research-article

MIFNet: Multi-Information Fusion Network for Sea-Land Segmentation

Authors:

Jinwen TianAuthors Info & Claims

ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing

Pages 24 - 29

https://doi.org/10.1145/3239576.3239578

Published: 16 June 2018 Publication History

Abstract

Sea-land segmentation is of great significance to coastline extraction and ship detection. Due to the complicated texture and intensity distribution of high resolution remote sensing images, traditional methods based on threshold and artificial features are difficult to perform well. This paper presents a new multi-information fusion network (MIFNet) based on convolutional neural network. MIFNet not only considers multi-scale edges and multi-scale segmentation information, but also introduces global context information, and fuses different scales and types of information through network learning. Experiments on a set of natural-colored images from Google Earth show that our model achieves better performance than the state-of-the-art methods.

References

[1]

H. Liu, K. C. Jezek. Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods{J}. International Journal of Remote Sensing, 2004, 25(5):937--958.

[2]

You X, Li W. A sea-land segmentation scheme based on statistical model of sea{C}// International Congress on Image and Signal Processing. IEEE, 2011:1155--1159.

[3]

Lin H, Shi Z, Zou Z. Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network{J}. Remote Sensing, 2017, 9(5):480.

[4]

Li R, Liu W, Yang L, et al. DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentation{J}. 2017.

[5]

Cheng D, Meng G, Cheng G, et al. SeNet: Structured Edge Network for Sea-Land Segmentation{J}. IEEE Geoscience & Remote Sensing Letters, 2017, 14(2):247--251.

[6]

Cheng D, Meng G, Xiang S, et al. FusionNet: Edge Aware Deep Convolutional Networks for Semantic Segmentation of Remote Sensing Harbor Images{J}. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2017, PP(99):1--15.

[7]

Bolei Zhou, Aditya Khosla, Agata Lapedriza, et al. Object Detectors Emerge in Deep Scene CNNs{J}. Computer Science, 2014.

[8]

Liu W, Rabinovich A, Berg A C. ParseNet: Looking Wider to See Better{J}. Computer Science, 2015.

[9]

Mcfeeters S K. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features{J}. International Journal of Remote Sensing, 1996, 17(7):1425--1432.

[10]

Kuleli T, Guneroglu A, Karsli F, et al. Automatic detection of shoreline change on coastal Ramsar wetlands of Turkey{J}. Ocean Engineering, 2011, 38(10):1141--1149.

[11]

Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation{C}// Computer Vision and Pattern Recognition. IEEE, 2015:3431--3440.

[12]

Yu F, Koltun V. Multi-Scale Context Aggregation by Dilated Convolutions{J}. 2015.

[13]

Noh H, Hong S, Han B. Learning Deconvolution Network for Semantic Segmentation{J}. 2015:1520--1528.

Digital Library

[14]

Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation{J}. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, PP(99):1--1.

[15]

Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation{M}// Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. Springer International Publishing, 2015:234--241.

[16]

Garcia-Garcia A, Orts-Escolano S, Oprea S, et al. A Review on Deep Learning Techniques Applied to Semantic Segmentation{J}. 2017.

[17]

Chen L C, Papandreou G, Kokkinos I, et al. Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs{J}. Computer Science, 2014(4):357--361.

[18]

Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs{J}. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2016, PP(99):1--1.

[19]

Zhao H, Shi J, Qi X, et al. Pyramid Scene Parsing Network{J}. 2016.

[20]

Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition{J}. Computer Science, 2014.

[21]

Ioffe S, Szegedy C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift{J}. 2015:448--456.

Digital Library

[22]

Nair V, Hinton G E. Rectified linear units improve restricted boltzmann machines{C}// International Conference on International Conference on Machine Learning. Omnipress, 2010:807--814

Digital Library

[23]

Xie S, Tu Z. Holistically-Nested Edge Detection{J}. International Journal of Computer Vision, 2015:1--16.

Digital Library

[24]

Jia, Yangqing, Shelhamer, et al. Caffe: Convolutional Architecture for Fast Feature Embedding{J}. 2014:675--678.

Digital Library

Cited By

Lu CWen YLi YMao QZhai Y(2024)Sea-land segmentation method based on an improved MA-Net for Gaofen-2 imagesEarth Science Informatics10.1007/s12145-024-01391-7Online publication date: 26-Jun-2024
https://doi.org/10.1007/s12145-024-01391-7
Jiang BAn XXu SChen Z(2022)Intelligent Image Semantic Segmentation: A Review Through Deep Learning Techniques for Remote Sensing Image AnalysisJournal of the Indian Society of Remote Sensing10.1007/s12524-022-01496-w51:9(1865-1878)Online publication date: 20-Jan-2022
https://doi.org/10.1007/s12524-022-01496-w
Chouhan MCorinne Veril DPrerana PAngappan K(2022)Gastric Cancer Diagnosis Using MIFNet Algorithm and Deep Learning TechniqueThird International Conference on Image Processing and Capsule Networks10.1007/978-3-031-12413-6_56(713-724)Online publication date: 29-Jul-2022
https://doi.org/10.1007/978-3-031-12413-6_56

Index Terms

MIFNet: Multi-Information Fusion Network for Sea-Land Segmentation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Scene understanding

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cover image ACM Other conferences

ICAIP '18: Proceedings of the 2nd International Conference on Advances in Image Processing

June 2018

261 pages

ISBN:9781450364607

DOI:10.1145/3239576

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Electronic Science and Technology of China: University of Electronic Science and Technology of China
Southwest Jiaotong University

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2018

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ICAIP '18

ICAIP '18: 2018 the 2nd International Conference on Advances in Image Processing

June 16 - 18, 2018

Chengdu, China

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Cited By

Lu CWen YLi YMao QZhai Y(2024)Sea-land segmentation method based on an improved MA-Net for Gaofen-2 imagesEarth Science Informatics10.1007/s12145-024-01391-7Online publication date: 26-Jun-2024
https://doi.org/10.1007/s12145-024-01391-7
Jiang BAn XXu SChen Z(2022)Intelligent Image Semantic Segmentation: A Review Through Deep Learning Techniques for Remote Sensing Image AnalysisJournal of the Indian Society of Remote Sensing10.1007/s12524-022-01496-w51:9(1865-1878)Online publication date: 20-Jan-2022
https://doi.org/10.1007/s12524-022-01496-w
Chouhan MCorinne Veril DPrerana PAngappan K(2022)Gastric Cancer Diagnosis Using MIFNet Algorithm and Deep Learning TechniqueThird International Conference on Image Processing and Capsule Networks10.1007/978-3-031-12413-6_56(713-724)Online publication date: 29-Jul-2022
https://doi.org/10.1007/978-3-031-12413-6_56

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