RStoolbox (version 0.1.1)

classifyQA: Classify Landsat8 QA Band

Description

extracts five classes from QA band: background, cloud, cirrus, snow and water.

Usage

classifyQA(img, type = c("background", "cloud", "cirrus", "snow", "water"),
  confLayers = FALSE, ...)

Arguments

img

RasterLayer. Landsat 8 OLI QA band.

type

Character. Classes which should be returned. One or more of c("background", "cloud", "cirrus","snow", "water").

confLayers

Logical. Return one layer per class classified by condidence levels, i.e. cloud:low, cloud:med, cloud:high.

...

further arguments passed to writeRaster

Value

• Returns a RasterLayer with maximal five classes: rr{ class value background 1L cloud 2L cirrus 3L snow 4L water 5L } Values outside of these classes are returned as NA. If confLayers = TRUE then a RasterStack with one layer per condition (except 'background') is returned, whereby each layer contains the confidence level of the condition. rr{ Confidence value low 1L med 2L high 3L }

Details

By default each class is queried for *high* confidence. See encodeQA for details. To return the different confidence levels per condition use confLayers=TRUE. This approach corresponds to the way LandsatLook Quality Images are produced by the USGS.

See Also

encodeQA decodeQA

Examples

library(raster)
qa <- raster(ncol = 100, nrow=100, val = sample(1:2^14,  10000))

## QA classes
qacs <- classifyQA(img = qa)
## Confidence levels
qacs_conf <- classifyQA(img = qa, confLayers = TRUE)