Bands are equal-shape matrices (a list). TasseledCap: brightness,
greenness, wetness with the sensor coefficients of
RStoolbox::tasseledCap. BandPca: principal components
(covariance or, spca, correlation; scores of the centred, scaled
pixels; loadings signed so their largest entry is positive).
MnfTransform: minimum noise fraction with the shift-difference noise
estimate (Green et al. 1988). PanSharpen: Brovey, IHS (intensity
replaced by mean/sd-matched pan) or PCA (PC1 replaced by pan stretched to
PC1's range) on bands already on the pan grid.
TopographicCorrection: cos, avgcos, Minnaert, C and statistical
corrections from the illumination \(\cos i\). EstimateHaze and
RadiometricCorrection: dark-object haze and DN to radiance, TOA or
DOS / COST surface reflectance (Chavez 1988, 1996). CloudMask
(NDTCI) and CloudShadowMask (the cloud mask shifted by the sun
geometry). SpectralAngleClassify, GaussianMlClassify
(quadratic discriminant, priors from training proportions),
KmeansClassify (Lloyd from given centres), AtgpEndmembers
(Ren and Chang 2003), FparFromNdvi (Los et al. 2000) and
Tvdi (Sandholt et al. 2002). Classes and indices are 1-based.
Identical to the Python arm morie.fn.rsimage.
Usage
TasseledCap(bands, sensor = "landsat8oli")
BandPca(bands, spca = FALSE, n_comp = NULL)
MnfTransform(bands, n_comp = NULL)
PanSharpen(ms, pan, method = "brovey", rgb = 1:3)
TopographicCorrection(
bands,
slope,
aspect,
sun_azimuth,
sun_zenith,
method = "C"
)
EstimateHaze(dn, dark_prop = 0.01, max_slope = TRUE)
RadiometricCorrection(
dn,
gain,
offset,
method = "apref",
esun = NULL,
sun_elevation = 90,
distance = 1,
haze_dn = NULL,
haze_band = 1L,
wavelengths = NULL,
atmosphere = NULL,
radiometric_bits = 8,
clamp = TRUE,
view_zenith = 1
)
CloudMask(blue, tir, threshold = 0.2)
CloudShadowMask(cloud, shift, dark = NULL, quantile = NULL)
SpectralAngleClassify(bands, endmembers)
GaussianMlClassify(bands, train, labels, priors = NULL)
KmeansClassify(bands, centers, max_iter = 100L)
AtgpEndmembers(bands, n)
FparFromNdvi(
ndvi,
ndvi_min = 0.05,
ndvi_max = 0.95,
fpar_min = 0.001,
fpar_max = 0.95,
method = "average"
)
Tvdi(ndvi, lst, n_bins = 10L, ndvi_range = NULL)Arguments
- bands, ms
List of equal-shape band matrices.
- sensor
Tasseled-cap sensor.
- spca
Use the correlation matrix.
- n_comp
Number of components.
- pan
Panchromatic matrix on the same grid.
- method
Method name.
- rgb
Indices of the red, green and blue bands.
- slope, aspect
Slope and aspect matrices (radians).
- sun_azimuth, sun_zenith
Sun angles (radians).
- dn
DN matrix (haze) or list of DN matrices.
- dark_prop
Dark-object proportion.
- max_slope
Use the steepest rise of the smoothed histogram.
- gain, offset
Radiance rescaling per band.
- esun
Exo-atmospheric solar irradiance per band.
- sun_elevation
Sun elevation (degrees).
- distance
Earth-sun distance (AU).
- haze_dn
Haze DN (one value, or one per band for
"sdos").- haze_band
Index of the haze band (
"dos","costz").- wavelengths
Band centre wavelengths.
- atmosphere
Relative scattering class, or
NULLto choose.- radiometric_bits
Radiometric resolution.
- clamp
Clamp reflectance to the unit interval (radiance to >= 0).
- view_zenith
Sensor view zenith (degrees) for
"costz"'s view transmittance \(\cos\theta_v\); 1 asRStoolbox, 0 for strict nadir.- blue, tir
Blue and thermal matrices.
- threshold
NDTCI threshold.
- cloud
Logical cloud mask.
- shift
Shift
c(dx, dy)in cells (dy north).- dark
Optional darkness matrix (e.g. band sum).
- quantile
Darkness quantile.
- endmembers
Endmember spectra (rows).
- train
Training pixels (rows, bands as columns).
- labels
Training labels.
- priors
Class priors (sorted label order).
- centers
Initial centres (rows).
- max_iter
Maximum iterations.
- n
Number of endmembers.
- ndvi, lst
NDVI and land surface temperature vectors.
- ndvi_min, ndvi_max, fpar_min, fpar_max
FPAR scaling constants.
- n_bins
Number of NDVI bins.
- ndvi_range
NDVI range for the bins.
References
Crist, E. P. (1985). A TM tasseled cap equivalent transformation for reflectance factor data. Remote Sensing of Environment 17, 301-306.
Baig, M. H. A., Zhang, L., Shuai, T. and Tong, Q. (2014). Derivation of a tasselled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters 5, 423-431.
Green, A. A., Berman, M., Switzer, P. and Craig, M. D. (1988). A transformation for ordering multispectral data in terms of image quality. IEEE Transactions on Geoscience and Remote Sensing 26, 65-74.
Teillet, P. M., Guindon, B. and Goodenough, D. G. (1982). On the slope-aspect correction of multispectral scanner data. Canadian Journal of Remote Sensing 8, 84-106.
Chavez, P. S. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24, 459-479.
Ren, H. and Chang, C.-I. (2003). Automatic spectral target recognition in hyperspectral imagery. IEEE Transactions on Aerospace and Electronic Systems 39, 1232-1249.
Los, S. O. et al. (2000). A global 9-yr biophysical land surface dataset from NOAA AVHRR data. Journal of Hydrometeorology 1, 183-199.
Sandholt, I., Rasmussen, K. and Andersen, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment 79, 213-224.