Integrating color features in polarimetric SAR image classification
BU Lijing1, HUANG Pengyan2, SHEN Lu1
1. School of Mapping and Geographical Science, Liaoning Technical University, Fuxin 123000, China; 2. School of Civil Engineering, Luoyang Institute of Science and Technology, Luoyang 471000, China
Abstract：This paper presents a method for combining the color feature and target decomposition characteristics so as to study the classification of polarimetric SAR. It makes up decomposition feature vector by polarimetric target decomposition and then, through the pseudo color enhancement method, obtains the false color image of polarimetric SAR data representation; after that, it extracts color histogram from the pseudo color images to make up the color feature vector, thus providing additional information for further land classification. Classification experiments were performed at different feature vectors by using RadarSat-2 polarimetric SAR image. In addition, the quantitative and qualitative comparison analysis was conducted with classification results. The experimental results show that the addition of the color feature can effectively improve the classification accuracy of polarimetric SAR images.
卜丽静, 黄鹏艳, 沈璐. 一种结合颜色特征的PolSAR图像分类方法[J]. 国土资源遥感, 2017, 29(3): 59-64.
BU Lijing, HUANG Pengyan, SHEN Lu. Integrating color features in polarimetric SAR image classification. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(3): 59-64.
[1] Lee J S,Pottier E.Airborne and space-borne polarimetric SAR systems[M]//Thompson B J Lee J S,Pottier E.Polarimetric Radar Imaging:From Basics to Applications.Boca Raton,FL,USA:CRC Press,2009:13-22. [2] Du L J,Lee J S.Polarimetric SAR image classification based on target decomposition theorem and complex Wishart distribution[C]//Proceedings of Remote Sensing for a Sustainable Future’ Geoscience and Remote Sensing Symposium.Lincoln,NE:IEEE,1996:439-441. [3] Haralick R M,Shanmugam K,Dinstein I.Textural features for image classification[J].IEEE Transactions on Systems,Man,and Cybernetics,1973,SMC-3(6):610-621. [4] Chen L J,Yang W,Liu Y,et al.Feature evaluation and selection for polarimetric SAR image classification[C]//Proceedings of 2010 IEEE the 10th International Conference on Signal Processing.Beijing,China:IEEE,2010:2202-2205. [5] Shanmugan K S,Narayanan V,Frost V S,et al.Textural features for Radar image analysis[J].IEEE Transactions on Geoscience and Remote Sensing,1981,GE-19(3):153-156. [6] Dell’Acqua F,Gamba P.Texture-based characterization of urban environments on satellite SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(1):153-159. [7] Ersahin K,Scheuchl B,Cumming I.Incorporating texture information into polarimetric Radar classification using neural networks[C]//Proceedings of 2004 IEEE International Geoscience and Remote Sensing Symposium.New York:IEEE,2004:560-563. [8] 余 磊.极化SAR图像伪彩色增强与可视化方法研究[D].郑州:解放军信息工程大学,2012. Yu L.Study on Methods of Pseudo-Color Enhancement and Visualization for Polarimetric SAR Image[D].Zhengzhou:The PLA Information Engineering University,2012. [9] Manjunath B S,Ohm J R,Vasudevan V V,et al.Color and texture descriptors[J].IEEE Transactions on Circuits and Systems for Video Technology,2001,11(6):703-715. [10] Gevers T,Gijsenij A,Weijer J,et al.Color in Computer Vision:Fundamentals and Applications[M].New York,NY,USA:Wiley,2012. [11] Huang J,Kumar S R,Mitra M,et al.Image indexing using color correlograms[C]//Proceedings of the 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Juan:IEEE,1997:762-768. [12] 官倩宁,覃团发,帅 勤,等.综合MPEG-7中纹理和颜色特征的图像检索方法[J].计算机应用研究.2008,25(3):957-960. Guan Q N,Qin T F,Shuai Q,et al.Image retrieval based on combined texture and color features in MPEG-7[J].Application Research of Computers,2008,25(3):957-960. [13] Cinque L,Ciocca G,Levialdi S,et al.Color-based image retrieval using spatial-chromatic histograms[J].Image and Vision Computing,2001,19(13):979-986. [14] Uhlmann S,Kiranyaz S.Integrating color features in polarimetric SAR image classification[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(4):2197-2216. [15] Zhang L M,Zou B,Zhang J P,et al.Classification of polarimetric SAR image based on support vector machine using multiple-component scattering model and texture features[J].EURASIP Journal on Advances in Signal Processing,2009,2010:Article ID 960831. [16] Cortes C,Vapnik V.Support-vector networks[J].Machine Learning,1995,20(3):273-297. [17] Zhou X D,Zhang C H.A perceptive uniform pseudo-color coding method of SAR images[C]//Proceedings of International Conference on Radar.Shanghai,China:IEEE,2006:1-4. [18] Swain M J,Ballard D H.Color indexing[J].International Journal of Computer Vision,1991,7(1):11-32. [19] Jiang H B,Su Y Y,Jiao Q S,et al.Typical geologic disaster surveying in Wenchuan 8.0 earthquake zone using high resolution ground LiDAR and UAV remote sensing[C]//Proceedings of SPIE Lidar Remote Sensing for Environmental Monitoring XIV.Beijing,China:SPIE,2014:926219.
2017, Vol. 29 
