摘要
We present a novel sea-ice classification framework based on locality preserving fusion of multi-source images information. The locality preserving fusion arises from two-fold, i.e., the local characterization in both spatial and feature domains. We commence by simultaneously learning a projection matrix, which preserves spatial localities, and a similarity matrix, which encodes feature similarities. We map the pixels of multi-source images by the projection matrix to a set fusion vectors that preserve spatial localities of the image. On the other hand, by applying the Laplacian eigen-decomposition to the similarity matrix, we obtain another set of fusion vectors that preserve the feature local similarities. We concatenate the fusion vectors for both spatial and feature locality preservation and obtain the fusion image. Finally, we classify the fusion image pixels by a novel sliding ensemble strategy, which enhances the locality preservation in classification. Our locality preserving fusion framework is effective in classifying multi-source sea-ice images(e.g., multi-spectral and synthetic aperture radar(SAR)images) because it not only comprehensively captures the spatial neighboring relationships but also intrinsically characterizes the feature associations between different types of sea-ices. Experimental evaluations validate the effectiveness of our framework.
We present a novel sea-ice classification framework based on locality preserving fusion of multi-source images information. The locality preserving fusion arises from two-fold, i.e., the local characterization in both spatial and feature domains. We commence by simultaneously learning a projection matrix, which preserves spatial localities, and a similarity matrix, which encodes feature similarities. We map the pixels of multi-source images by the projection matrix to a set fusion vectors that preserve spatial localities of the image. On the other hand, by applying the Laplacian eigen-decomposition to the similarity matrix, we obtain another set of fusion vectors that preserve the feature local similarities. We concatenate the fusion vectors for both spatial and feature locality preservation and obtain the fusion image. Finally, we classify the fusion image pixels by a novel sliding ensemble strategy, which enhances the locality preservation in classification. Our locality preserving fusion framework is effective in classifying multi-source sea-ice images(e.g., multi-spectral and synthetic aperture radar(SAR)images) because it not only comprehensively captures the spatial neighboring relationships but also intrinsically characterizes the feature associations between different types of sea-ices. Experimental evaluations validate the effectiveness of our framework.
引文
Clausi D A.2001.Comparison and fusion of co-occurrence,Gabor and MRF texture features for classification of SAR sea-ice imagery.Atmosphere-Ocean,39(3):183-194,doi:10.1080/07055900.2001.9649675
He Xiaofei,Niyogi P.2004.Locality preserving projections.In:Advances in Neural Information Processing Systems.Cambridge:MIT Press
Huang Xin,Zhang Liangpen.2013.An SVM ensemble approach combining spectral,structural,and semantic features for the classification of high-resolution remotely sensed imagery.IEEETransactions on Geoscience and Remote Sensing,51(1):257-272,doi:10.1109/TGRS.2012.2202912
Kettig R L,Landgrebe D A.1976.Classification of multispectral image data by extraction and classification of homogeneous objects.IEEE Transactions on Geoscience Electronics,14(1):19-26,doi:10.1109/TGE.1976.294460
Liao Wenzhi,Pi?urica A,Bellens R,et al.2015.Generalized graphbased fusion of hyperspectral and LiDAR data using morphological features.IEEE Geoscience and Remote Sensing Letters,12(3):552-556,doi:10.1109/LGRS.2014.2350263
Liu Meijie,Dai Yongshou,Zhang Jie,et al.2013.The research on the object-based method of sea ice classification of high-resolution quad-polarization SAR data.Haiyang Xuebao(in Chinese),35(4):80-87
Liu Meijie,Dai Yongshou,Zhang Jie,et al.2015.PCA-based sea-ice image fusion of optical data by HIS transform and SAR data by wavelet transform.Acta Oceanologica Sinica,34(3):59-67,doi:10.1007/s13131-015-0634-7
Luo Yawei,Wu Huiding,Zhang Yunfei,et al.2004.Application of the HY-1 satellite to sea ice monitoring and forecasting.Acta Oceanologica Sinica,23(3):251-266
von LuxBurg U.2007.A tutorial on spectral clustering.Statistics and Computing,17(4):395-416,doi:10.1007/s11222-007-9033-z
Ouyang Lunxi,Hui Fengming,Zhu Lixian,et al.2017.The spatiotemporal patterns of sea ice in the Bohai Sea during the winter seasons of 2000-2016.International Journal of Digital Earth:doi:10.1080/17538947.2017.1365957
Shi Wei,Wang Menghua.2012.Sea ice properties in the Bohai Sea measured by MODIS-Aqua:1.Satellite algorithm development.Journal of Marine Systems,95:32-40
Su Hua,Wang Yunpeng,Yang Jingxue.2012.Monitoring the spatiotemporal evolution of sea ice in the Bohai Sea in the 2009-2010 winter combining MODIS and meteorological data.Estuaries and Coasts,35(1):281-291,doi:10.1007/s12237-011-9425-3
Tao Xuanwen,Cui Tingwei,Yu Zhiqiang,et al.2018.Locality preserving endmember extraction for estimating green algae area.In:Proceedings of OCEANS 2017,Aberdeen
Tarabalka Y,Benediktsson J A,Chanussot J.2009.Spectral-spatial classification of hyperspectral imagery based on partitional clustering techniques.IEEE Transactions on Geoscience and Remote Sensing,47(8):2973-2987,doi:10.1109/TGRS.2009.2016214
Yu S,Shi Jiaobo.2003.Multiclass Spectral Clustering.In:Proceedings of the 9th International Conference on Computer Vision.France:IEEE,1:313-319
Zeng Tao,Shi Lijian,Marko M,et al.2016.Sea ice thickness analyses for the Bohai Sea using MODIS thermal infrared imagery.Acta Oceanologica Sinica,35(7):96-104,doi:10.1007/s13131-016-0908-8
Zhang Limei,Qiao Lishan,Chen Songcan.2010.Graph-optimized locality preserving projections.Pattern Recognition,43(6):1993-2002,doi:10.1016/j.patcog.2009.12.022
Zhang Limei,Qiao Lishan.2017.A graph optimization method for dimensionality reduction with pairwise constraints.International Journal of Machine Learning and Cybernetics,8(1):275-281,doi:10.1007/s13042-014-0321-6
Zheng Jiajia,Ke Changqing,Shao Zhude.2017.Winter sea ice albedo variations in the Bohai Sea of China.Acta Oceanologica Sinica,36(1):56-63,doi:10.1007/s13131-017-0993-3