RStoolbox 0.1.6
=====================================
Fixes:
* fix import issue: replace deprecated export from caret 

RStoolbox 0.1.5
=====================================
Changes:
* If the bandSet argument in `radCor()` is used to process only a subset of bands it will no longer return unprocessed bands along with processed bands. Instead only processed bands are returned.
* By default `superClass()` will now use dataType = 'INT2S' for classification maps to avoid issues with raster NA handling in INT1U
* Allow reading and importing from Landsat MSS MTL files with `readMeta()` and `stackMeta()` (@aszeitz, #7)

Fixes:
* fix readMeta time-stamp conversion now correctly set to GMT time (@mraraju, #12)
* radCor caused R to crash if bandSet was a single band 
* fix single RasterLayer capability for superClass
* spectralIndices now calculates *all* documented indices if specified to do so (@mej1d1, #6)
* unsuperClass predicted map now handles NAs properly
* pifMatch did not return adjusted image (@tmb3006, #13)

Deprecated:
* argument `norm` was dropped from rasterPCA, because it was effectively a duplicate of the standardized pca (spca) argument in the same function.

RStoolbox 0.1.4
=====================================
New:
* new function `validateMap()` for assessing map accuracy separately from model fitting, e.g. after majority or MMU filtering
* new function `getValidation()` to extract specific validation results of superClass objects (proposed by James Duffy)
* new spectral index NDVIc (proposed by Jeff Evans)
* new argument scaleFactor for `spectralIndices()` for calculation of EVI/EVI2 based on scaled reflectance values. 
* implemented dark object subtraction radCor(..,method='sdos') for Landsat 8 data (@BayAludra, #4)

Changes:
* superClass based on polygons now considers only pixels which have their center coordinate within a polygon  
* rasterCVA now returns angles from 0 to 360° instead of 0:45 by quadrant (reported by Martin Wegmann)
* improved dark object DN estimation based on maximum slope of the histogram in `estimateHaze` (@BayAludra, #4)

Fixes:
* superClass failed when neither valData or trainPartition was specified. regression introduced in 0.1.3 (reported by Anna Stephani)
* spectralIndices valid value range of EVI/EVI2 now [-1,1]
* radCor returned smallest integer instead of NA for some NA pixels
* fix 'sdos' for non-contiguous bands in radCor (@BayAludra, #4)


RStoolbox 0.1.3
=====================================
New:
* new logical argument `predict` for superClass. Disables prediction of full raster (validation is still conducted).
* new generic predict() function for superClass objects. Useful to separate model training and prediction. 
* new example data set (landcover training polygons) for lsat example data under /extdata/trainingPolygons.rds 

Fixes:
* fix histMatch for single layers (affected also 'ihs' pan-sharpening)
* fix superClass validation sampling for factors (character based factors could lead to wrong factor conversions and wrong validation results)
* improved handling of of training polygons with overlaps and shared borders in superClass
* improved checks and error messages for insufficient training polygons

RStoolbox 0.1.2
=====================================
New:
New model for superClass: maximum likelihood classification (model = "mlc")

Fixes:
* Restrict calculation of EVI/EVI2 to reflectance data (#3)
* Enforce valid value ranges in radCor: radiance: [0,+Inf], reflectance: [0,1]. Includes a new argument `clamp` to turn this on or off (on by default).

RStoolbox 0.1.1
=====================================
Added kernlab to suggested packages to be able to test \donttest{} examples

RStoolbox 0.1.0
=====================================
Initial release to CRAN (2015-09-05) with the following functions: 
 * classifyQA()
 * cloudMask()
 * cloudShadowMask()
 * coregisterImages()
 * decodeQA()
 * encodeQA()
 * estimateHaze()
 * fortify.raster()
 * fCover()
 * getMeta()
 * ggR()
 * ggRGB()
 * histMatch()
 * ImageMetaData()
 * normImage()
 * panSharpen()
 * pifMatch()
 * radCor()
 * rasterCVA()
 * rasterEntropy()
 * rasterPCA()
 * readEE()
 * readMeta()
 * readRSTBX()
 * readSLI()
 * rescaleImage()
 * rsOpts()
 * sam()
 * saveRSTBX()
 * spectralIndices()
 * stackMeta()
 * superClass()
 * tasseledCap()
 * topCor()
 * unsuperClass()
 * writeSLI() 
 
Included example data sets: 
* data(srtm)
* data(lsat)
* data(rlogo)


