The RADARSAT-2 SAR processor

Thompson, Alison; McLeod, I.H.

doi:10.5589/m04-016

Cited by 10 publications

(6 citation statements)

References 2 publications

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“…These are strong indications that the sole use of radiometric variables to differentiate the oil slick type is indeed intricate, as well as suggests that by using the Size Information one has a somewhat better chance in differentiating seeps from spills. The CFS-Merit resulted from the analysis of DNs showed the worse value (0.059), thus agreeing with literature that does not recommend the use of DN values to cross-compare time series of SAR images [11,12,46].…”

Section: Data Treatment Processessupporting

confidence: 69%

“…This corroborates the quality of excellence of the explored dataset, i.e., CBOS-MOD. DN is again the worst, matching expectations [11,12,46]. A slightly better discrimination is observed while using only the Size Information.…”

Section: Correlation Matrixsupporting

confidence: 58%

“…The Discriminant Functions results for separate analysis of Size Information (best) and of DNs variables (worst) are presented in Table 11. A two-fold positive outcome is evident: (1) The relevancy of Size Information to distinguish oil seeps from oil spills on the sea surface of the Campeche Bay region; and (2) The corroboration with literature (e.g., [11,12,46] that DN images should not be used to compare time-series of SAR imagery. Table 2) as best to distinguish the oil slick type, a separate analysis explores the only two basic morphological features originally present in the CBOS-Data: area and perimeter.…”

Section: Oil Slick Classification Algorithmmentioning

confidence: 54%

“…Although literature suggests not using images given in DNs to compare SAR images [11,12,46], herein, an assessment explores DNs to quantify the consequences of using DNs. Only Frost filter and Antenna Pattern Compensation (APC) were applied.…”

Section: Attribute Selection Methodsmentioning

confidence: 99%

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Exploratory Data Analysis of Synthetic Aperture Radar (SAR) Measurements to Distinguish the Sea Surface Expressions of Naturally-Occurring Oil Seeps from Human-Related Oil Spills in Campeche Bay (Gulf of Mexico)

Carvalho

Minnett

Miranda

et al. 2017

IJGI

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An Exploratory Data Analysis (EDA) aims to use Synthetic Aperture Radar (SAR) measurements for discriminating between two oil slick types observed on the sea surface: naturally-occurring oil seeps versus human-related oil spills-the use of satellite sensors for this task is poorly documented in scientific literature. A long-term RADARSAT dataset (2008)(2009)(2010)(2011)(2012)) is exploited to investigate oil slicks in Campeche Bay (Gulf of Mexico). Simple Classification Algorithms to distinguish the oil slick type are designed based on standard multivariate data analysis techniques. Various attributes of geometry, shape, and dimension that describe the oil slick Size Information are combined with SAR-derived backscatter coefficients-sigma-(σ o ), beta-(β o ), and gamma-(γ o ) naught. The combination of several of these characteristics is capable of distinguishing the oil slick type with~70% of overall accuracy, however, the sole and simple use of two specific oil slick's Size Information (i.e., area and perimeter) is equally capable of distinguishing seeps from spills. The data mining exercise of our EDA promotes a novel idea bridging petroleum pollution and remote sensing research, thus paving the way to further investigate the satellite synoptic view to express geophysical differences between seeped and spilled oil observed on the sea surface for systematic use.

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Section: Data Treatment Processessupporting

confidence: 69%

Section: Correlation Matrixsupporting

confidence: 58%

Section: Oil Slick Classification Algorithmmentioning

confidence: 54%

Section: Attribute Selection Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Exploratory Data Analysis of Synthetic Aperture Radar (SAR) Measurements to Distinguish the Sea Surface Expressions of Naturally-Occurring Oil Seeps from Human-Related Oil Spills in Campeche Bay (Gulf of Mexico)

Carvalho

Minnett

Miranda

et al. 2017

IJGI

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“…The GF-3 ultra-fine stripmap mode uses the dual-receive technique to obtain 3 m resolution and 30 km swath simultaneously, which is exactly the same as RadarSat-2 [ 12 ]. The samples of dual-receive channels were interleaved by azimuth processing indicated in Figure 3 .…”

Section: Sar Algorithm Designmentioning

confidence: 99%

The GF-3 SAR Data Processor

Han

Ding

Zhong

et al. 2018

Sensors

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The Gaofen-3 (GF-3) data processor was developed as a workstation-based GF-3 synthetic aperture radar (SAR) data processing system. The processor consists of two vital subsystems of the GF-3 ground segment, which are referred to as data ingesting subsystem (DIS) and product generation subsystem (PGS). The primary purpose of DIS is to record and catalogue GF-3 raw data with a transferring format, and PGS is to produce slant range or geocoded imagery from the signal data. This paper presents a brief introduction of the GF-3 data processor, including descriptions of the system architecture, the processing algorithms and its output format.

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