Remote sensing image retrieval with ant colony optimization and a weighted image-to-class distance

doi:10.11947/j.AGCS.2021.20200357

Abstract

Abstract: Remote sensing image retrieval (RSIR) aims to find relevant images of a query image from a remote sensing image retrieval dataset. But the similarity between a query image and a retrieval image is generally used and the relationship among images on the retrieval dataset is neglected during the retrieval process. To deal with the problem, this paper presents a new retrieval method based on ant colony optimization (ACO) for RSIR. First, our method uses the pheromone to represent the similarity between images on the retrieval dataset; then the pheromone matrix is updated by ACO. Finally, the pheromone of images is used to improve the performance of RSIR. Meanwhile, an improved weighted image-to-class distance is used to measure the similarity between two images for further improving the retrieval performance. Extensive experiments are conducted on two publicly available remote sensing image databases, UCMD and PatternNet. Compared with the state-of-the-art methods, the proposed method can achieve better retrieval results.

Key words: remote sensing image retrieval, ant colony optimization, pheromone, image-to-class similarity, convolutional neural networks

CLC Number:

P227

YE Famao, MENG Xianglong, DONG Meng, Nie Yunju, GE Yun, CHEN Xiaoyong. Remote sensing image retrieval with ant colony optimization and a weighted image-to-class distance[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(5): 612-620.

