像的分类结果。在Indian Pines,Kennedy Space Center和University of Pavia数据集上的实验表明,该算法能够提取较为有效的空间信息,当随机选取10%的像素作为训练样本时,该算法的总体分类准确率均能达到96%以上,其分类精度和Kappa系数均优于其他分类算法。
中图分类号:
[1]MIYOSHI G T,IMAI N N,TOMMASELLI A M G,et al.Radiometric block adjustment of hyperspectral image blocks in the Brazilian environment[J].International Journal of Remote Sen-sing,2018,39(15-16):1-21. [2]ZHANG Y X,GAO X Y,WANG T,et al.Background self-learning framework for bio information extraction from hyperp-sectral images [C]∥2014 academic annual meeting of Hubei Computer Society.2014:292-296.(in Chinese) 张玉香,高旭杨,王挺,等.一种基于背景自学习的高光谱图像生物信息提取方法[C]∥2014湖北省计算机学会学术年会.2014:292-296. [3]WEN S X,LI S W,JIN X,et al.Research on Anthrax Disease Classification of Dangshan Pear Based on Hyperspectral Imaging Technology [J].Computer Science,2017,44(s1):216-219.(in Chinese) 温淑娴,李绍稳,金秀,等.基于高光谱的砀山酥梨炭疽病害等级分类研究[J].计算机科学,2017,44(s1):216-219. [4]ZHANG X,PAN Z,LU X,et al.Hyperspectral image classification based on joint spectrum of spatial space and spectral space[J].Multimedia Tools & Applications,2018(3):1-19. [5]LI D,CHENG Y,WANG X,et al.Incremental Graph Embedding Based on Spatial-Spectral Neighbors for Hyperspectral Ima-ge Classification[J].IEEE Access,2018,6(99):10996-11006. [6]YANG L,WANG M,YANG S,et al.Hybrid ProbabilisticSparse Coding With Spatial Neighbor Tensor for Hyperspectral Imagery Classification[J].IEEE Transactions on Geoscience & Remote Sensing,2018,PP(99):1-12. [7]ZHONG Z,LI J.Generative Adversarial Networks and Probabilistic Graph Models for Hyperspectral Image Classification[OL].http://www.researchgate.net/publication/323076591_Generative_Adversarial_Networks_and_Probailistic_Graph_Models_for_Hyperspectral_Image_Classification. [8]ZHANG B,LI S,JIA X,et al.Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery[J].IEEE Geoscience & Remote Sensing Letters,2011,8(5):973-977. [9]LI W,PRASAD S,FOWLER J E.Hyperspectral Image Classification Using Gaussian Mixture Models and Markov Random Fields[J].Geoscience & Remote Sensing Letters IEEE,2014,11(1):153-157. [10]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 & Remote Sensing,2012,50(3):809-823. [11]XIA J,CHANUSSOT J,DU P,et al.Spectral─Spatial Classification for Hyperspectral Data Using Rotation Forests With Local Feature Extraction and Markov Random Fields[J].IEEE Transactions on Geoscience & Remote Sensing,2015,53(5):2532-2546. [12]IORDACHE M D,BIOUCAS-DIAS J M,PLAZA A.Total Varia-tion Spatial Regularization for Sparse Hyperspectral Unmixing[J].IEEE Transactions on Geoscience & Remote Sensing,2012,50(11):4484-4502. [13]HUANG X,GUAN X,BENEDIKTSSON J A,et al.MultipleMorphological Profiles From Multicomponent-Base Images for Hyperspectral Image Classification[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2014,7(12):4653-4669. [14]FU W,LI S,FANG L.Spectral-spatial hyperspectral image