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Research Articles

High-spatial-resolution remote sensing image segmentation using adaptive watershed-driven joint MDEDNet

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Pages 10713-10742 | Received 26 Aug 2021, Accepted 23 Jan 2022, Published online: 08 Mar 2022

References

  • Abdollahi A, Pradhan B, Alamri A. 2021. RoadVecNet: a new approach for simultaneous road network segmentation and vectorization from aerial and Google earth imagery in a complex urban set-up. Gisci Remote Sens. 58(7):1151–1174.
  • Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Süsstrunk S. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell. 34(11):2274–2282.
  • Achanta R, Susstrunk S. 2017. Superpixels and Polygons Using Simple Non-iterative Clustering. IEEE Conf Comput Vis Pattern Recognit. p. 4895–4904.
  • Arbeláez P, Maire M, Fowlkes C, Malik J. 2011. Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell. 33(5):898–916.
  • Awaisu M, Li L, Peng J, Zhang J. 2019. Fast superpixel segmentation with deep features. Adv Comput Graph. p. 410–416
  • Bai Y, Chen X. 2016. Efficient structure-preserving superpixel segmentation based on minimum spanning tree. IEEE International Conference on Multimedia and Expo. p. 1–6.
  • Biswajeet P, Husam AHA, Maher IS, Ivor T, Abdullah MA. 2020. Unseen land cover classification from high-resolution orthophotos using integration of zero-shot learning and convolutional neural networks. Remote Sensing. 12(10):1676.
  • Cadieu C, Hong H, Yamins D, Pinto N, Ardila D, Solomon E, Majaj N, DiCarlo J. 2014. Deep neural networks rival the representation of primate IT cortex for core visual object recognition. PLoS Comput Biol. 10(12):e1003963.
  • Chen J, Li Z, Huang B. 2017. Linear spectral clustering superpixel. IEEE Trans Image Process. 26(7):3317–3330.
  • Comaniciu D, Meer P. 2002. Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Machine Intell. 24(5):603–619.
  • Cui X, Y. Deng G, Yang G, Wu S. 2014. An improved image segmentation algorithm based on the watershed transform. IEEE 7th Joint International Information Technology and Artificial Intelligence Conference. p. 28–431.
  • Deng L, Geoffrey H, Brian K. 2013. New types of deep neural network learning for speech recognition and related applications: an overview. International Conference on Acoustics, Speech, and Signal Processing. p. 26–31.
  • Deng R, Shen C, Liu S, Wang H, Liu X. 2018. Learning to predict crisp boundaries. Comput Vis Pattern Recognit. 11210:562–578.
  • Drever L, Roa W, McEwan A, Robinson D. 2007. Iterative threshold segmentation for PET target volume delineation. Med Phys. 34(4):1253–1265.
  • Fan Y, Chen W, Pan Z. 2008. An adaptive watermarking algorithm in DWT domain based on multi-scale morphological gradient. Int Conf Comput Sci Softw Eng. p. 738–741.
  • Fang Z, Yu X, Wu C, Chen D, Jia T. 2018. Superpixel segmentation using weighted coplanar feature clustering on RGBD images. Appl Sci. 8(6):902.
  • Farabet C, Couprie C, Najman L, Lecun Y. 2013. Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell. 35(8):1915–1929.
  • Hou M, Yin J, Ge J, Li Y, Feng Q. 2020. Land cover remote sensing classification method of alpine wetland region based on random forest algorithm. Trans Chin Soc Agric Machinery. 51:220–227.
  • Huang L, Yao B, Chen P, Ren A, Xia Y. 2020. Superpixel segmentation method of high resolution remote sensing images based on hierarchical clustering. J Infrared Millim Waves. 39:263–272.
  • Huang X, Zhang L. 2009. A comparative study of spatial approaches for urban mapping using hyperspectral ROSIS images over Pavia City, northern Italy. Int J Remote Sens. 30(12):3205–3221.
  • Huo F, Yang L, Wang D, Sun B. 2017. Bloch quantum artificial bee colony algorithm and its application in image threshold segmentation. Signal Image Video Process. 11:1–8.
  • Ji S, Wei S, Lu M. 2019. Fully convolutional networks for multi-source building extraction from an open aerial and satellite imagery dataset. IEEE Trans Geosci Remote Sensing. 57(1):574–586.