References

[1] GE Yun, TANG Yiling, JIANG Shunliang, et al. Region-based cascade pooling of convolutional features for HRRS image retrieval[J]. Remote Sensing Letters, 2018, 9(10):1002-1010.
[2] SEBAI H, KOURGLI A, SERIR A. Dual-tree complex wavelet transform applied on color descriptors for remote-sensed images retrieval[J]. Journal of Applied Remote Sensing, 2015, 9(1):1-17.
[3] 王新建, 罗光春, 秦科, 等. 一种基于SVM和主动学习的图像检索方法[J]. 计算机应用研究, 2016, 33(12):3836-3838, 3846. WANG Xinjian, LUO Guangchun, QIN Ke, et al. Image retrieval method based on SVM and active learning[J]. Application Research of Computers, 2016, 33(12):3836-3838, 3846.
[4] 葛芸, 江顺亮, 叶发茂, 等. 视觉词袋和Gabor纹理融合的遥感图像检索[J]. 光电工程, 2016, 43(2):76-81, 88. GE Yun, JIANG Shunliang, YE Famao, et al. Remote sensing image retrieval based on the fusion of BoVW and Gabor texture[J]. Opto-Electronic Engineering, 2016, 43(2):76-81, 88.
[5] ZHOU Weixun, SHAO Zhenfeng, DIAO Chunyuan, et al. High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder[J]. Remote Sensing Letters, 2015, 6(10):775-783.
[6] BOSILJ P, APTOULA E, LEFōVRE S, et al. Retrieval of remote sensing images with pattern spectra descriptors[J]. ISPRS International Journal of Geo-Information, 2016, 5(12):228. DOI:10.3390/ijgi5120228.
[7] 刘冰, 余旭初, 张鹏强, 等. 联合空-谱信息的高光谱影像深度三维卷积网络分类[J]. 测绘学报, 2019, 48(1):53-63. DOI:10.11947/j.AGCS.2019.20170578. LIU Bing, YU Xuchu, ZHANG Pengqiang, et al. Deep 3D convolutional network combined with spatial-spectral features for hyperspectral image classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1):53-63. DOI:10.11947/j.AGCS.2019.20170578.
[8] DAI Yuchao, ZHANG Jing, HE Mingyi, et al. Salient object detection from multi-spectral remote sensing images with deep residual network[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(2):101-110.
[9] 董志鹏, 王密, 李德仁, 等. 遥感影像目标的尺度特征卷积神经网络识别法[J]. 测绘学报, 2019, 48(10):1285-1295. DOI:10.11947/j.AGCS.2019.20180393. DONG Zhipeng, WANG Mi, LI Deren, et al. Object detection in remote sensing imagery based on convolutional neural networks with suitable scale features[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(10):1285-1295. DOI:10.11947/j.AGCS.2019.20180393.
[10] FAN Dazhao, DONG Yang, ZHANG Yongsheng. Satellite image matching method based on deep convolutional neural network[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(2):90-100. DOI:10.11947/j.JGGS.2019.0210.
[11] YE Famao, XIAO Hui, ZHAO Xuqing, et al. Remote sensing image retrieval using convolutional neural network features and weighted distance[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(10):1535-1539.
[12] 彭晏飞, 宋晓男, 訾玲玲, 等. 基于卷积神经网络和改进模糊C均值的遥感图像检索[J]. 激光与光电子学进展, 2018, 55(9):167-176. PENG Yanfei, SONG Xiaonan, ZI Lingling, et al. Remote sensing image retrieval based on convolutional neural network and modified fuzzy C-means[J]. Laser & Optoelectronics Progress, 2018, 55(9):167-176.
[13] ZHOU Weixun, NEWSAM S, LI Congmin, et al. Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval[J]. Remote Sensing, 2017, 9(5):489.
[14] 叶发茂, 董萌, 罗威, 等. 基于卷积神经网络和重排序的农业遥感图像检索[J]. 农业工程学报, 2019, 35(15):138-145. YE Famao, DONG Meng, LUO Wei, et al. Agricultural remote sensing image retrieval based on convolutional neural network and Reranking[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(15):138-145.
[15] 张洪群, 刘雪莹, 杨森, 等. 深度学习的半监督遥感图像检索[J]. 遥感学报, 2017, 21(3):406-414. ZHANG Hongqun, LIU Xueying, YANG Sen, et al. Retrieval of remote sensing images based on semisupervised deep learning[J]. Journal of Remote Sensing, 2017, 21(3):406-414.
[16] 李宇, 刘雪莹, 张洪群, 等. 基于卷积神经网络的光学遥感图像检索[J]. 光学精密工程, 2018, 26(1):200-207. LI Yu, LIU Xueying, ZHANG Hongqun et al. Optical remote sensing image retrieval based on convolutional neural networks[J]. Optics and Precision Engineering, 2018, 26(1):200-207.
[17] YE Famao, ZHAO Xuqing, LUO Wei, et al. Query-adaptive remote sensing image retrieval based on image rank similarity and image-to-query class similarity[J]. IEEE Access, 2020, 8:116824-116839.
[18] GE Yun, JIANG Shunliang, XU Qingyong, et al. Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval[J]. Multimedia Tools and Applications, 2018, 77(13):17489-17515.
[19] YE Famao, LUO Wei, DONG Meng, et al. SAR image retrieval based on unsupervised domain adaptation and clustering[J]. IEEE Geoscience and Remote Sensing Letter, 2019, 16(9):1482-1486.
[20] TONG Xinyi, XIA Guisong, HU Fan, et al. Exploiting deep features for remote sensing image retrieval:a systematic investigation[J]. IEEE Transactions on Big Data, 2020, 6(3):507-521.
[21] CHAUDHURI B, DEMIR B, BRUZZONE L, et al. Region-based retrieval of remote sensing images using an unsupervised graph-theoretic approach[J]. IEEE Geoscience and Remote Sensing Letters, 2016, 13(7):987-991.
[22] YE Famao, DONG Meng, LUO Wei, et al. A new re-ranking method based on convolutional neural network and two image-to-class distances for remote sensing image retrieval[J]. IEEE Access, 2019, 7:141498-141507.
[23] 董萌. 基于卷积神经网络和蚁群算法的遥感图像检索研究[D]. 南昌:南昌大学, 2020. DONG Meng. Remote sensing image retrieval based on convolutional neural network and ant colony optimization algorithm[D]. Nanchang:Nanchang University, 2020.
[24] DORIGO M, MANIEZZO V, COLORNI A. Ant system:optimization by a colony of cooperating agents[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996, 26(1):29-41.
[25] 张松灿, 普杰信, 司彦娜, 等. 蚁群算法在移动机器人路径规划中的应用综述[J]. 计算机工程与应用, 2020, 56(8):10-19. ZHANG Songcan, PU Jiexin, SI Yanna, et al. Survey on application of ant colony algorithm in path planning of mobile robot[J]. Computer Engineering and Applications, 2020, 56(8):10-19.
[26] 张驰, 李姗姗, 史颜俊, 等. 蚁群-势场算法在水下重力辅助导航航迹规划中的应用[J]. 测绘学报, 2020, 49(7):865-873. DOI:10.11947/j.AGCS.2020.20190194. ZHANG Chi, LI Shanshan, SHI Yanjun, et al. Application of ant colony-potential field algorithm in underwater gravity matching navigation track planning[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(7):865-873. DOI:10.11947/j.AGCS.2020.20190194.
[27] RAVEENDRA K, VINOTHKANNA R. Hybrid ant colony optimization model for image retrieval using scale-invariant feature transform local descriptor[J]. Computers & Electrical Engineering, 2019, 74:281-291.
[28] RASHNO A, SADRI S, SADEGHIANNEJAD H. An efficient content-based image retrieval with ant colony optimization feature selection schema based on wavelet and color features[C]//2015 The International Symposium on Artificial Intelligence and Signal Processing. Mashhad:IEEE, 2015.
[29] YANG Yi, NEWSAM S. Bag-of-visual-words and spatial extensions for land-use classification[C]//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose, CA:ACM, 2010:270-279.
[30] ZHOU Weixun, NEWSAM S, LI Congmin, et al. PatternNet:a benchmark dataset for performance evaluation of remote sensing image retrieval[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 145:197-209.
[31] 葛芸, 马琳, 江顺亮, 等. 基于高层特征图组合及池化的高分辨率遥感图像检索[J]. 电子与信息学报, 2019, 41(10):2487-2494. GE Yun, MA Lin, JIANG Shunliang, et al. The combination and pooling based on high-level feature map for high-resolution remote sensing image retrieval[J]. Journal of Electronics and Information Technology, 2019, 41(10):2487-2494.