Coastline extraction and coastal area classification via SAR hybrid-polarimetry architecture

Paes, Rafael Lemos; Nunziata, Ferdinando; Migliaccio, Maurizio

doi:10.1109/igarss.2015.7326651

Cited by 10 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, he proposed a novel metric for dual-polarization coherent and incoherent synthetic aperture radar (SAR) data processing based on the correlation between co-polarization and cross-polarization channels, aiming at coastline extraction as well as unsupervised separation of land and ocean [102]. Except for the study of co-polarization, Paes extracted continuous coastlines using Hybrid Polarity SAR data [104]. In the follow-up study, in addition to the dual polarization, the research team also used the fully polarized SAR measurement to extract the coastline [105].…”

Section: Polarization Methodsmentioning

confidence: 99%

An Overview of Coastline Extraction from Remote Sensing Data

Zhou,

Wang,

Zheng

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

The coastal zone represents a unique interface between land and sea, and addressing the ecological crisis it faces is of global significance. One of the most fundamental and effective measures is to extract the coastline’s location on a large scale, dynamically, and accurately. Remote sensing technology has been widely employed in coastline extraction due to its temporal, spatial, and sensor diversity advantages. Substantial progress has been made in coastline extraction with diversifying data types and information extraction methods. This paper focuses on discussing the research progress related to data sources and extraction methods for remote sensing-based coastline extraction. We summarize the suitability of data and some extraction algorithms for several specific coastline types, including rocky coastlines, sandy coastlines, muddy coastlines, biological coastlines, and artificial coastlines. We also discuss the significant challenges and prospects of coastline dataset construction, remotely sensed data selection, and the applicability of the extraction method. In particular, we propose the idea of extracting coastlines based on the coastline scene knowledge map (CSKG) semantic segmentation method. This review serves as a comprehensive reference for future development and research pertaining to coastal exploitation and management.

show abstract

Section: Polarization Methodsmentioning

confidence: 99%

An Overview of Coastline Extraction from Remote Sensing Data

Zhou,

Wang,

Zheng

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…As a constituent aquatic ecosystems, the lakes as well as their surrounding areas, constitute extremely vital and dynamic biodiversity sites that needs monitoring, conservation, and protection due to its physical, economic, social and cultural values (Dellepiane et. al., 2004;Climate Change Impacts Study Committee, 2011;Paes et. al., 2015;Zormpas et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…More specifically, regarding SAR sensors, their broad spatial coverage, their high spatial resolution, their ability to acquire imagery regardless of the weather conditions and the time of the day, make their data a reliable source of information in the environmental research (Brisco, 2015;Parcharidis, 2015). As for the disadvantages, the methods and techniques of discerning water bodies with SAR imagery, are requiring computing hardware of high performance and the user's advanced skills for manual modifications (Paes et. al.…”

Section: Introductionmentioning

confidence: 99%

Multitemporal observation of Karla reservoir in Thessaly Greece utilizing SAR and optical remotely sensing imagery

Falaras¹,

Koilakou²,

Tsoukalas³

2020

EJG

View full text Add to dashboard Cite

Wetlands constitute areas with significant value and service offerings both in anthropogenic and natural environments. Taking into consideration the importance of studying wetland ecosystems and their changes, the aim of this paper is the observation of Lake Karla’s reservoir fluctuation, since its reconstitution in 2010. For this reason, annual and seasonal fluctuations of the reservoir were estimated, utilizing remote sensing Synthetic Aperture Radar (SAR) (Sentinel 1) and optical (Sentinel 2) imagery, as well as Landsat 5 imagery. For SAR imagery an image segmentation method with a dynamic threshold operator based on mean values is utilized, while for optical imagery the Normalized Difference Water Index (NDWI) is applied. The findings reveal changes in Karla’s reservoir, with its acreage being continually increased on average on an annual basis. Meanwhile, on a seasonal basis, the results indicate some variations in the reservoir, due to precipitation and irrigation purposes.

show abstract

“…Their algorithms implemented edge detection methods. Paes et al (2015) presented a method by using k-means clustering algorithm and Canny edge detection filter. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy unsupervised approach.…”

Section: Introductionmentioning

confidence: 99%

Extraction of Coastlines With Fuzzy Approach Using Sentinel-1 Sar Image

Demir

Kaynarca

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

show abstract

Coastline extraction and coastal area classification via SAR hybrid-polarimetry architecture

Cited by 10 publications

References 9 publications

An Overview of Coastline Extraction from Remote Sensing Data

An Overview of Coastline Extraction from Remote Sensing Data

Multitemporal observation of Karla reservoir in Thessaly Greece utilizing SAR and optical remotely sensing imagery

Extraction of Coastlines With Fuzzy Approach Using Sentinel-1 Sar Image

Contact Info

Product

Resources

About