SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area

Elfadaly, Abdelaziz; Abate, Nicodemo; Masini, Nicola; Lasaponara, Rosa

doi:10.3390/rs12162611

Cited by 30 publications

(23 citation statements)

References 58 publications

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“…x,y g x,y g m Z (20) where, m is the number of pixels of the target LCB. The LCB edge features that carry the characteristics of human vision were extracted, and the template matching coefficient θLTM was configured.…”

Section: Lcb Divisionmentioning

confidence: 99%

“…Based on genetic algorithm and quadrature mirror filter, Smutnicki [19] put forward a visual effect optimization method to overcome the low resolution and severe information loss of classical landscape images, and proved that the method can output high resolution images and extract desirable image features. Elfadaly et al [20] combined the histogram equalization with the imaging mechanism into a hybrid strategy: Firstly, the salient areas in each fuzzy landscape image are equalized; the contrast difference vectors of the salient areas are acquired by histogram specification; the difference vectors were optimized to reconstruct an enhanced image.…”

Section: Introductionmentioning

confidence: 99%

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Feature Extraction and Image Retrieval of Landscape Images Based on Image Processing

Li¹,

Han²,

Wang³

et al. 2020

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Facing the existing digital image libraries on landscape, researchers need to urgently solve a challenging problem: how to realize rational management and accurate retrieval of landscape images that contain feature information like hierarchy, layout, color system, and color matching. For accurate organization and labeling of landscape Images, this paper presents a novel method for feature extraction and image retrieval of landscape images based on image processing. Firstly, a color quantization process was designed for landscape images, and used to analyze the color composition and color space pattern (CSP) of such images. Next, the existing methods, which are suitable for the extraction of color features from landscape Images, were briefly reviewed, and the basic flows of our improved algorithm and division method of landscape color blocks (LCBs) were explained. Finally, the retrieval performance of landscape images was improved by matching of weighted color blocks of regional landscape, based on the multi-dimensional color eigenvectors of landscape image. The experimental results demonstrate the effectiveness of our algorithm. The research results shed light on the feature extraction from other types of color images.

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Section: Lcb Divisionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Image Retrieval of Landscape Images Based on Image Processing

Li¹,

Han²,

Wang³

et al. 2020

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“…The reconnaissance of typical archeological marks (such as crop, shadow, and soil/damp marks) using radar is more complex than optical imaging [13]. The discriminability of archeological marks is a complex issue mainly because linked to very subtle and nonpermanent signals [14], [15] only evident in specific surface conditions, (as for example, vegetation type and phenology, moisture content, etc.) that can be captured using a multitemporal data processing [15].…”

Section: Introductionmentioning

confidence: 99%

“…1). For both S-1 and S-2, the processing was addressed to feature enhancement and data reduction to capture archeological features and characterize their spatial and temporal variations in terms of both size and distribution [11], [14]. This was performed using Javascript in GEE to obtain Bands combination, RGB composition, Spectral Signature and Backscattered observation, and PCA [11].…”

Section: Introductionmentioning

confidence: 99%

On the Use of Google Earth Engine and Sentinel Data to Detect “Lost” Sections of Ancient Roads. The Case of Via Appia

Lasaponara

Abate

Masini

2022

IEEE Geosci. Remote Sensing Lett.

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The currently available tools and services as open and free cloud resources to process big satellite data opened up a new frontier of possibilities and applications including archeological research. These new research opportunities also pose several challenges to be faced, as, for example, the data processing and interpretation. This letter is about the assessment of different methods and data sources to support a visual interpretation of EO imagery. Multitemporal Sentinel 1 and Sentinel 2 data sets have been processed to assess their capability in the detection of buried archeological remains related to some lost sections of the ancient Via Appia road (herein selected as case study). The very subtle and nonpermanent features linked to buried archeological remains can be captured using multitemporal (intra-and inter-year) satellite acquisitions, but this requires strong hardware infrastructures or cloud facilities, today also available as open and free tools as Google Earth Engine (GEE). In this study, a total of 2948 Sentinel 1 and 743 Sentinel 2 images were selected (from February 2017 to August 2020) and processed using GEE to enhance and unveil archeological features. Outputs obtained from both Sentinel 1 and Sentinel 2 have been successfully compared with in situ analysis and high-resolution Google Earth images.

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“…Specifically for heritage management, while earth observation has been well introduced in the literature, the majority of these studies are focused on the exploitation of a limited number of high-resolution optical sensors [ 18 , 19 , 20 , 21 ] with some noticeable exceptions [ 22 , 23 ]. Even fewer studies are found in the literature to be dealing with the use of radar sensors at a medium resolution [ 24 , 25 ]. Indeed, the use of the Copernicus Sentinel-1 radar sensor [ 26 ] is still limited for heritage management applications, with exceptions in the application of interferometric synthetic-aperture radar (In-SAR) analysis [ 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Change Detection Analysis of Vertical Sprawl over Limassol City Centre and Amathus Archaeological Site in Cyprus during 2015–2020 Using the Sentinel-1 Sensor and the Google Earth Engine Platform

Αγαπίου

2021

Sensors

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Urban sprawl can negatively impact the archaeological record of an area. In order to study the urbanisation process and its patterns, satellite images were used in the past to identify land-use changes and detect individual buildings and constructions. However, this approach involves the acquisition of high-resolution satellite images, the cost of which is increases according to the size of the area under study, as well as the time interval of the analysis. In this paper, we implemented a quick, automatic and low-cost exploration of large areas, for addressing this purpose, aiming to provide at a medium resolution of an overview of the landscape changes. This study focuses on using radar Sentinel-1 images to monitor and detect multi-temporal changes during the period 2015–2020 in Limassol, Cyprus. In addition, the big data cloud platform, Google Earth Engine, was used to process the data. Three different change detection methods were implemented in this platform as follow: (a) vertical transmit, vertical receive (VV) and vertical transmit, horizontal receive (VH) polarisations pseudo-colour composites; (b) the Rapid and Easy Change Detection in Radar Time-Series by Variation Coefficient (REACTIV) Google Earth Engine algorithm; and (c) a multi-temporal Wishart-based change detection algorithm. The overall findings are presented for the wider area of the Limassol city, with special focus on the archaeological site of “Amathus” and the city centre of Limassol. For validation purposes, satellite images from the multi-temporal archive from the Google Earth platform were used. The methods mentioned above were able to capture the urbanization process of the city that has been initiated during this period due to recent large construction projects.

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SAR Sentinel 1 Imaging and Detection of Palaeo-Landscape Features in the Mediterranean Area

Cited by 30 publications

References 58 publications

Feature Extraction and Image Retrieval of Landscape Images Based on Image Processing

Feature Extraction and Image Retrieval of Landscape Images Based on Image Processing

On the Use of Google Earth Engine and Sentinel Data to Detect “Lost” Sections of Ancient Roads. The Case of Via Appia

Multi-Temporal Change Detection Analysis of Vertical Sprawl over Limassol City Centre and Amathus Archaeological Site in Cyprus during 2015–2020 Using the Sentinel-1 Sensor and the Google Earth Engine Platform

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