地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (2): 263-271.doi: 10.3724/SP.J.1047.2016.00263
收稿日期:
2015-05-11
修回日期:
2015-10-27
出版日期:
2016-02-10
发布日期:
2016-02-04
作者简介:
作者简介:朱 勇(1989-),男,硕士,研究方向为遥感图像处理。E-mail:
基金资助:
Received:
2015-05-11
Revised:
2015-10-27
Online:
2016-02-10
Published:
2016-02-04
Contact:
WU Bo
摘要:
高光谱遥感影像的稀疏分类是当前遥感信息处理的研究热点。本文提出一种光谱与空间双重稀疏表达的高光谱遥感影像分类方法(WSSRC)。首先利用小波字典对光谱维进行稀疏表示,将光谱维稀疏分类转化到小波域稀疏分类;其次,考虑空间邻域地物光谱的统一性和差异性,对邻域内像元分别进行稀疏编码,并对编码进行累加聚合;然后,利用聚合后的稀疏编码构造线性分类器对高光谱影像进行分类;最后,通过2幅标准的高光谱影像数据验证了本文所提出的方法。实验结果表明,该方法能有效地提高影像的分类精度。
朱勇, 吴波. 光谱与空间维双重稀疏表达的高光谱影像分类[J]. 地球信息科学学报, 2016, 18(2): 263-271.DOI:10.3724/SP.J.1047.2016.00263
ZHU Yong,WU Bo. Classification of Hyperspectral Images with Spectral-Spatial Sparse Representation[J]. Journal of Geo-information Science, 2016, 18(2): 263-271.DOI:10.3724/SP.J.1047.2016.00263
表4
不同模型各类别分类精度比较 / (%)
类别 | SRC | WSRC | JSRC | WSSRC |
---|---|---|---|---|
苜蓿 | 39.02 | 75.61 | 21.95 | 92.68 |
玉米I | 60.31 | 65.45 | 94.09 | 96.11 |
玉米II | 52.61 | 59.30 | 84.20 | 91.83 |
玉米III | 32.39 | 42.25 | 85.92 | 95.31 |
草I | 86.64 | 89.63 | 94.24 | 94.70 |
草II | 95.74 | 95.89 | 99.70 | 99.70 |
草III | 64.00 | 84.00 | 80.00 | 96.00 |
干草梗 | 95.81 | 97.44 | 100.00 | 100.00 |
燕麦 | 55.56 | 61.11 | 0.00 | 0.00 |
大豆I | 56.86 | 69.11 | 85.47 | 91.42 |
大豆II | 69.58 | 73.38 | 94.43 | 98.37 |
大豆III | 49.91 | 60.41 | 95.87 | 97.94 |
小麦 | 98.37 | 98.37 | 100.00 | 99.46 |
树丛 | 89.89 | 89.10 | 98.51 | 98.86 |
建筑物-草-树-路 | 41.21 | 51.87 | 85.59 | 93.95 |
石-钢顶棚 | 79.52 | 80.72 | 97.59 | 98.80 |
OA | 69.53 | 74.46 | 92.98 | 96.46 |
kappa系数 | 0.651 | 0.708 | 0.920 | 0.960 |
表6
不同模型各类别分类精度比较 / (%)
类别 | SRC | WSRC | JSRC | WSSRC |
---|---|---|---|---|
柏油 | 79.97 | 82.62 | 90.16 | 96.75 |
草甸 | 92.75 | 93.11 | 99.41 | 99.21 |
碎石 | 66.65 | 68.29 | 82.69 | 80.36 |
树 | 89.01 | 88.68 | 94.41 | 95.43 |
金属板 | 99.83 | 99.67 | 99.92 | 99.92 |
裸土 | 66.06 | 66.02 | 74.04 | 79.89 |
沥青 | 68.17 | 76.44 | 89.31 | 88.22 |
砖 | 68.52 | 70.57 | 84.79 | 94.11 |
阴影 | 90.96 | 94.25 | 93.08 | 97.77 |
OA | 83.42 | 84.54 | 92.12 | 94.57 |
kappa系数 | 0.779 | 0.794 | 0.894 | 0.927 |
[1] |
Elad M, Aharon M.Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Transactions on Image Processing, 2006,15(12):3736-3745.
doi: 10.1109/TIP.2006.881969 pmid: 17153947 |
[2] | Mairal J, Elad M, Sapiro G.Sparse representation for color image restoration[J]. IEEE Transactions on Image Processing, 2008,17(1):53-69. |
[3] |
Yang J, Wright J, Huang T S, et al.Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010,19(11):2861-2873.
doi: 10.1109/TIP.2010.2050625. pmid: 20483687 |
[4] | Huang K, Aviyente S.Sparse representation for signal classification[C]. Advances in Neural Information Processing Systems. MIT: Cambridge, MA, 2006:609-616. |
[5] | Wright J, Yang A Y, Ganesh A, et al.Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009,31(2):210-227. |
[6] |
Chen Y, Nasrabadi N M, Tran T D.Hyperspectral image classification using dictionary-based sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011,49(10):3973-3985.
