Automated extraction of the Antarctic coastline using snakes

In this paper we present an automatic approach for coastline detection from images which is based on parametric active contours (snakes). Snakes require the definition of an energy functional that reflects the underlying coastline model. As for Antarctica, our application domain, the coastline appearance in the used optical images is heterogeneous. Therefore, a single model does not work equally well in all situations. On the basis of an up-to-date Landsat mosaic three different models are formulated that match a large part of the Antarctic coastline, i.e. the transition from ice shelf to water, from ice shelf to sea ice and from rocky terrain to water. For each of the three different cases the energy terms are optimized based on the radiometric properties of the adjacent regions as well as the curvature and the potential change-rate of the coastline itself. A supervised classification for the three classes ice, water and rocky terrain controls the whole process by choosing the most applicable model for a certain image region. With a view to the practical application the developed approach was integrated into a semiautomatic system, where the human operator supervises the optimization process of the contour and interactively corrects the results if the system fails

Edge enhancement algorithm based on the wavelet transform for automatic edge detection in SAR images

This paper presents a novel technique for automatic edge enhancement and detection in synthetic aperture radar (SAR) images. The characteristics of SAR images justify the importance of an edge enhancement step prior to edge detection. Therefore, this paper presents a robust and unsupervised edge enhancement algorithm based on a combination of wavelet coefficients at different scales. The performance of the method is first tested on simulated images. Then, in order to complete the automatic detection chain, among the different options for the decision stage, the use of geodesic active contour is proposed. The second part of this paper suggests the extraction of the coastline in SAR images as a particular case of edge detection. Hence, after highlighting its practical interest, the technique that is theoretically presented in the first part of this paper is applied to real scenarios. Finally, the chances of its operational capability are assessed.Peer ReviewedPostprint (published version

Using airborne laser altimetry to improve river flood extents delineated from SAR data

Flood extent maps derived from SAR images are a useful source of data for validating hydraulic models of river flood flow. The accuracy of such maps is reduced by a number of factors, including changes in returns from the water surface caused by different meteorological conditions and the presence of emergent vegetation. The paper describes how improved accuracy can be achieved by modifying an existing flood extent delineation algorithm to use airborne laser altimetry (LiDAR) as well as SAR data. The LiDAR data provide an additional constraint that waterline (land-water boundary) heights should vary smoothly along the flooded reach. The method was tested on a SAR image of a flood for which contemporaneous aerial photography existed, together with LiDAR data of the un-flooded reach. Waterline heights of the SAR flood extent conditioned on both SAR and LiDAR data matched the corresponding heights from the aerial photo waterline significantly more closely than those from the SAR flood extent conditioned only on SAR data

HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the coastline. In contrast to this, a human annotator will usually keep a mental map of both segmentation and delineation when performing manual coastline detection. To take into account this task duality, we therefore devise a new model to unite these two approaches in a deep learning model. By taking inspiration from the main building blocks of a semantic segmentation framework (UNet) and an edge detection framework (HED), both tasks are combined in a natural way. Training is made efficient by employing deep supervision on side predictions at multiple resolutions. Finally, a hierarchical attention mechanism is introduced to adaptively merge these multiscale predictions into the final model output. The advantages of this approach over other traditional and deep learning-based methods for coastline detection are demonstrated on a dataset of Sentinel-1 imagery covering parts of the Antarctic coast, where coastline detection is notoriously difficult. An implementation of our method is available at \url{https://github.com/khdlr/HED-UNet}.Comment: This work has been accepted by IEEE TGRS for publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Shoreline extraction based on an active connection matrix (ACM) image enhancement strategy

Coastal environments are facing constant changes over time due to their dynamic nature and geological, geomorphological, hydrodynamic, biological, climatic and anthropogenic factors. For these reasons, the monitoring of these areas is crucial for the safeguarding of the cultural heritage and the populations living there. The focus of this paper is shoreline extraction by means of an experimental algorithm, called J-Net Dynamic (Semeion Research Center of Sciences of Communication, Rome, Italy). It was tested on two types of image: a very high resolution (VHR) multispectral image (WorldView-2) and a high resolution (HR) radar synthetic aperture radar (SAR) image (Sentinel-1). The extracted shorelines were compared with those manually digitized for both images independently. The results obtained with the J-Net Dynamic algorithm were also compared with common algorithms, widely used in the literature, including theWorldView water index and the Canny edge detector. The results show that the experimental algorithm is more effective than the others, as it improves shoreline extraction accuracy both in the optical and SAR images

Potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection

This article investigates the potential of nonlocally filtered pursuit monostatic TanDEM-X data for coastline detection in comparison to conventional TanDEM-X data, i.e. image pairs acquired in repeat-pass or bistatic mode. For this task, an unsupervised coastline detection procedure based on scale-space representations and K-medians clustering as well as morphological image post-processing is proposed. Since this procedure exploits a clear discriminability of "dark" and "bright" appearances of water and land surfaces, respectively, in both SAR amplitude and coherence imagery, TanDEM-X InSAR data acquired in pursuit monostatic mode is expected to provide a promising benefit. In addition, we investigate the benefit introduced by a utilization of a non-local InSAR filter for amplitude denoising and coherence estimation instead of a conventional box-car filter. Experiments carried out on real TanDEM-X pursuit monostatic data confirm our expectations and illustrate the advantage of the employed data configuration over conventional TanDEM-X products for automatic coastline detection

Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

GeoAI-enhanced Techniques to Support Geographical Knowledge Discovery from Big Geospatial Data

abstract: Big data that contain geo-referenced attributes have significantly reformed the way that I process and analyze geospatial data. Compared with the expected benefits received in the data-rich environment, more data have not always contributed to more accurate analysis. “Big but valueless” has becoming a critical concern to the community of GIScience and data-driven geography. As a highly-utilized function of GeoAI technique, deep learning models designed for processing geospatial data integrate powerful computing hardware and deep neural networks into various dimensions of geography to effectively discover the representation of data. However, limitations of these deep learning models have also been reported when People may have to spend much time on preparing training data for implementing a deep learning model. The objective of this dissertation research is to promote state-of-the-art deep learning models in discovering the representation, value and hidden knowledge of GIS and remote sensing data, through three research approaches. The first methodological framework aims to unify varied shadow into limited number of patterns, with the convolutional neural network (CNNs)-powered shape classification, multifarious shadow shapes with a limited number of representative shadow patterns for efficient shadow-based building height estimation. The second research focus integrates semantic analysis into a framework of various state-of-the-art CNNs to support human-level understanding of map content. The final research approach of this dissertation focuses on normalizing geospatial domain knowledge to promote the transferability of a CNN’s model to land-use/land-cover classification. This research reports a method designed to discover detailed land-use/land-cover types that might be challenging for a state-of-the-art CNN’s model that previously performed well on land-cover classification only.Dissertation/ThesisDoctoral Dissertation Geography 201