文献类型： 外文期刊
作者： Dang, Xiaoyan 1 ; Wu, Yumei 1 ; Fan, Wei 1 ; Zhang, Shengmao 1 ;
作者机构： 1.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Key Lab East China Sea & Ocean Fishery Resources, Minist Agr PR China, Shanghai 200090, Peoples R China
2.Shanghai Ocean Univ, Coll Marine Sci, Shanghai 201306, Peoples R China
关键词： SAR;high resolution;sea ice segmentation;Otsu method;k-mean method
期刊名称：2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
ISSN： 2153-6996
年卷期： 2016 年
页码：
收录情况： SCI
摘要： The analysis of high resolution (6m) radar data was carried out on the Antarctic sea ice segmentation. This paper presented two methods for Synthetic Aperture Radar (SAR) sea ice image segmentation: k-means clustering method and threshold value method (binary). First, the speckle noise in SAR image was filtered and segmented by two methods. Then the results were compared. It was found that both methods could distinguish the crack and small ice, and these two sea ice distribution results were almost similar. The difference between two methods was: image of threshold value method contained disordered spots and isolated small ice, while k-mean method outcome had clear segmentation boundary and ensured completely segmentation region. So the result from the k-mean clustering method was better. In order to reflect the advantage of high resolution image, the image processed by k-mean clustering method was contrast with Antarctic sea ice concentration data, which was received from Advanced Microwave Scanning Radiometer 2. It is fully proved that the high resolution radar data can provide the location and the size of sea ice distribution more clearly and more accurately.
- 相关文献
作者其他论文 更多>>
-
Effects of western boundary currents and sea surface temperature anomalies on interannual variability of chub mackerel abundance in the Northwest Pacific
作者：Li, Jiasheng;Zhou, Weifeng;Dai, Yang;Tang, Fenghua;Wu, Yumei;Zhang, Heng;Fan, Xiumei;Cui, Xuesen;Li, Jiasheng
关键词：Chub mackerel; Abundance; Kuroshio; Oyashio; Sea surface temperature anomaly; Northwest Pacific
-
Comparative Analysis of Prediction Models for Trawling Grounds of the Argentine Shortfin Squid Illex argentinus in the Southwest Atlantic High Seas Based on Vessel Position and Fishing Log Data
作者：Xiang, Delong;Sun, Yuyan;Zhu, Hanji;Wang, Jianhua;Huang, Sisi;Zhang, Shengmao;Zhang, Famou;Zhang, Heng;Xiang, Delong;Sun, Yuyan;Huang, Sisi;Zhang, Shengmao;Zhang, Famou;Zhang, Heng;Xiang, Delong;Sun, Yuyan;Huang, Sisi;Zhang, Shengmao;Zhang, Famou;Zhang, Heng;Zhu, Hanji;Wang, Jianhua
关键词：Southwest Atlantic;
Illex argentinus ; AIS; deep learning; fishing ground prediction -
Computation and analysis of phenotypic parameters of Scylla paramamosain based on YOLOv11-DYPF keypoint detection
作者：Wu, Chong;Zhang, Shengmao;Wang, Wei;Wu, Zuli;Yang, Shenglong;Chen, Wei;Wu, Chong;Zhang, Shengmao;Zhang, Shengmao
关键词：Scylla paramamosain; Mud crab; Keypoint detection; YOLOv11; Multi-scale fusion; Dynamic upsampling; Support vector regression (SVR)
-
Evaluation Method for Artificial Reefs Based on Multi-Object Tracking
作者：Wu, Zuli;Song, Yifan;Cui, Xuesen;Zhang, Shengmao;Quan, Weimin;Shi, Yongchuang;Xiong, Xinquan;Li, Penglong;Wu, Zuli;Cui, Xuesen;Zhang, Shengmao;Quan, Weimin;Shi, Yongchuang;Song, Yifan;Xiong, Xinquan;Li, Penglong
关键词：artificial reef; sonar images; multi-object tracking (MOT) algorithm; three-dimensional position evaluation
-
Enhancing Landmark Point Detection in Eriocheir Sinensis Carapace with Differentiable End-to-End Networks
作者：Wu, Chong;Wang, Shuxian;Zhang, Shengmao;Zheng, Hanfeng;Wang, Wei;Yang, Shenglong;Wu, Chong;Zhang, Shengmao;Zheng, Hanfeng
关键词：Eriocheir sinensis; Chinese mitten crab; landmark detection; Gaussian heatmaps; deep convolutional neural network; DSNT module; generalization ability; power consumption
-
Assessment of Fish Biomass and Distribution in a Nuclear Power Plant's Water Intake Zone Using Acoustic and Trawl Methods
作者：Wu, Zuli;Song, Yunpeng;Zhao, Guoqing;Shi, Yongchuang;Wu, Yumei;Zhang, Shengmao;Wu, Zuli;Zhao, Guoqing;Shi, Yongchuang;Wu, Yumei;Zhang, Shengmao;Song, Yunpeng
关键词：resource density; acoustic assessment; dominant species; spatiotemporal distribution
-
A Lightweight Deep Learning Model for Forecasting the Fishing Ground of Purpleback Flying Squid (Sthenoteuthis oualaniensis) in the Northwest Indian Ocean
作者：Zhang, Shengmao;Chen, Junlin;Han, Haibin;Tang, Fenghua;Cui, Xuesen;Shi, Yongchuang;Zhang, Shengmao;Chen, Junlin;Han, Haibin
关键词：deep learning; lightweight; Northwest Indian Ocean; remote sensing data;
Sthenoteuthis oualaniensis