doi: 10.1109/TGRS.2011.2129595 |
[7] |
Zhang H, Li J, Huang Y, et al.A nonlocal weighted joint sparse representation classification method for hyperspectral imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014,7(6):2056-2065.
doi: 10.1109/JSTARS.2013.2264720 |
[8] |
Chen Y, Nasrabadi N M, Tran T D.Hyperspectral image classification via kernel sparse representation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013,51(1):217-231.
doi: 10.1109/TGRS.2012.2201730 |
[9] |
Liu J, Wu Z, Wei Z, et al.Spatial-spectral kernel sparse representation for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013,6(6):2462-2471.
doi: 10.1109/JSTARS.2013.2252150 pmid: 24059451 |
[10] |
宋相法,焦李成.基于稀疏表示及光谱信息的高光谱遥感图像分类[J].电子与信息学报,2012,34(2):268-272.
doi: 10.3724/SP.J.1146.2011.00540 |
[ Song X F, Jiao L C.Classification of hyperspectral remote sensing image based on sparse representation and spectral information[J]. Journal of Electronics & Information Technology, 2012,34(2):268-272. ]
doi: 10.3724/SP.J.1146.2011.00540 |
|
[11] |
孙伟伟,刘春,施蓓琦,等.基于随机矩阵的高光谱影像非负稀疏表达分类[J].同济大学学报(自然科学版),2013,41(8).
doi: 10.3969/j.issn.0253-374x.2013.08.026 |
[ Sun W W, Liu C, Shi P Y, et al.Random matrix-based nonnegative sparse representation for hyperspectral image classification[J]. Journal of Tongji University (Natural Science), 2013.41(8):1274-1278. ]
doi: 10.3969/j.issn.0253-374x.2013.08.026 |
|
[12] |
刘建军,吴泽彬,韦志辉,等.基于空间相关性约束稀疏表示的高光谱图像分类[J].电子与信息学报,2012,34(11):2666-2671.
doi: 10.3724/SP.J.1146.2012.00577 |
[ Liu J J, Wu Z B, Wei Z H, et al.Spatial correlation constrained sparse representation for hyperspectral image classification[J]. Journal of Electronics & Information Technology, 2012,34(11):2666-2671. ]
doi: 10.3724/SP.J.1146.2012.00577 |
|
[13] |
Davis G M, Mallat S G, Zhang Z.Adaptive time-frequency decompositions[J]. Optical Engineering, 1994,33(7):2183-2191.
doi: 10.1117/12.173207 |
[14] |
Chen S S, Donoho D L, Saunders M A.Atomic decomposition by Basis Pursuit[J]. SIAM Journal on Scientific Computing, 1998,20(1):33-61.
doi: 10.1137/S003614450037906X |
[15] |
Ophir B, Lustig M, Elad M.Multi-scale dictionary learning using wavelets[J]. IEEE Journal of Selected Topics in Signal Processing, 2011,5(5):1014-1024.
doi: 10.1109/JSTSP.2011.2155032 |
[16] |
Rubinstein R, Zibulevsky M, Elad M.Double sparsity: Learning sparse dictionaries for sparse signal approximation[J]. IEEE Transactions on Signal Processing, 2010,58(3):1553-1564.
doi: 10.1109/TSP.2009.2036477 |
[17] |
梁锐华,成礼智.基于小波域字典学习方法的图像双重稀疏表示[J].国防科技大学学报,2012,34(4):126-131.
doi: 10.3969/j.issn.1001-2486.2012.04.025 |
[ Liang R H, Cheng L Z.Double sparse image representation via learning dictionaries in wavelet domain[J]. Journal of National University of Defense Technology, 2012,34(4):126-131. ]
doi: 10.3969/j.issn.1001-2486.2012.04.025 |
|
[18] | 吴波,熊助国.基于光谱最佳尺度分割特征的高光谱混合像元分解[J].测绘学报,2012,41(2):205-212. |
[ Wu B, Xiong Z G.Unmixing of hyperspectral mixture pixels based on spectral multi-scale segmented features[J]. Acta Geodaetics et Cartographica Sinica, 2012,41(2):205-212. ] | |
[19] |
吴波,张良培,李平湘.基于光谱维小波特征的混合像元投影迭代分解[J].电子与信息学报,2005,33(11):1933-1936.
doi: 10.3321/j.issn:0372-2112.2005.11.004 |
[ Wu B, Zhang L P, Li P X.Projective iterative unmixing of hyperspectral image based on spectral domain wavelet feature[J]. Journal of Electronics & Information Technology, 2005,33(11):1933-1936. ]
doi: 10.3321/j.issn:0372-2112.2005.11.004 |
|
[20] |
Yuan H L, Tang Y Y.A novel sparsity-based framework using max pooling operation for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Signal Processing, 2014,7(8):3570-2576.
