Feature extraction of dual-pol SAR imagery for sea ice image segmentation

2012 ◽  
Vol 38 (3) ◽  
pp. 352-366 ◽  
Author(s):  
Peter Yu ◽  
A. K. Qin ◽  
David A. Clausi
Author(s):  
Zhenzhen Yang ◽  
Pengfei Xu ◽  
Yongpeng Yang ◽  
Bing-Kun Bao

The U-Net has become the most popular structure in medical image segmentation in recent years. Although its performance for medical image segmentation is outstanding, a large number of experiments demonstrate that the classical U-Net network architecture seems to be insufficient when the size of segmentation targets changes and the imbalance happens between target and background in different forms of segmentation. To improve the U-Net network architecture, we develop a new architecture named densely connected U-Net (DenseUNet) network in this article. The proposed DenseUNet network adopts a dense block to improve the feature extraction capability and employs a multi-feature fuse block fusing feature maps of different levels to increase the accuracy of feature extraction. In addition, in view of the advantages of the cross entropy and the dice loss functions, a new loss function for the DenseUNet network is proposed to deal with the imbalance between target and background. Finally, we test the proposed DenseUNet network and compared it with the multi-resolutional U-Net (MultiResUNet) and the classic U-Net networks on three different datasets. The experimental results show that the DenseUNet network has significantly performances compared with the MultiResUNet and the classic U-Net networks.


2021 ◽  
Author(s):  
Grant J. Macdonald ◽  
Stephen F. Ackley ◽  
Alberto M. Mestas-Nuñez

Abstract. Polynyas are key sites of ice production during the winter and are important sites of biological activity and carbon sequestration during the summer. The Amundsen Sea Polynya (ASP) is the fourth largest Antarctic polynya, has recorded the highest primary productivity and lies in an embayment of key oceanographic significance. However, knowledge of its dynamics, and of sub-annual variations in its area and ice production, is limited. In this study we primarily utilize Sentinel-1 SAR imagery, sea ice concentration products and climate reanalysis data, along with bathymetric data, to analyze the ASP over the period November 2016–March 2021. Specifically, we analyze (i) qualitative changes in the ASP's characteristics and dynamics, and quantitative changes in (ii) summer polynya area, (iii) winter polynya area and ice production. From our analysis of SAR imagery we find that ice produced by the ASP becomes stuck in the vicinity of the polynya and sometimes flows back into the polynya, contributing to its closure and limiting further ice production. The polynya forms westward off a persistent chain of grounded icebergs that are located at the site of a bathymetric high. Grounded icebergs also influence the outflow of ice and facilitate the formation of a 'secondary polynya' at times. Additionally, unlike some polynyas, ice produced by the polynya flows westward after formation, along the coast and into the neighboring sea sector. During the summer and early winter, broader regional sea ice conditions can play an important role in the polynya. The polynya opens in all summers, but record-low sea ice conditions in 2016/17 cause it to become part of the open ocean. During the winter, an average of 78 % of ice production occurs in April–May and September–October, but large polynya events often associated with high winds can cause ice production throughout the winter. While passive microwave data or daily sea ice concentration products remain key for analyzing variations in polynya area and ice production, we find that the ability to directly observe and qualitatively analyze the polynya at a high temporal and spatial resolution with Sentinel-1 imagery provides important insights about the behavior of the polynya that are not possible with those datasets.


2020 ◽  
pp. 17-23
Author(s):  
Neeraj Kumari ◽  
Ashutosh Kumar Bhatt ◽  
Rakesh Kumar Dwivedi ◽  
Rajendra Belwal

Image segmentation is an essential and critical step in huge number of applications of image processing. Accuracy of image segmentation influence retrieved information for further processing in classification and other task. In image segmentation algorithms, a single segmentation technique is not sufficient in providing accurate segmentation results in many cases. In this paper we are proposing a combining approach of image segmentation techniques for improving segmentation accuracy. As a case study fruit mango is selected for classification based on surface defect. This classification method consists of three steps: (a) image pre-processing, (b) feature extraction and feature selection and (c) classification of mango. Feature extraction phase is performed on an enhanced input image. In feature selection PCA methodology is used. In classification three classifiers BPNN, Naïve bayes and LDA are used. Proposed image segmentation technique is tested on online dataset and our own collected images database. Proposed segmentation technique performance is compared with existing segmentation techniques. Classification results of BPNN in training and testing phase are acceptable for proposed segmentation technique.


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