classification via superpixel merging and sparse representation[C]∥Geoscience and Remote Sensing Symposium.IEEE,2015:4971-4974. [15]WANG J,JIAO L,LIU H,et al.Hyperspectral Image Classification by Spatial-Spectral Derivative-Aided Kernel Joint Sparse Representation[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2015,8(6):1-16. [16]JI R,GAO Y,HONG R,et al.Spectral-Spatial Constraint Hyperspectral Image Classification[J].IEEE Transactions on Geoscience & Remote Sensing,2014,52(3):1811-1824. [17]KANG X,LI S,BENEDIKTSSON J A.Spectral─Spatial Hyperspectral Image Classification With Edge-Preserving Filtering[J].IEEE Transactions on Geoscience & Remote Sensing,2014,52(5):2666-2677. [18]TARABALKA Y,BENEDIKTSSON J A,CHANUSSOT J.Spectral-Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques[J].IEEE Transactions on Geoscience & Remote Sensing,2009,47(8):2973-2987. [19]POMALAZA-RAEZ C,MCGILLEM C.An adaptative,nonli-near edge-preserving filter[J].IEEE Transactions on Signal Processing,1984,32(3):571-576. [20]FAUVEL M,BENEDIKTSSON J A,CHANUSSOT J,et al.Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles[J].IEEE Transactions on Geoscience & Remote Sensing,2007,46(11):3804-3814. [21]BAUER E,KOHAVI R.An Empirical Comparison of Voting Classification Algorithms:Bagging,Boosting,and Variants[J].Machine Learning,1999,36(1):105-139. [22]JIMENEZ L O,MORALES-MORELL A,CREUS A.Classification of hyperdimensional data based on feature and decision fusion approaches using projection pursuit,majority voting,and neural networks[J].IEEE Transactions on Geoscience & Remote Sensing,1999,37(3):1360-1366. [23]PAL M.Ensemble of support vector machines for land coverclassification[J].International Journal of Remote Sensing,2008,29(10):3043-3049. [24]FAUVEL M,CHANUSSOT J,BENEDIKTSSON J A,et al.Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles[J].IEEE Transactions onGeo-science and Remote Sensing,2008,46(11):3804-3814. [25]IMANI M,GHASSEMIAN H.Discriminant analysis in morphological feature space for high-dimensional image spatial-spectral classification[J].Journal of Applied Remote Sensing,2018,12(1):1. [26]LI J,BIOUCAS-DIAS J M,PLAZA A.Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning[J].IEEE Transactions on Geoscience & Remote Sensing,2010,48(11):4085-4098. [27]SOOMRO B N,XIAO L,HUANG L,et al.Bilayer Elastic Net Regression Model for Supervised Spectral-Spatial Hyperspectral Image Classification[J].IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing,2017,9(9):4102-4116. [28]DIETTERICH T G.Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms[J].Neural Computation,1998,10(7):1895-1923. |
[1] | 李瑶, 李涛, 李埼钒, 梁家瑞, Ibegbu Nnamdi JULIAN, 陈俊杰, 郭浩. 基于多尺度的稀疏脑功能超网络构建及多特征融合分类研究 Construction and Multi-feature Fusion Classification Research Based on Multi-scale Sparse Brain Functional Hyper-network 计算机科学, 2022, 49(8): 257-266. https://doi.org/10.11896/jsjkx.210600094 |