doi: 10.1109/JSTARS.2014.2339298 |
[21] | Gualtieri J A, Cromp R F.Support vector machines for hyperspectral remote sensing classification[C]. The 27th AIPR Workshop: Advances in Computer-Assisted Recognition. International Society for Optics and Photonics, Springer Netherlands, 1999:221-232. |
[22] | 辛芳芳,焦李成,王桂婷.非局部均值加权的动态模糊Fisher分类器的遥感图像变化检测[J].测绘学报,2012,41(4):584-590. |
[ Xin F F,Jiao L C,Wang G T.Change detection in multitemporal remote sensing image based on dynamic fuzzy fisher classifier and non local mean weighted method[J]. Acta Geodaetica et Cartographica Sinica, 2012,41(4):584-590. ] | |
[23] | Yang M, Zhang D, Feng X.Fisher discrimination dictionary learning for sparse representation[C]. IEEE International Conference on Computer Vision (ICCV), 2011:543-550. |
[24] |
Plaza A, Benediktsson J A, Boardman J W, et al.Recent advances in techniques for hyperspectral image processing[J]. Remote Sensing of Environment, 2009,113:S110-S122.
doi: 10.1016/j.rse.2007.07.028 |
[1] | 左溪冰, 刘智, 金飞, 林雨准, 王淑香, 刘潇, 李美霖. 面向高光谱影像小样本分类的全局-局部特征自适应融合方法[J]. 地球信息科学学报, 2023, 25(8): 1699-1716. |
[2] | 刘文宋, 张仲英, 郑琳, 郭风成. 基于改进HLT与深度学习的双时相PolSAR洪涝灾害监测新方法[J]. 地球信息科学学报, 2023, 25(8): 1730-1745. |
[3] | 周温存, 刘正佳, 王坤, 邹时林, 钟会民, 陈芳鑫. 北方农牧交错区干旱特征变化及其对植被总初级生产力的影响[J]. 地球信息科学学报, 2023, 25(2): 421-437. |
[4] | 何山, 闫浩文, 李蓬勃. 基于离散傅里叶变换的线要素节点压缩方法[J]. 地球信息科学学报, 2022, 24(12): 2309-2321. |
[5] | 戴俊杰, 董婧雯, 杨晟, 孙毅中. 基于空间突变特征的城市边缘区提取方法[J]. 地球信息科学学报, 2021, 23(8): 1401-1421. |
[6] | 何永红, 靳鹏伟, 舒敏. 基于多尺度相关性分析的InSAR对流层延迟误差改正算法[J]. 地球信息科学学报, 2020, 22(9): 1878-1886. |
[7] | 许佳峰, 李云梅, 徐杰, 雷少华, 毕顺, 周玲. 黑臭水体水面阴影提取的自适应阈值算法研究[J]. 地球信息科学学报, 2020, 22(10): 1959-1970. |
[8] | 施慧慧, 王妮, 滕文秀, 刘玉婵. 结合Gabor小波和形态学的高分辨率图像树冠提取方法[J]. 地球信息科学学报, 2019, 21(2): 249-258. |
[9] | 车磊, 王海起, 费涛, 闫滨, 刘玉, 桂丽, 陈冉, 翟文龙. 基于多尺度最小二乘支持向量机优化的克里金插值方法[J]. 地球信息科学学报, 2017, 19(8): 1001-1010. |
[10] | 陈素景, 李丽娟, 李九一, 刘佳旭. 近55年来澜沧江流域降水时空变化特征分析[J]. 地球信息科学学报, 2017, 19(3): 365-373. |
[11] | 王飞, 丁建丽, 魏阳. “一带一路”国家和地区百年尺度干旱化特征分析[J]. 地球信息科学学报, 2017, 19(11): 1442-1455. |
[12] | 刘佳旭, 李丽娟, 李九一, 王志勇, 陈素景, 张凯. 1954-2014年云南省降水变化特征与潜在的旱涝区域响应[J]. 地球信息科学学报, 2016, 18(8): 1077-1086. |
[13] | 杨可明, 魏华锋, 刘飞, 史钢强, 孙阳阳. 以光谱信息熵改进的N-FINDR高光谱端元提取算法[J]. 地球信息科学学报, 2015, 17(8): 979-985. |
[14] | 张亚南, 朱长青, 杜福光. 多进制小波变换的图像分辨率定量降低方法[J]. 地球信息科学学报, 2012, 14(3): 352-357. |
[15] | 李艳红, 庞小平, 李海亭. 网络环境下的遥感影像金字塔纹理压缩算法与实验[J]. 地球信息科学学报, 2012, 14(1): 109-115. |
|