Remote Sensing Image Segmentation Using Super-Pixel and Dot Product Representation of Graphs

Daming Zhang; Xueyong Zhang; Huayong Liu; Lu Li

doi:10.3788/LOP202259.1210015

[1] Chehata N, Orny C, Boukir S et al. Object-based change detection in wind storm-damaged forest using high-resolution multispectral images[J]. International Journal of Remote Sensing, 35, 4758-4777(2014).

[2] Bao S W, Chen H Y, Zhang L M et al. Absolute radiometric calibration of optical sensor based on gray-scale targets method and reflectance inversion validation[J]. Acta Optica Sinica, 41, 0428001(2021).

[3] Han Z S, Wang C P, Fu Q et al. Remote sensing image mode translation based on spatial disentangled representation GAN[J]. Acta Optica Sinica, 41, 0728003(2021).

[4] Huang P, Zheng Q, Liang C. Overview of image segmentation methods[J]. Journal of Wuhan University (Natural Science Edition), 66, 519-531(2020).

[5] Peng B, Zhang L, Zhang D. A survey of graph theoretical approaches to image segmentation[J]. Pattern Recognition, 46, 1020-1038(2013).

[6] Zhang Y J. Image engineering in China: 2020[J]. Journal of Image and Graphics, 26, 978-990(2021).

[7] Fan S C, Sun Y H, Shui P L. Region-merging method with texture pattern attention for SAR image segmentation[J]. IEEE Geoscience and Remote Sensing Letters, 18, 112-116(2021).

[8] Zhang L P, Zhang Y, Chen Z Z et al. Splitting and merging based multi-model fitting for point cloud segmentation[J]. Journal of Geodesy and Geoinformation Science, 2, 78-89(2019).

[9] Lassalle P, Inglada J, Michel J et al. A scalable tile-based framework for region-merging segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 5473-5485(2015).

[10] Zhang Y M, Sun H Y, Xu Y L. An improved multispectral image segmentation method based on super-pixels[J]. Remote Sensing for Land & Resources, 31, 58-64(2019).

[11] Huang L, Yao B X, Chen P D et al. Superpixel segmentation method of high-resolution remote sensing image based on fuzzy clustering[J]. Acta Geodaetica et Cartographica Sinica, 49, 589-597(2020).

[12] An J Q, Shi Y C, Han Y H et al. Extract and merge: superpixel segmentation with regional attributes[M]. Vedaldi A, Bischof H, Brox T, et al. Computer Vision-ECCV 2020, 12375, 155-170(2020).

[13] Achanta R, Shaji A, Smith K et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, 2274-2282(2012).

[14] Guo Q, Peng L. Hyperspectral classification based on 3D convolutional extension network and super pixel segmentation[J]. Acta Optica Sinica, 41, 2210001(2021).

[15] Xu H L, Zhang H Y, He W et al. Superpixel-based spatial-spectral dimension reduction for hyperspectral imagery classification[J]. Neurocomputing, 360, 138-150(2019).

[16] Song X Y, Zhou L L, Li Z G et al. Review on superpixel methods in image segmentation[J]. Journal of Image and Graphics, 20, 599-608(2015).

[17] Kang X J, Zhu L, Ming A L. Dynamic random walk for superpixel segmentation[J]. IEEE Transactions on Image Processing, 29, 3871-3884(2020).

[18] Ban Z H, Liu J G, Cao L. Superpixel segmentation using Gaussian mixture model[J]. IEEE Transactions on Image Processing, 27, 4105-4117(2018).

[19] Karydas C, Jiang B. Scale optimization in topographic and hydrographic feature mapping using fractal analysis[J]. ISPRS International Journal of Geo-Information, 9, 631(2020).

[20] Pont-Tuset J, Arbelaez P, Barron J T et al. Multiscale combinatorial grouping for image segmentation and object proposal generation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 128-140(2017).

[21] Scheinerman E R, Tucker K. Modeling graphs using dot product representations[J]. Computational Statistics, 25, 1-16(2010).

[22] Athreya A, Fishkind D E, Levin K et al. Statistical inference on random dot product graphs: a survey[EB/OL]. https://arxiv.org/abs/1709.05454

[24] Li W T, Mao K Z, Zhang H et al. Selection of Gabor filters for improved texture feature extraction[C], 361-364(2010).

[25] Trudeau R J[M]. Introduction to graph theory(2013).

[26] Unnikrishnan R, Pantofaru C, Hebert M. Toward objective evaluation of image segmentation algorithms[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 929-944(2007).

[27] Shi J B, Malik J. Normalized cuts and image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 888-905(2000).

[28] Bengio Y, Paiement J, Vincent P et al. Out-of-sample extensions for LLE, Isomap, MDS, eigenmaps, and spectral clustering[C], 177-184(2003).

[29] Fujita A, Takahashi D Y, Patriota A G. A non-parametric method to estimate the number of clusters[J]. Computational Statistics & Data Analysis, 73, 27-39(2014).

[30] Fu W, Perry P O. Estimating the number of clusters using cross-validation[J]. Journal of Computational and Graphical Statistics, 29, 162-173(2020).

[31] Cheng G, Zhou P C, Han J W. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 7405-7415(2016).

[33] Yan D H, Huang L, Jordan M I. Fast approximate spectral clustering[C], 907-916(2009).

[34] Wang R, Nie F P, Yu W Z. Fast spectral clustering with anchor graph for large hyperspectral images[J]. IEEE Geoscience and Remote Sensing Letters, 14, 2003-2007(2017).

[35] Song Y Q, Chen W Y, Bai H J et al. Parallel spectral clustering[M]. Daelemans W, Goethals B, Morik K. Machine learning and knowledge discovery in databases, 5212, 374-389(2008).