Automatic Coastline Extraction Research Articles

Automatic Coastline Extraction Based on the Improved Instantaneous Waterline Extraction Method and Correction Criteria Using SAR Imagery

Coastlines with different morphologies form boundaries between the land and ocean, and play a vital role in tourism, integrated coastal zone management, and marine engineering. Therefore, determining how to extract the coastline from satellite images quickly, accurately, and intelligently without manual intervention has become a hot topic. However, the instantaneous waterline extracted directly from the image must be corrected to the coastline using the tide survey station data. This process is challenging due to the scarcity of tide stations. Therefore, an improved instantaneous waterline extraction method was proposed in this paper with an integrated Otsu threshold method, a region-growing algorithm, Canny edge detection, and a morphology operator. Based on SAR feature extraction and screening, the multi-scale segmentation method and KNN classification algorithms were used to achieve object-oriented automatic classification. According to different types of ground features, the correction criteria were presented and used in correcting the instantaneous waterline in biological coasts and undeveloped silty coasts. As a result, the accurate extraction of the coastline was accomplished in the area of the Yellow River Delta. The coastline was compared with that extracted from the GF-1 optical image. The result shows that the deviation degree was less than the field distance represented by three pixels.

Automatic coastline extraction through enhanced sea-land segmentation by modifying Standard U-Net

Sea-land segmentation (SLS) is an essential remote sensing task for various coastal and environmental studies such as coastline extraction, coastal erosion, coastal area monitoring, and ship or iceberg detection. This study aims at improving the SLS performance by modifying the Standard U-Net (SUN) model and developing an automatic coastline extraction framework. SUN generally has an acceptable performance in many applications. However, better SLS outputs are needed for reliable coastline extraction. In our proposed framework, we firstly analyzed three different input images, including Red-Green-Blue (RGB), Normalized Difference Water Index (NDWI), and Near-Infrared (NIR) images. Secondly, we modified the SUN architecture to improve the segmentation results. The main modifications are using different loss functions and two fusion methods for RGB and NIR images. The segmentation results were then passed into the subsequent automatic coastline extraction pipeline based on morphological operations and pixel connectivity analysis. The training and testing steps were accomplished utilizing a benchmark dataset of China’s coastal areas. Moreover, another dataset consisting of a time series of Landsat-8 imagery from the southern Caspian Sea coastlines was collected to evaluate coastline extraction efficiency. The results indicate that the proposed modifications could effectively enhance the performance of the SUN, with the most significant improvement to the Intersection over Union (IoU) score being as high as 1.68% and 8.95% in China and Caspian Sea datasets, respectively, while outperforming other state-of-the-art models including FC-DenseNet and DeepLabV3+.

Corrections to “Automatic Coastline Extraction and Changes Analysis Using Remote Sensing and GIS Technology”

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Automatic Coastline Extraction and Changes Analysis Using Remote Sensing and GIS Technology

This study highlights the coastline position changes of Qingdao coastal area from 2000 to 2019, using GIS and remote sensing technologies through Digital Shoreline Analysis System and LANDSAT images. Understanding the coastline movement by suitable method is an important challenge for this extremely dynamic coast. The shoreline changes were statistically measured using three techniques, namely; Linear Regression Rate, End Point Rate and Net Shoreline Movement. For the automatic coastline extraction, different methods were applied, but among them most suitable techniques is the canny edge algorithm technique, which gives the accurate result. The result show maximum accretion reached was 266.07m/yr, 2391.85m,124.47m/yr for End point rate, net shoreline movement and linear regression rate, respectively. While, the maximum erosion was −142.55m/yr, −1234.59m, −63.22m/yr for End point rate, net shoreline movement and linear regression rate, respectively. This paper hence presents the monitoring processes of coast and analyzing the coastline change by the use of geospatial techniques that would be helpful for the coastal planning and management of the Qingdao coast. The applicability of the proposed model is tested with other generic edge detection algorithms that include; Sobel, Prewitt, and Robert edge detection techniques and it was concluded that our model outperforming in accurately detecting the coastline.

THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES

Abstract. Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled "Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model – Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and water-body classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5th band) and GOKTURK-2 (4th band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies.

