Building area recognition method of remote sensing image based on MRELBP feature, Franklin moment and SVM

doi:10.11947/j.AGCS.2020.20190073

Abstract

Abstract: To further improve the recognition accuracy of remote sensing image building area recognition, a classification method based on median robust extended local binary pattern(MRELBP) feature, Franklin moment and optimized support vector machine(SVM) by cuckoo search is proposed. Firstly, calculate the texture feature vector of the image block with MRELBP feature operator and use Franklin moment obtain the shape feature vector, the texture feature vector and the shape feature vector are combined into a comprehensive feature vector. Then train the SVM with training image samples, meanwhile, use cuckoo search to optimize the kernel function parameter as well as the penalty factor. Lastly, input the recognizing image into the SVM to get the result of building area recognition. The results of 30 groups of experiments show that, compared with the classification method based on RGB and SVM, the classification method based on LBP and SVM and the classification method based on Zernike moment and SVM, the accuracy of the remote sensing image building area identified by the proposed method is higher.

Key words: remote sensing image, building area recognition, MRELBP feature, Franklin moment, support vector machine, cuckoo search

CLC Number:

P237

ZHOU Jianwei, WU Yiquan. Building area recognition method of remote sensing image based on MRELBP feature, Franklin moment and SVM[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(3): 355-364.

