Abstract
Remote sensing images have become one of the most important imaging resources recently. Thus, it is important to develop high-performance techniques to process and manipulate these images. On the other hand, image processing techniques are enhanced spatially based on neural networks. Deep learning is one of the most important techniques in use for computer vision tasks and has been deployed successfully to solve many tasks. But when dealing with remote sensing images, the deep learning method faces two main problems: the underfitting problem, because of the small amount of learning data and the unbalanced receptive field problem, because of the structural stereotype of the remote sensing images. In this paper, we propose to use a complex-valued neural network to segment high-resolution remote sensing images. The proposed network can deal with the problems of remote sensing images by using an ensemble of Complex-Valued Auto-Encoder. Based on an adaptive clustering technique, this network can be used to solve the multi-label segmentation problem of remote sensing images. The proposed method achieves state-of-the-art performance when evaluated on the ISPRS 2D dataset.
Similar content being viewed by others
References
Xin P, Jian Z (2018) High-resolution remote sensing image classification method based on convolutional neural network and restricted conditional random field. Remote Sens 10:920
Lu X, Yuan Y, Zheng X (2017) Joint dictionary learning for multispectral change detection. IEEE Trans Cybern 2:884–897
Voulodimos A, Doulamis N, Doulamis A (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:1–13
Wang P, Di J (2018) Deep learning-based object classification through multimode fiber via a CNN-architecture SpeckleNet. Appl Opt 57:8258–8263
Afif M, Ayachi R, Said Y, Pissaloux E, Atri M (2018) Indoor image recognition and classification via deep convolutional neural network. In: ICSETIT, pp 364–371
Ayachi R, Said Y, Atri M (2019) To perform road signs recognition for autonomous vehicles using cascaded deep learning pipeline. Artif Intell Adv 1:1–10
Zhao Z, Zheng P, Xu S, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212–3232
Ayachi R, Afif M, Said Y, Atri M (2019) Traffic signs detection for real-world application of an advanced driving assisting system using deep learning. Neural Process Lett 51:1–15
Hong C, Yu J, Zhang J, Jin X, Lee K (2018) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Ind Inform 15:3952–3961
Yu J, Tan M, Zhang H, Tao D, Rui Y (2019) Hierarchical deep click feature prediction for fine-grained image recognition. IEEE Trans Pattern Anal Mach Intell
Clough J, Oksuz I, Byrne N, Schnabel J, King A (2019) Explicit topological priors for deep-learning based image segmentation using persistent homology. In: ICIPMI, pp 16–28
Yu J, Tao D, Wang M, Rui Y (2014) Learning to rank using user clicks and visual features for image retrieval. IEEE Trans Cybern 45:767–779
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of the IEEE conference on vision and pattern recognition (CVPR), San Diego, CA, USA, pp 886–893
Cheung W, Hamarneh G (2007) N-sift: N-dimensional scale invariant feature transform for matching medical images. In: 2007 4th IEEE international symposium on biomedical imaging: from nano to macro, IEEE, pp 720–723
Biadgie Y, Sohn L (2014) Feature detector using adaptive accelerated segment test. In: 2014 international conference on information science & applications (ICISA), IEEE, pp 1–4
Karimi F, Sultana S, Babakan A, Suthaharan S (2019) An enhanced support vector machine model for urban expansion prediction. Comput Environ Urban Syst 75:61–75
Xu J, Lange L (2019) Power k-means clustering. In: ICML, pp 6921–6931
O’Brien R, Ishwaran H (2019) A random forests quantile classifier for class imbalanced data. Pattern Recognit 90:232–249
Hirose A (2003) Complex-valued neural networks: theories and applications, vol 5. World Scientific, Singapore
Socher R, Huval B, Bath B, Manning C, Ng A (2012) Convolutional-recursive deep learning for 3d object classification. In: Advances in neural information processing systems, pp 656–664
Farabet C, Couprie C, Najman L, LeCun Y (2012) Learning hierarchical features for scene labeling. IEEE Trans Pattern Anal Mach Intell 35(8):1915–1929
Pinheiro P, Collobert R (2014) Recurrent convolutional neural networks for scene labeling. In: 31st international conference on machine learning (ICML), No. CONF. 2014
Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3431–3440
Noh H, Hong S, Han B (2015) Learning deconvolution network for semantic segmentation. In: Proceedings of the IEEE international conference on computer vision, pp 1520–1528
Zeiler M, Fergus R (2014) Visualizing and understanding convolutional networks. In: European conference on computer vision, pp 818–833
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495
Pinheiro P, Lin T, Collobert R, Dollár P (2016) Learning to refine object segments. In: European conference on computer vision, pp 75–91
Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille A (2014) Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv preprint arXiv:1412.7062
Krähenbühl P, Koltun V (2011) Efficient inference in fully connected crfs with gaussian edge potentials. Adv Neural Inf Process Syst 24:109–117
Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille A (2017) Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Yu J, Yao J, Zhang J, Yu Z, Tao D (2019) Single pixel reconstruction for one-stage instance segmentation. arXiv preprint arXiv:1904.07426
Yu J, Zhu C, Zhang J, Huang Q, Tao D (2019) Spatial pyramid-enhanced NetVLAD with weighted triplet loss for place recognition. IEEE Trans Neural Netw Learn Syst 31(2):661–674
Zhang J, Yu J, Tao D (2018) Local deep-feature alignment for unsupervised dimension reduction. IEEE Trans Image Process 27:2420–2432
Hong C, Yu J, Wan J, Tao D, Wang M (2015) Multimodal deep autoencoder for human pose recovery. IEEE Trans Image Process 24:5659–5670
Hirose A (1994) Complex-valued neural networks, vol 400. Springer, Berlin
Chen S, McLaughlin S, Mulgrew B (1994) Complex-valued radial basic function network, part I: network architecture and learning algorithms. Signal Process 35(1):19–31
Savitha R, Suresh S, Sundararajan N (2009) A fully complex-valued radial basis function network and its learning algorithm. Int J Neural Syst 19:253–267
Wu J, Wang K, Shang Z, Xu J, Ding D, Li X, Yang G (2019) Oval shape constraint based optic disc and cup segmentation in fundus photographs. In: Proceedings of british machine vision conference (BMVC), Cardiff, UK, pp 1–11
Chen L, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587
Chen L, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801–818
Cheng W, Yang W, Wang M, Wang G, Chen J (2019) Context aggregation network for semantic labeling in aerial images. Remote Sens 11:1158
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Barr, M. A Novel Technique for Segmentation of High Resolution Remote Sensing Images Based on Neural Networks. Neural Process Lett 52, 679–692 (2020). https://doi.org/10.1007/s11063-020-10280-1
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11063-020-10280-1