Comparison of support vector machine and object based classification methods for coastline detection

Abstract. Detection of coastline is an important procedure for management of coastal zones. According to the International Geographic Data Committee (IGDC), coastlines are one of the most important environmental heritages on the earth’s surface. In the coastal areas, main challenge is to understand the present coastline dynamics and to predict its future developments. Therefore the coastal zone monitoring is an essential process for sustainable coastal management and environmental protection. Shoreline extraction is an important issue for coastal zone monitoring. In this study, efficiency of two different methods for detection of coastline features from satellite images, which cover Lakeland region of Turkey, has been tested. Firstly, object based classification method (OBC) has been used to extract shoreline automatically. Developed process based rule set extracts coastline as a vector file from satellite imagery. As a second method, support vector machine (SVM) algorithm has been used to extract coastline. For the application of these two different methods, Landsat 8 data have been used. The results of these two automatic coastline extraction methods were compared with the results derived from manual digitization process. Random control points over the coastline were used in the evaluation. Results showed that both methods have a sub-pixel accuracy to detect coastline features from Landsat 8 imagery.

Automatic extraction of coastline by remote sensing technology based on SVM and auto-selection of training samples

The timely and accurate automatic extraction of coastline from satellite remote sensing imagery is one of the important applications of remote sensing technology and has great significance for management planning of the sea area.Because the spectral characteristics of coastal water are susceptible to regional environment,the traditional method of normalized difference water index(NDWI)threshold segmentation may easily misclassify water as land in the process of separation of land and water,which will seriously affect the accuracy of shoreline extraction.In this paper,on the basis of NDWI model,the authors proposed an automatic coastline extraction method based on classification sample auto-selection and support vector machine(SVM).Firstly,through the NDWI calculation and global threshold segmentation,the initial water distribution information is obtained.And then,the classification samples are selected automatically under the control of NDWI information.Thirdly,the water are separated from the land by using SVM classifier.The last step is to fill small terrestrial water body units and track coastline automatically.The experimental results show that this method can effectively enhance the capability of coastal water identification and improve the accuracy and automation of the coastline extraction from remote sensing imager.

AN ALGORITHM FOR COASTLINE DETECTION USING SAR IMAGES

Abstract. Coastal management requires rapid, up-to-date, and correct information. Thus, coastal movements have primary importance for coastal managers. For monitoring the change of shorelines, remote sensing data are some of the most important information and are utilized for differentiating any detections of change on shorelines. It is possible to monitor coastal changes by extracting the coastline from satellite images. In the literature most of the algorithms developed for optical images have been discussed in detail. In this study, an algorithm which extracts coastlines efficiently and automatically by processing SAR (Synthetic Aperture Radar) satellite images has been developed. A data set of ALOS Palsar image of Fine Beam Double (FBD) HH-HV polarized data has been used. PALSAR image has L-band data, and has a 14 MHz bandwidth and 34.3 degrees look angle. Data were acquired in ascending geometry. Ground resolution of PALSAR image was resampled to 15 m to amplitude image. Zonguldak city, lies on the northwest costs of Turkey, has been selected as the test area. An algorithm was developed for automatic coastline extraction from SAR images. The algorithm is encoded in a C__ environment. To verify the results the algorithm was applied on two PALSAR images gathered in two different date as 2007 and 2010. The results of automatic coastline extraction obtained from SAR images were compared to the results derived from manual digitizing. Random control points which are seen on each image were used. The average differences of selected points were calculated.

A Novel Algorithm for Coastline Fitting through a Case Study over the Bosphorus

Decrease of habitat, coastal erosion, and shoreline changes are recent issues for coastal management. In this study, an algorithm which extracts coastlines efficiently and automatically by processing low- or medium-resolution satellite images has been developed. The junction of sea and land is a common result yielded by the automatic coastline extraction method.In this study, CORONA (1963), IRS-1D (2000), and LANDSAT-7 (2001) satellite images for the same region in Istanbul, Turkey were used. A novel algorithm was developed for automatic coastline extraction. The algorithm is encoded in a C environment. The results of automatic coastline extraction obtained from different images were compared to the results derived from manual digitizing. Random control points which are seen on every image were used.The average differences of selected points were calculated. Obtained results from selected points were rendered as 3 pixels on the CORONA and IRS-1D images and as 2 pixels on the LANDSAT-7 image. These show that the differences are similar, although different images were used. On the other hand, the results are acceptable compared to manual digitizing.