[2] | 王馨彤, 王璇, 孙知信. 基于多尺度记忆残差网络的网络流量异常检测模型 Network Traffic Anomaly Detection Method Based on Multi-scale Memory Residual Network 计算机科学, 2022, 49(8): 314-322. https://doi.org/10.11896/jsjkx.220200011 |
[3] | 魏恺轩, 付莹. 基于重参数化多尺度融合网络的高效极暗光原始图像降噪 Re-parameterized Multi-scale Fusion Network for Efficient Extreme Low-light Raw Denoising 计算机科学, 2022, 49(8): 120-126. https://doi.org/10.11896/jsjkx.220200179 |
[4] | 孙福权, 崔志清, 邹彭, 张琨. 基于多尺度特征的脑肿瘤分割算法 Brain Tumor Segmentation Algorithm Based on Multi-scale Features 计算机科学, 2022, 49(6A): 12-16. https://doi.org/10.11896/jsjkx.210700217 |
[5] | 方连花, 林玉梅, 吴伟志. 随机多尺度序决策系统的最优尺度选择 Optimal Scale Selection in Random Multi-scale Ordered Decision Systems 计算机科学, 2022, 49(6): 172-179. https://doi.org/10.11896/jsjkx.220200067 |
[6] | 范新南, 赵忠鑫, 严炜, 严锡君, 史朋飞. 结合注意力机制的多尺度特征融合图像去雾算法 Multi-scale Feature Fusion Image Dehazing Algorithm Combined with Attention Mechanism 计算机科学, 2022, 49(5): 50-57. https://doi.org/10.11896/jsjkx.210400093 |
[7] | 张红民, 李萍萍, 房晓冰, 刘宏. 改进YOLOv3网络模型的人体异常行为检测方法 Human Abnormal Behavior Detection Method Based on Improved YOLOv3 Network Model 计算机科学, 2022, 49(4): 233-238. https://doi.org/10.11896/jsjkx.210300251 |
[8] | 邵海琳, 季怡, 刘纯平, 徐云龙. 基于增强特征金字塔网络的场景文本检测算法 Scene Text Detection Algorithm Based on Enhanced Feature Pyramid Network 计算机科学, 2022, 49(2): 248-255. https://doi.org/10.11896/jsjkx.201100072 |
[9] | 郑建炜, 黄娟娟, 秦梦洁, 徐宏辉, 刘志. 基于非局部相似及加权截断核范数的高光谱图像去噪 Hyperspectral Image Denoising Based on Non-local Similarity and Weighted-truncated NuclearNorm 计算机科学, 2021, 48(9): 160-167. https://doi.org/10.11896/jsjkx.200600135 |
[10] | 陶星朋, 徐宏辉, 郑建炜, 陈婉君. 基于非凸低秩矩阵逼近和全变分正则化的高光谱图像去噪 Hyperspectral Image Denoising Based on Nonconvex Low Rank Matrix Approximation and TotalVariation Regularization 计算机科学, 2021, 48(8): 125-133. https://doi.org/10.11896/jsjkx.200400143 |
[11] | 王栋, 周大可, 黄有达, 杨欣. 基于多尺度多粒度特征的行人重识别 Multi-scale Multi-granularity Feature for Pedestrian Re-identification 计算机科学, 2021, 48(7): 238-244. https://doi.org/10.11896/jsjkx.200600043 |
[12] | 袁星星, 吴秦. 基于显著性特征和角度信息的遥感图像目标检测 Object Detection in Remote Sensing Images Based on Saliency Feature and Angle Information 计算机科学, 2021, 48(4): 174-179. https://doi.org/10.11896/jsjkx.191200027 |
[13] | 巫勇, 刘永坚, 唐瑭, 王洪林, 郑建成. 基于鲁棒低秩张量恢复的高光谱图像去噪 Hyperspectral Image Denoising Based on Robust Low Rank Tensor Restoration 计算机科学, 2021, 48(11A): 303-307. https://doi.org/10.11896/jsjkx.210200103 |
[14] | 顾兴健, 朱剑峰, 任守纲, 熊迎军, 徐焕良. 多尺度U网络实现番茄叶部病斑分割与识别 Multi-scale U Network Realizes Segmentation and Recognition of Tomato Leaf Disease 计算机科学, 2021, 48(11A): 360-366. https://doi.org/10.11896/jsjkx.201000166 |
[15] | 朱戎, 叶宽, 杨博, 谢欢, 赵蕾. 基于改进DeeplabV3+的地物分类方法研究 Feature Classification Method Based on Improved DeeplabV3+ 计算机科学, 2021, 48(11A): 382-385. https://doi.org/10.11896/jsjkx.201100184 |
|