References

[1] KANG Jian, KÖRNER M, WANG Yuanyuan, et al. Building instance classification using street view images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 145:44-59.
[2] NIEMEYER J, ROTTENSTEINER F, SOERGEL U. Contextual classification of LiDAR data and building object detection in urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87:152-165.
[3] SUN Li, TANG Yuqi, ZHANG Liangpei. Rural building detection in high-resolution imagery based on a two-stage CNN model[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(11):1998-2002.
[4] 谢嘉丽, 李永树, 李何超, 等. 利用灰度共生矩阵纹理特征识别空心村损毁建筑物的方法[J]. 测绘通报, 2017(12):90-93, 102. DOI:10.13474/j.cnki.11-2246.2017.0386. XIE Jiali, LI Yongshu, LI Hechao, et al. Recognition of damage buildings in hollow village based on texture feature of gray level co-occurrence matrix[J]. Bulletin of Surveying and Mapping, 2017(12):90-93, 102. DOI:10.13474/j.cnki.11-2246.2017.0386.
[5] 宋桔尔, 王雪, 李培军. 加入空间-时相纹理的高分辨率图像城市建筑物倒塌提取[J]. 遥感学报, 2012, 16(6):1233-1245. SONG Juer, WANG Xue, LI Peijun. Urban building damage detection from VHR imagery by including temporal and spatial texture features[J]. Journal of Remote Sensing, 2012, 16(6):1233-1245.
[6] LUO Qingli, ZHOU Guoqing, ZHANG Guangyun, et al. The texture extraction and mapping of buildings with occlusion detection[C]//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy:IEEE, 2015:3002-3005.
[7] 何曼芸, 程英蕾, 邱浪波, 等. 一种改进顶帽变换与LBP高程纹理的城区建筑物提取算法[J]. 测绘学报, 2017, 46(9):1116-1122. DOI:10.11947/j.AGCS.2017.20170158. HE Manyun, CHENG Yinglei, QIU Langbo, et al. An algorithm of building extraction in urban area based on improved top-hat transformations and LBP elevation texture[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(9):1116-1122. DOI:10.11947/j.AGCS.2017.20170158.
[8] LIU Li, FIEGUTH P W, GUO Yulan, et al. Median robust extended local binary pattern for texture classification[J]. IEEE Transactions on Image Processing, 2016, 25(3):1368-1381.
[9] 高薇, 王珂, 原发杰, 等. 基于不变矩的高分辨率遥感图像的建筑物提取方法[J]. 计算机应用研究, 2014, 31(2):622-624. GAO Wei, WANG Ke, YUAN Fajie, et al. Building information extraction of high resolution remote sensing image based on moment invariants[J]. Application Research of Computers, 2014, 31(2):622-624.
[10] 徐海卿, 李培军, 沈毅. 加入不变矩的高分辨率遥感图像分类[J]. 国土资源遥感, 2008(2):9-13. XU Haiqing, LI Peijun, CHEN Yi. The application of invariant moments to high resolution remote sensing image classification[J]. Remote Sensing for Land & Resources, 2008(2):9-13.
[11] 陈伟, 蔡占川, 齐东旭. 一类新的正交矩-Franklin矩及其图像表达[J]. 计算机学报, 2015, 38(6):1140-1147. CHEN Wei, CAI Zhanchuan, QI Dongxu. Orthogonal Franklin moments and its application for image representation[J]. Chinese Journal of Computers, 2015, 38(6):1140-1147.
[12] SUN Weidong, SHI Lei, YANG Jie, et al. Building collapse assessment in urban areas using texture information from postevent SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(8):3792-3808.
[13] YANG H L, YUAN Jiangye, LUNGA D, et al. Building extraction at scale using convolutional neural network:mapping of the United States[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(8):2600-2614.
[14] 涂继辉, 眭海刚, 冯文卿, 等. 利用词袋模型检测建筑物顶面损毁区域[J]. 武汉大学学报(信息科学版), 2018, 43(5):691-696. TU Jihui, SUI Haigang, FENG Wenqing, et al. Detection of damaged areas based on visual bag-of-words model from aerial remote sensing images[J]. Geomatics and Information Science of Wuhan University, 2018, 43(5):691-696.
[15] SHIROWZHAN S, TRINDER J. Building classification from LiDAR data for spatio-temporal assessment of 3D urban developments[J]. Procedia Engineering, 2017, 180:1453-1461.
[16] YANG Xinshe, DEB S. Cuckoo search via lévy flights[C]//Proceedings of World Congress on Nature & Biologically Inspired Computing. Coimbatore, India:IEEE, 2009:210-214.
[17] 陈亮, 卢厚清. 求解连续函数优化的自适应布谷鸟搜索算法[J]. 解放军理工大学学报(自然科学版), 2015, 16(3):299-304. CHEN Liang, LU Houqing. Self-adaptive cuckoo search algorithm for continuous function optimization problems[J]. Journal of PLA University of Science and Technology (Natural Science Edition), 2015, 16(3):299-304.
[18] 赵莉. 基于改进布谷鸟搜索算法的云计算资源调度[J]. 南京理工大学学报, 2016, 40(4):472-476. ZHAO Li. Cloud computing resource scheduling based on modified cuckoo search algorithm[J]. Journal of Nanjing University of Science and Technology, 2016, 40(4):472-476.
[19] GOBAL J, NARAYAN S. Hybrid decision tree fuzzy rule based classifier for heart disease prediction using chaotic cuckoo search algorithm[J]. Journal of Engineering and Applied Sciences, 2017, 12(23):7358-7366.
[20] 张浩, 赵云胜, 陈冠宇, 等. 基于支持向量机的遥感图像建筑物识别与分类方法研究[J]. 地质科技情报, 2016, 35(6):194-199. ZHANG Hao, ZHAO Yunsheng, CHEN Guanyu, et al. Remote sensing image building recognition and classification based on the support vector machine[J]. Geological Science and Technology Information, 2016, 35(6):194-199.
[21] ÇAMLICA Z, TIZHOOSH H R, KHALVATI F. Medical image classification via SVM using LBP features from saliency-based folded data[C]//Proceedings of the 14th International Conference on Machine Learning and Applications. Miami, FL:IEEE, 2015:128-132.
[22] 刘丽, 谢毓湘, 魏迎梅, 等. 局部二进制模式方法综述[J]. 中国图象图形学报, 2014, 19(12):1696-1720. LIU Li, XIE Yuxiang, WEI Yingmei, et al. Survey of local binary pattern method[J]. Journal of Image and Graphics, 2014, 19(12):1696-1720.
[23] 蔡占川, 陈伟, 齐东旭, 等. 一类新的正交样条函数——Franklin函数的推广及其应用[J]. 计算机学报, 2009, 32(10):2004-2013. CAI Zhanchuan, CHEN Wei, QI Dongxu, et al. A class of general franklin functions and its application[J]. Chinese Journal of Computers, 2009, 32(10):2004-2013.
[24] 梁栋, 孔颉, 胡根生, 等. 基于支持向量机的遥感影像厚云及云阴影去除[J]. 测绘学报, 2012, 41(2):225-231, 238. LIANG Dong, KONG Jie, HU Gensheng, et al. The removal of thick cloud and cloud shadow of remote sensing image based on support vector machine[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2):225-231, 238.
[25] YANG Xinshe. Nature-inspired metaheuristic algorithms[M]. 2nd ed. Frome, UK:Luniver Press, 2010:11-16.