  • Lei T, Jia X, Liu T, Liu S, Meng H, Nandi AK. 2019. Adaptive morphological reconstruction for seeded image segmentation. IEEE Trans Image Process. 28(11):5510–5523.
  • Lei T, Jia X, Zhang Y, Liu S, Meng H, Nandi AK. 2019. Superpixel-based fast fuzzy C-means clustering for color image segmentation. IEEE Trans Fuzzy Syst. 27(9):1753–1766.
  • Li C, Zhao L, Sun S. 2010. An adaptive morphological edge detection algorithm based on image fusion. 3rd International Congress on Image and Signal Processing. p. 1072–1076.
  • Liu Y, Cheng M, Hu X, Wang K, Bai X. 2017. Richer convolutional features for edge detection. IEEE Conf Comput Vis Pattern Recognit. p.5872–5881.
  • Liu Y, Lew MS. 2016. Learning relaxed deep supervision for better edge detection. IEEE Conf Comput Vis Pattern Recognit. p.231–240.
  • Long J, Shelhamer E, Darrell T. 2014. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell. 39:640–651.
  • Mahaman SC, Pierre-Henri C, Karim K, Basel S, Mohamed AM. 2017. Adaptive strategy for superpixel-based region-growing image segmentation. J Electron Imaging. 26:1.
  • Nakamura K, Byung-Woo H. 2016. Fast-convergence superpixel algorithm via an approximate optimization. J of Electron Imag. 25(5):053035.
  • Rémi G, Vinh T, Nicolas P. 2018. Robust superpixels using color and contour features along linear path. Comput Vision Image Understanding. 170:1–13.
  • Ren X, Malik J. 2003. Learning a classification model for segmentation. Proceedings Ninth IEEE International Conference on Computer Vision.p. 10–17.
  • Rother C, Kolmogorov V, Blake A. 2004. GrabCut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph. 23(3):309–314.
  • Shen J, Hao X, Liang Z, Yu L, Wang W, Shao L. 2016. Real-time superpixel segmentation by DBSCAN clustering algorithm. IEEE Trans on Image Process. 25(12):5933–5942.
  • Shen W, Wang X, Wang Y, Bai X, Zhang Z. 2015. Deepcontour: a deep convolutional feature learned by positive-sharing loss for contour detection. IEEE Conf Comput Vis Pattern Recognit. p. 3982–3991.
  • Shen Y, Yuan Y, Peng J, Chen X, Yang Q. 2020. River extraction from remote sensing images in cold and arid regions based on deep learning. Trans Chin Soc Agric Machinery. 51:192–201.
  • Sun JG, Liu J, Zhao LY. 2008. Clustering algorithms research. J Softw. 19(1):48–61.
  • Wang Y, Zhao X, Li Y, Huang K. 2019. Deep crisp boundaries: from boundaries to higher-level tasks. IEEE Trans Image Process. 28(3):1285–1298.
  • Wu MY, Chen L. 2015. Image recognition based on deep learning. Chinese Automation Congress. p. 27–29.
  • Xia G, Bai X, Ding J, Zhu Z, Belongie S, Luo J, Datcu M, Pelillo M, Zhang L. 2018. DOTA: a large-scale dataset for object detection in aerial images. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
  • Xie S, Tu Z. 2015. Holistically-Nested Edge Detection. IEEE International Conference on Computer Vision. p. 1395–1403.
  • Xu G, Su Z, Wang J, Yin Y, Shen Y. 2008. An adaptive morphological filter based on multiple structure and multi-scale elements. Int Conf Comput Sci Softw Eng. p. 399–403.
  • Yamini B, Sabitha R. 2017. Image steganalysis: adaptive color image segmentation using otsu’s method. J Comput Theor Nanosci. 14(9):4502–4507.
  • Yu K, Chen X, Shi F, Zhu W, Zhang B, Xiang D. 2016. A novel 3D graph cut based co-segmentation of lung tumor on PET-CT images with Gaussian mixture models. SPIE Medical Imaging. International Society for Optics and Photonics.
  • Zhang J, Zhang L. 2017. A watershed algorithm combining spectral and texture information for high resolution remote sensing image segmentation. Geomat Inf Ence Wuhan Univ. 42:449–455.
  • Zhang SP, Jiang W, Satoh S. 2018. Multilevel thresholding color image segmentation using a modified artificial bee colony algorithm. IEICE Trans Inf Syst. E101.D(8):2064–2071.
  • Zhang Y, Bai X, Fan R, Wang Z. 2018. Deviation-sparse fuzzy c-means with neighbor information constraint. IEEE Trans Fuzzy Syst. 27(1):185–199.
  • Zhao ZQ, Zheng P, Xu ST, Wu XD. 2015. Object detection with deep learning: a review. IEEE Trans Networks Learning Syst. p. 27–29.