Proposed workflow for improved Kauth–Thomas transform derivations

Yarbrough, Lance D.; Easson, Greg; Kuszmaul, J. S.

doi:10.1016/j.rse.2012.05.003

Cited by 20 publications

(19 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TCT coefficients, despite being image independent, are affected by the type of terrestrial surface they've been calculated for, therefore their application to a different type of region might be not appropriate and can affect the results of a multi-temporal analysis [3,29,35,36]. For this work, two sets of desert-adapted TCT for Landsat TOAR data were used.…”

Section: Landsat Datamentioning

confidence: 99%

A combined change detection procedure to study desertification using opensource tools

Zanchetta

Bitelli

2017

Open geospatial data, softw. stand.

View full text Add to dashboard Cite

Background and Methods: The paper presents a combination of two unsupervised techniques for change detection studies in arid and semi-arid areas. Among Remote Sensing change detection techniques, unsupervised approaches have the advantage of promptly producing a map of the change between two dates, but often the interpretation of the results is not straightforward, and requires further processing of the image. The aim of the research is to propose a new time effective and semi-automated reproducible technique in order to reduce the weakness of the unsupervised approach in change detection. Two techniques, Change Vector Analysis (CVA) and Maximum Autocorrelation Factor transform of Multivariate Alteration Detector components (MAD/MAF) are chosen to serve the purpose.

show abstract

Section: Landsat Datamentioning

confidence: 99%

A combined change detection procedure to study desertification using opensource tools

Zanchetta

Bitelli

2017

Open geospatial data, softw. stand.

View full text Add to dashboard Cite

show abstract

“…This was necessary in order to simulate the ground data to at-satellite broadband reflectance. As [25] argue, this step is necessary since the shape of transformation is dependent on the spectral response of the sensors. Therefore, sensors with dissimilar bandwidths may have significantly different feature space shapes, yet have coordinate axis directions similar to the original tasseled-cap space.…”

Section: Calculation Of N-space Coefficientsmentioning

confidence: 99%

“…The algorithms are based on the work of Kauth-Thomas [24], also known as Tasseled Cap transformation (K-T algorithm), in order to detect vegetation in satellite data. K-T algorithm is a widely used metric capable of capturing scene characteristics in related coordinate directions in a defined feature space [25]. This transformation reduces the satellite reflectance bands to three orthogonal indices called brightness, greenness and wetness [26].…”

Section: Introductionmentioning

confidence: 99%

“…However, since new sensors are deployed, the wavelengths used to define each of the spectral bands depend on the relative spectral response (RSR) filters of the sensor for each particular bandwidth. Therefore, the nature of the tasseled-cap space is similar for each sensor, yet refinements of the T-C space coefficients must be investigated for each sensor [25]. Several researchers [27][28][29][30] have been able to define the new linear coefficients of the T-C algorithm for a variety of sensors using satellite image data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Orthogonal Equations of Multi-Spectral Satellite Imagery for the Identification of Un-Excavated Archaeological Sites

et al. 2013

View full text Add to dashboard Cite

This paper aims to introduce new linear orthogonal equations for different satellite data derived from QuickBird; IKONOS; WorldView-2; GeoEye-1, ASTER; Landsat 4 TM and Landsat 7 ETM+ sensors, in order to enhance the exposure of crop marks. The latest are of significant value for the detection of buried archaeological features using remote sensing techniques. The proposed transformations, re-projects the initial VNIR bands of the satellite image, into a new 3D coordinate system where the first component is the so called "crop mark", the second component "vegetation" and the third component "soil". For the purpose of this study, a large ground spectral signature database has been explored and analyzed separately for each different satellite image. The narrow band reflectance has been re-calculated using the Relative Spectral Response filters of each sensor, and then a PCA analysis was carried out. Subsequently, the first three PCA components were rotated in order to enhance the detection of crop marks. Finally, all proposed transformations have been successfully evaluated in different existing archaeological sites and some interesting crop marks have been exposed.

show abstract

“…Some of these have been integrated into commercial remote-sensing processing software, such as PCI, ERDAS, and ENVI. Because the shape of the tasselled-cap-like space is dependent on the spectral response of the sensors and is a spectral enhancement method, the dominant land cover of a scene, the number of scenes, the number of random samples, the number of bands, and their bandwidth used for derivation of TCT parameters all affect the TCT parameters, the components, and the shape of the tasselled-cap-like space (Yarbrough, Easson, and Kuszmaul 2012). However, the above TCT parameters of these sensors are calculated based on all the visible, near-infrared, and shortwave infrared bands acquired by these sensors, yet the number of bands and the bandwidths of these sensors are not exactly the same.…”

Section: Introductionmentioning

confidence: 99%

Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images

Liu

Huang

et al. 2015

International Journal of Remote Sensing

View full text Add to dashboard Cite

Parameters for the tasselled cap transformation (TCT) have been derived for many sensors since 1976. There have been concerns about the comparability of TCT brightness (TCTB), greenness (TCTG), and wetness (TCTW) from different sensors because the number and bandwidth of spectral bands of the different sensors are not exactly the same and the derivation methods vary. In this research, comparisons between the TCT components derived from different combination images with a different number of spectral bands are considered. First, a new TCT based on a new data set from Landsat 8 Operational Land Imager top of atmosphere (OLI TOA) reflectance was developed. Then, a case study of the Yellow River Delta, China, demonstrated that TCT parameters derived from a Landsat 8 OLI TOA reference image from May 2013 were probably applicable to clear and nearly cloud-free images for spring, summer, and autumn over the Yellow River Delta. Finally, we compared the TCT components derived from selected bands of Landsat 8 OLI TOA reflectance images and those derived from images from other well-known moderateresolution worldwide remote-sensing data by statistical characteristics, correlation coefficients, the optimum index factor (OIF), and classification with a support vector machine. The result supports our conclusion (a) that two new shortwave bands (Bands 1 and 9) of the Landsat 8 OLI have little effect on derivations of the TCT components and their ability to classify land cover type and (b) that Bands 4-7 in Landsat multispectral scanner, Bands 1-4 in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Bands 1-4 in Systeme Probatoire d'Observation dela Tarre 5 are sufficient for deriving TCTB, TCTG, and TCTW components and mapping land cover.

show abstract

Proposed workflow for improved Kauth–Thomas transform derivations

Cited by 20 publications

References 12 publications

A combined change detection procedure to study desertification using opensource tools

A combined change detection procedure to study desertification using opensource tools

Orthogonal Equations of Multi-Spectral Satellite Imagery for the Identification of Un-Excavated Archaeological Sites

Comparison of tasselled cap transformations based on the selective bands of Landsat 8 OLI TOA reflectance images

Contact Info

Product

Resources

About