Shoreline Extraction Based on an Active Connection Matrix (ACM) Image Enhancement Strategy

Monitoring 23-year of shoreline changes of the Zengwun Estuary in Southern Taiwan using time-series Landsat data and edge detection techniques

More than 80% of sandy beaches in Taiwan have been experiencing a severe recession, although the sediment discharge of rivers in Taiwan is significantly higher than the world average producing almost 2% of global fluvial sediment discharge. This contradiction is primarily due to the widespread constructions of reservoirs and intensive anthropogenic activities in coastal regions. In addition, coasts are particularly vulnerable to hazards due to climate change, such as sea-level rise, as they are located at the transition zone of terrestrial and marine environments. Along with the fact that Taiwan is an island and is one of the most climate-vulnerable regions globally, coastal management and sustainability are nationally critical topics, especially considering the ongoing reformation and legislation of Taiwan's coastal conservation laws. As stated in the Sustainable Development Goals (SDGs) goal 14, accurate and continuous shoreline positions information is essential for coastal conservation. However, by reviewing previous global studies and projects commissioned by the Taiwanese government aiming at monitoring shoreline changes, they usually exhibit several limitations, such as limited band selections or conservative band ratio-derived water indices, relying on either manual digitization or simple thresholding methods, focusing on either artificial or smoothly shaped coasts, and using images acquired at considerably different tidal height levels. Therefore, in the present study, a subpixel shoreline extraction approach based on a sustainable cross-generation dataset and a robust edge detection algorithm is proposed. This approach is exemplified by the Zengwun River Estuary located in southwestern Taiwan-Taiwan's most critical coastal preservation region. By quantitatively analyzing the resultant time-series shoreline positions from 1999 to 2021, several hotspots of shoreline recession have been identified: an extreme erosional rate up to -69.4 m year^-1 is revealed in the northern sand bank; while the offshore sand bar demonstrates an overall landward retreat rate of -35.4 ± 1.24 m year^-1.

Remote Sensing-Based Automatic Detection of Shoreline Position: A Case Study in Apulia Region

Remote sensing and satellite imagery have become commonplace in efforts to monitor and model various biological and physical characteristics of the Earth. The land/water interface is a continually evolving landscape of high scientific and societal interest, making the mapping and monitoring thereof particularly important. This paper aims at describing a new automated method of shoreline position detection through the utilization of Synthetic Aperture Radar (SAR) images derived from European Space Agency satellites, specifically the operational SENTINEL Series. The resultant delineated shorelines are validated against those derived from video monitoring systems and in situ monitoring; a mean distance of 1 and a maximum of 3.5 pixels is found.

Shoreline Extraction in SAR Image Based on Advanced Geometric Active Contour Model

Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy.

RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images

Image enhancement (IE) technology can help enhance the brightness of remote-sensing images to obtain better interpretation and visualization effects. Convolutional neural networks (CNN), such as the Low-light CNN (LLCNN) and Super-resolution CNN (SRCNN), have achieved great success in image enhancement, image super resolution, and other image-processing applications. Therefore, we adopt CNN to propose a new neural network architecture with end-to-end strategy for low-light remote-sensing IE, named remote-sensing CNN (RSCNN). In RSCNN, an upsampling operator is adopted to help learn more multi-scaled features. With respect to the lack of labeled training data in remote-sensing image datasets for IE, we use real natural image patches to train firstly and then perform fine-tuning operations with simulated remote-sensing image pairs. Reasonably designed experiments are carried out, and the results quantitatively show the superiority of RSCNN in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) over conventional techniques for low-light remote-sensing IE. Furthermore, the results of our method have obvious qualitative advantages in denoising and maintaining the authenticity of colors and textures.