MIFNet
Author:
Affiliation:
1. School of Automation, Huazhong University of Science and Technology, Wuhan, China
Publisher
ACM Press
Reference24 articles.
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.
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1. Sea-land segmentation method based on an improved MA-Net for Gaofen-2 images;Earth Science Informatics;2024-06-26
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3. Intelligent Image Semantic Segmentation: A Review Through Deep Learning Techniques for Remote Sensing Image Analysis;Journal of the Indian Society of Remote Sensing;2022-01-20
4. Gastric Cancer Diagnosis Using MIFNet Algorithm and Deep Learning Technique;Third International Conference on Image Processing and Capsule Networks;2022
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