基于低秩结构提取的高光谱图像压缩表示

唐中奇; 付光远; 陈进; 张利

doi:10.11999/JEIT150906

基于低秩结构提取的高光谱图像压缩表示

doi: 10.11999/JEIT150906

2.
(第二炮兵工程大学信息工程系西安 710025) ②(清华大学电子工程系北京 100084) ③(北京市遥感信息研究所北京 100192)

基金项目:

国家自然科学基金(61132007, 61202332, 61503405)，国家自然科学青年基金(61403397)，中国博士后科学基金(2012M521905)，陕西省自然科学基础研究计划项目(2015JM6313)

计量
- 文章访问数: 1407
- HTML全文浏览量: 107
- PDF下载量: 384
- 被引次数: 0
出版历程
- 收稿日期: 2015-07-30
- 修回日期: 2015-12-31
- 刊出日期: 2016-05-19

Low-rank Structure Based Hyperspectral Compression Representation

2.
(Xi&rsquo

Funds:

The National Natural Science Foundation of China (61132007, 61202332, 61503405), The National Natural Science Foundation for Young Scientists of China (61403397), China Postdoctoral Science Foundation (2012M521905), Natural Science Foundation of Shaanxi Province, China (2015JM6313)

摘要

摘要: 为实现高效、精准的高光谱图像分类，该文利用低秩矩阵恢复从原始数据中提取低维特征，实现高光谱图像的压缩表示。针对高光谱应用的特殊性，该文算法基于结构相似性度量(Structural Similarity Index Measurement, SSIM)对矩阵恢复过程提出了信噪分离约束，有助于选择更优的模型参数，增强表示的准确性。实验证明，相比现有相关方法，该文算法能够有效去除高光谱图像中的噪声，表示结果更为鲁棒；在仅使用低维特征时，仍能达到较高的分类精度。
- 高光谱图像分类 /
- 压缩表示 /
- 低秩矩阵恢复
Abstract: A method which makes use of structure information abstracted from hyperspectral data via low-rank matrix recovery for hyperspectral image classification is proposed in this paper. The principle of maximizing structure information based on Structural Similarity Index Measurement (SSIM) is proposed to restrain the process of matrix recovery as well, which facilitates the separation of the signal and the noise. The experiments show that the proposed algorithm can effectively eliminate the non-linear noise in hyperspectral image and abstract the low-rank characteristics of hyperspectral image, which achieves better performance in classification.
- Hyperspectral image classification /
- Compression representation /
- Low-rank matrix recovery

HTML全文

参考文献(20)

吴倩, 张荣, 徐大卫. 基于稀疏表示的高光谱数据压缩算法[J]. 电子与信息学报, 2015, 37(1): 78-84. doi: 10.11999/ JEIT140214.

WU Qian, ZHANG Rong, and XU Dawei. Hyperspectral data compression based on sparse representation[J]. Journal of Electronics Information Technology, 2015, 37(1): 78-84. doi: 10.11999/JEIT140214.

CAMPS-VALLS G, TUIA D, BRUZZONE L, et al. Advances in hyperspectral image classification[J]. IEEE Signal Processing Magazine, 2014, 21(4): 45-54. doi: 10.1109/MSP. 2013.2279179.

贾应彪, 冯燕, 王忠良, 等. 基于谱间结构相似先验的高光谱压缩感知重构[J]. 电子与信息学报, 2014, 36(6): 1406-1412. doi: 10.3724/SP.J.1146.2013.01132.

JIA Yingbiao, FENG Yan, WANG Zhongliang, et al. Hyperspectral compressive sensing recovery via spectrum structure similarity[J]. Journal of Electronics Information Technology, 2014, 36(6): 1406-1412. doi: 10.3724/SP.J.1146. 2013.01132.

BIOUCAS-DIAS J, PLAZA A, CAMPS-VALLS G, et al. Hyperspectral remote sensing data analysis and future challenges[J]. IEEE Remote Sensing Magazine, 2013, 1(2): 6-36. doi: 10.1109/MGRS.2013.2244672.

粘永健, 辛勤, 汤毅, 等. 基于多波段预测的高光谱图像分布式无损压缩[J]. 光学精密工程, 2012, 20(4): 906-912. doi: 10.3788/OPE.20122004.0906.

NIAN Yongjian, XIN Qin, TANG Yi, et al. Distributed lossless compression of hyperspectral images based on multi-band prediction[J]. Optics and Precision Engineering, 2012, 20(4): 906-912. doi: 10.3788/OPE.20122004.0906.

唐中奇, 付光远, 陈进, 等. 基于多尺度分割的高光谱图像稀疏表示与分类[J]. 光学精密工程, 2015, 23(9): 2708-2714. doi: 10.3788/OPE.20152309.2708.

TANG Zhongqi, FU Guangyuan, CHEN Jin, et al. Multiscale segmentation-based sparse coding for hyperspectral image classification[J]. Optics and Precision Engineering, 2015, 23(9): 2708-2714. doi: 10.3788/OPE.20152309.2708.

ZHOU Yicong, PENG Jiangtao, and CHEN C L P. Dimension reduction using spatial and spectral regularized local discriminant embedding for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(2): 1082-1095. doi: 10.1109/TGRS. 2014.2333539.

LIAO W, PIZURICA A, SCHEUNDERS P, et al. Semi- supervised local discriminant analysis for feature extraction in hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(1): 184-198. doi: 10.1109/ TGRS.2012.2200106.

CARIOU C, CHEHDI K, and MOAN S L. An unsupervised band reduction method for hyperspectral remote sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(3): 565-569. doi: 10.1109/LGRS.2010.2091673.

XUE Zhaohui, LI Jun, CHENG Liang, et al. Spectral-spatial classification of hyperspectral data via morphological component analysis-based image separation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 70-84. doi: 10.1109/TGRS.2014.2318332.

LIU Guangcan, LIN Zhouchen, YAN Shuicheng, et al. Robust recovery of subspace structures by low-rank representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2013, 35(1): 171-184. doi: 10.1109/ TPAMI.2012.88.

CANDS E, LI Xiaodong, MA Yi, et al. Robust principal component analysis?[J]. Journal of the ACM, 2011, 58(3). doi: 10.1145/1970392.1970395.

LIANG Xiao, REN Xiang, ZHANG Zhengdong, et al. Repairing sparse low-rank texture[C]. European Conference on Computer Vision (ECCV), Florence, Italy, 2012. doi: 10.1007/978-3-642-33715-4_35.

JIA Kui, CHAN T H, and MA Yi. Robust and practical face recognition via structured sparsity[C]. European Conference on Computer Vision (ECCV), Florence, Italy, 2012. doi: 10.1007/978-3-642-33765-9_24.

WANG Z and SIMONCELLI E P. An adaptive linear system framework for image distortion analysis[C]. IEEE International Conference Image Processing, Genoa, Italy, 2005, 3: 1160-1163. doi: 10.1007/11889762_19.

LIN Zhouchen, CHEN Minming, WU Leqin, et al. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices[R]. University of Illinois at Urbana Champaign (UIUC) Technical Report UILU- ENG-09-2215, 2009. doi: 10.1016/j.jsb.2012.10.010.

施引文献

资源附件(0)

访问统计