Coastline Extraction from GF-3 SAR Images Using LKDACM and GMM Algorithms

Coastline detection using a Gaussian Mixture Model (GMM) applied to synthetic aperture radar (SAR) imagery is usually inaccurate due to the inherent noise of SAR data. In addition, the traditional active counter model is sensitive to the initial position of the contour line and requires a large number of iterations to converge to a solution. In this study, we first used the GMM algorithm to segment the SAR images and obtain a coarse land and sea segmentation map. This map is then used as the initial position for a subsequent active contour model. The K distribution was introduced into the local statistical active contour model to better model the SAR image. The Gaussian distribution-based local active contour model and the algorithm detailed in this paper were used to perform coastline extraction experiments on four SAR images. Four GF-3 SAR images with different modes were collected to validate the efficiency of the proposed method. The experimental results show that the coastline extraction methods from SAR images based on the GMM algorithm and the K distribution-based local statistical active contour model (LKDACM) overcame the shortcomings of the traditional active contour model to accurately and quickly detect coastlines, thus enabling the detection of coastline changes.

Chart Mining: A Survey of Methods for Automated Chart Analysis

Charts are useful communication tools for the presentation of data in a visually appealing format that facilitates comprehension. There have been many studies dedicated to chart mining, which refers to the process of automatic detection, extraction and analysis of charts to reproduce the tabular data that was originally used to create them. By allowing access to data which might not be available in other formats, chart mining facilitates the creation of many downstream applications. This paper presents a comprehensive survey of approaches across all components of the automated chart mining pipeline, such as (i) automated extraction of charts from documents; (ii) processing of multi-panel charts; (iii) automatic image classifiers to collect chart images at scale; (iv) automated extraction of data from each chart image, for popular chart types as well as selected specialized classes; (v) applications of chart mining; and (vi) datasets for training and evaluation, and the methods that were used to build them. Finally, we summarize the main trends found in the literature and provide pointers to areas for further research in chart mining.