Detection and classification of mesoscale atmospheric phenomena above sea in SAR imagery

Topouzelis, Konstantinos; Kitsiou, Dimitra

doi:10.1016/j.rse.2015.02.006

Cited by 34 publications

(22 citation statements)

References 46 publications

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“…For this analysis, images were segmented into groups of pixels with similar spectral and spatial characteristics. A multiresolution segmentation algorithm that was previously used in several oceanic applications [21,35,36]. eCognition Developer 9.1 software [37] was used with the following multiresolution segmentation parameters: size 20, shape 0.5, and compactness 0.5.…”

Section: Geobia -Coastline Detectionmentioning

confidence: 99%

“…For example, the acquired spatial data resolution via a manned aircraft ranges from 10-100 cm when the satellite system provides a resolution greater than 50 cm [12]. Datasets produced by UAV-based remote sensing have such a high spatial resolution (2-5 cm) that characteristics and changes of the landscape, such as coastal morphology, coastal zones, and beach morphological characteristics, can be mapped in detail in two (2D) and three dimensions (3D) [3,8,20,21]. However, such small changes are not distinguishable at the spatial resolutions generally obtained using manned aircraft and satellite systems.…”

Section: Introductionmentioning

confidence: 99%

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Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

Παπακωνσταντίνου

Topouzelis

Pavlogeorgatos

2016

IJGI

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113

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Abstract:Spatial data acquisition is a critical process for the identification of the coastline and coastal zones for scientists involved in the study of coastal morphology. The availability of very high-resolution digital surface models (DSMs) and orthophoto maps is of increasing interest to all scientists, especially those monitoring small variations in the earth's surface, such as coastline morphology. In this article, we present a methodology to acquire and process high resolution data for coastal zones acquired by a vertical take off and landing (VTOL) unmanned aerial vehicle (UAV) attached to a small commercial camera. The proposed methodology integrated computer vision algorithms for 3D representation with image processing techniques for analysis. The computer vision algorithms used the structure from motion (SfM) approach while the image processing techniques used the geographic object-based image analysis (GEOBIA) with fuzzy classification. The SfM pipeline was used to construct the DSMs and orthophotos with a measurement precision in the order of centimeters. Consequently, GEOBIA was used to create objects by grouping pixels that had the same spectral characteristics together and extracting statistical features from them. The objects produced were classified by fuzzy classification using the statistical features as input. The classification output classes included beach composition (sand, rubble, and rocks) and sub-surface classes (seagrass, sand, algae, and rocks). The methodology was applied to two case studies of coastal areas with different compositions: a sandy beach with a large face and a rubble beach with a small face. Both are threatened by beach erosion and have been degraded by the action of sea storms. Results show that the coastline, which is the low limit of the swash zone, was detected successfully by both the 3D representations and the image classifications. Furthermore, several traces representing previous sea states were successfully recognized in the case of the sandy beach, while the erosion and beach crests were detected in the case of the rubble beach. The achieved level of detail of the 3D representations revealed new beach characteristics, including erosion crests, berm zones, and sand dunes. In conclusion, the UAV SfM workflow provides information in a spatial resolution that permits the study of coastal changes with confidence and provides accurate 3D visualizations of the beach zones, even for areas with complex topography. The overall results show that the presented methodology is a robust tool for the classification, 3D visualization, and mapping of coastal morphology.

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Section: Geobia -Coastline Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

Παπακωνσταντίνου

Topouzelis

Pavlogeorgatos

2016

IJGI

Self Cite

113

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“…SAR images (apart from normalization) were selected for the study of atmospheric phenomena in sea surface [1]. This was ideal for examining the proposed methods in a complex sea environment because the main challenge was to reveal phenomena not visible before normalization.…”

Section: Data Setmentioning

confidence: 99%

“…SAR imagery has proved very effective for observing, measuring and quantifying oceanographic phenomena such as fronts, waves, eddies, winds, storms, oil spills, algae blooms, currents, and boundary layer rolls [1,2]. The ability of SAR sensors in retrieving data in almost all weather conditions, independently of sunlight surface, is extremely important for oceanographic studies.…”

Section: Introductionmentioning

confidence: 99%

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Incidence angle normalization of Wide Swath SAR data for oceanographic applications

2016

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A backscattering trend in the range direction of the signal received by Synthetic Aperture Radar (SAR) in Wide Swath (WS) mode results in a progressive reduction of brightness over images from near to far range, which affects the detection and classification of sea surface features on wide swath SAR images. The aim of the present paper is to investigate methods for limiting the issue of Normalized Radar Cross-Section (NRCS or σ 0 ) variation due to the incidence angle. Two sensor independent functions are investigated: a theoretical backscattering shape function derived from a minimum wind speed and an empirical range fit of NRCS against incidence angle θ. The former method exploits only the modeled NRCS values while the latter only the image content. The results were compared with the squared cosine correction, the most widely applied method for normalization, using six newly developed comparison factors. The results showed that the cosine squared normalization has the lowest efficiency while the proposed methods have similar behaviors and comparable results. Nevertheless, after the log-transformation and summation of the comparison factors, it was clearly shown that theoretical normalization performance is superior to the empirical one since it has the highest accuracy and requires less computational time.

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A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel‐1 wave mode

Wang

Mouche

Tandeo

et al. 2019

Geoscience Data Journal

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The Sentinel‐1 mission is part of the European Copernicus program aiming at providing observations for Land, Marine and Atmosphere Monitoring, Emergency Management, Security and Climate Change. It is a constellation of two (Sentinel‐1 A and B) Synthetic Aperture Radar (SAR) satellites. The SAR wave mode (WV) routinely collects high‐resolution SAR images of the ocean surface during day and night and through clouds. In this study, a subset of more than 37,000 SAR images is labelled corresponding to ten geophysical phenomena, including both oceanic and meteorologic features. These images cover the entire open ocean and are manually selected from Sentinel‐1A WV acquisitions in 2016. For each image, only one prevalent geophysical phenomenon with its prescribed signature and texture is selected for labelling. The SAR images are processed into a quick‐look image provided in the formats of PNG and GeoTIFF as well as the associated labels. They are convenient for both visual inspection and machine learning‐based methods exploitation. The proposed dataset is the first one involving different oceanic or atmospheric phenomena over the open ocean. It seeks to foster the development of strategies or approaches for massive ocean SAR image analysis. A key objective was to allow exploiting the full potential of Sentinel‐1 WV SAR acquisitions, which are about 60,000 images per satellite per month and freely available. Such a dataset may be of value to a wide range of users and communities in deep learning, remote sensing, oceanography and meteorology.

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Detection and classification of mesoscale atmospheric phenomena above sea in SAR imagery

Cited by 34 publications

References 46 publications

Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

Coastline Zones Identification and 3D Coastal Mapping Using UAV Spatial Data

Incidence angle normalization of Wide Swath SAR data for oceanographic applications

A labelled ocean SAR imagery dataset of ten geophysical phenomena from Sentinel‐1 wave mode

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