Geological case studies related to RADARSAT-2

Singhroy, V.; Molch, Katrin

doi:10.5589/m04-050

Cited by 21 publications

(5 citation statements)

References 17 publications

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“…Application of Synthetic Aperture Radar (SAR) data and combinations of SAR and multispectral data have also been extensively studied within the context of improved landform recognition on a local scale (Madhavan et al, 1997;Singhroy and Molch, 2004). Different wavelengths of the SAR signal enable structural analysis of elements in specific size classes, while polarization angles are particularly sensitive for directional structures.…”

Section: Landform Mapping Based On Combined Data Sourcesmentioning

confidence: 99%

“…Moreover, SAR is cloud-penetrating while the strength of backscatter depends on the dielectric properties of surface materials (e.g. soil water content), and the copolarization sensitivity to surficial sediments, both improve the classification of exposed surficial sediments (Singhroy and Molch, 2004). Singhroy et al (2003) used fused RADARSAT with TM images and employed spectral classification for distinguishing surficial deposits, moisture conditions and vegetation cover, which facilitated the interpretation and delineation of terrain units within an area of about 4800 km 2 (Singhroy, 2000;Singhroy et al, 2003).…”

Section: Landform Mapping Based On Combined Data Sourcesmentioning

confidence: 99%

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The use of remote sensing in soil and terrain mapping — A review

et al. 2011

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Section: Landform Mapping Based On Combined Data Sourcesmentioning

confidence: 99%

Section: Landform Mapping Based On Combined Data Sourcesmentioning

confidence: 99%

The use of remote sensing in soil and terrain mapping — A review

et al. 2011

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“…Dierking and Haack (1998) demonstrated the capability of polarimetric L-band data acquired by the Danish airborne sensor EMISAR in August, 1997 in delineating the different lava flow and surrounding terrain as well as the identification of different lava facies within one flow. Singhroy et al,(2004) has provided interesting information of polarimetric signatures collected from CV-80 high resolution airborne SAR data. It is found that copolarised polarimetric signatures are significantly different for different surface material type such as sand-silt alluvium, clayey till, deltaic sediment and sedimentary textures played important role in generating different polarimetric signatures.…”

Section: Review Of Literaturementioning

confidence: 99%

Remote Sensing Applications

Liu¹,

Mason²

2009

Essential Image Processing and GIS for Remote Sensing

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Texture: Refers to the frequency of tonal changes in an image. Texture is produced by an aggregate of unit features, which may be too small to be clearly discernible individually on the image. It is a product of their individual shape, size, pattern, shadow and tone. By definition, texture is dependent on the scale. As the scale of the photograph is reduced the texture of a given object becomes progressively finer and eventually disappears. Some of the terms often used to describe relative texture values qualitatively are coarse, fine, medium, smooth, rough, etc., it is rather easier to distinguish various textural classes visually than in the digital-oriented techniques. Pattern:The pattern relates to the spatial arrangement of the objects. The repetition of certain general forms or relationships is characteristic of many objects, both natural and man made, and gives objects a pattern which aids the image interpreter in recognizing them. For example, interbedded sedimentary rocks typically gives an alternating tonal pattern which aids in their identification. Shape:Relates to the general form, configuration or out line of an individual object. Shape is one of the most important single factor for recognizing objects from images. For example, a railway line is usually readily distinguished from a highway or a kuchha road because its shape consists of long straight tangents and gentle curves as opposed to the shape of a highway. The shape of an object viewed from above may quite different from its profile view. However, the plan view of object is more important and sometimes conclusive indication of their structure, composition and function is possible. Size:The size of an objects can be important tool for its identification. Objects can be misinterpreted if their sizes are not evaluated properly. Although, the third dimension, i.e., height of the objects, is not readily measurable on satellite images, but valuable information can be derived from the shadows of the objects. Images with stereoscopic coverages, such as those from SPOT and CARTOSAT-1 & 2 provide information on third dimension (height). For planar objects, it is easier to calculate the areal dimensions on imagery, for example-alluvial fan, flood plain, etc.Shadows: are of importance to photo interpreters in two opposing respects (1) The outline or shape of a shadow affords a profile view of objects, which aids interpretation, and (2) objects within shadow reflect little light and are difficult to discern on photographs, which hinders interpretation.Association: It is one of the most helpful clues in identification of land forms. For example, a flood plain is associated with several fluvial features such as terraces, meanders, ox-bow lakes, abandoned channel, etc. Similarly, a sandy plain in a desert is associated with various types of sand dunes. Terrain ElementsIn addition to the image elements described above, the terrain elements listed below are also very useful for image interpretation. The terrain elements include drainage patterns, drainage dens...

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“…Co-polarized signatures were more useful overall than cross-polarized signatures, which has also been previously observed. Singhroy and Molch (2004) for example, found this to be the case for discrimination of various surficial materials, including: alluvium, marine sediments, and till, using fully polarimetric C-band airborne radar imagery over Ottawa, Canada. As mentioned previously however, in this analysis cross-polarized plots were also useful for identification of Anthropogenic and Wetland classes.…”

Section: Sample Statistics General Observations and Trendsmentioning

confidence: 92%

“…These surfaces had RMS surface heights between 2.5 and 4 cm, which are comparable to the dominant grain size of material observed in the Mixed Sediment class (i.e., pebbles: 0.4 to 6.4 cm in diameter). The ability to distinguish a number of different sediment types was also observed by Singhroy and Molch (2004) using shallow angle imagery (72°).…”

Section: Water and Substratesmentioning

confidence: 96%

Assessing Radarsat-2 polarimetric SAR for mapping shoreline cleanup and assessment technique (SCAT) classes in the Canadian Arctic

Banks¹

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Geological case studies related to RADARSAT-2

Cited by 21 publications

References 17 publications

The use of remote sensing in soil and terrain mapping — A review

The use of remote sensing in soil and terrain mapping — A review

Remote Sensing Applications

Assessing Radarsat-2 polarimetric SAR for mapping shoreline cleanup and assessment technique (SCAT) classes in the Canadian Arctic

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