SAR and PolSAR images (R6) — SARImage • SITSAR

SARImage stores a SAR image as an array of dimension nrow x ncol x layers. Two types are supported:

"polsar": a complex array whose p = m(m+1)/2 layers are the distinct entries of the $m \times m$ covariance matrix of each pixel, in the order $C_{11}, \ldots, C_{mm}, C_{12}, C_{13}, \ldots$ (for $m = 3$: HH, HV, VV, HH.HV*, HH.VV*, HV.VV*), as in SanFrancisco150;
"intensity": a real (or real-valued complex) array whose layers are intensity channels.

The class offers cropping, Pauli colour composition, plotting, local (moving-window) features for classification, entropy maps and conversion to tibbles.

Public fields

data: The image array.
type: "polsar" or "intensity".
channels: Names of the intensity channels.
looks: Nominal number of looks (optional, NULL if unknown).

Active bindings

nrow: Number of rows (read-only).
ncol: Number of columns (read-only).
m: Number of channels; matrix order for PolSAR images (read-only).

Methods

`SARImage$new()`

Create a SAR image.

Usage

SARImage$new(data, type = NULL, looks = NULL, channels = NULL)

Arguments

data: Array nrow x ncol x layers (a matrix is treated as a single intensity channel).
type: "polsar" or "intensity". Guessed when NULL: complex arrays with m(m+1)/2 layers (m >= 2) and non-zero imaginary parts are PolSAR images.
looks: Nominal number of looks.
channels: Names of the intensity channels.

`SARImage$intensity()`

Intensity of one channel.

Usage

SARImage$intensity(channel = 1)

Arguments

channel: Channel index or name.

`SARImage$intensities()`

All intensity channels as a real array.

Usage

SARImage$intensities()

`SARImage$crop()`

Crop the image.

Usage

SARImage$crop(rows, cols)

Arguments

rows, cols: Row and column indices to keep.

`SARImage$pixel()`

Observation at a pixel: the Hermitian covariance matrix (PolSAR) or the vector of intensities.

Usage

SARImage$pixel(row, col)

Arguments

row, col: Pixel position.

`SARImage$window()`

Observations inside a square window centred at a pixel (truncated at the borders). Returns an n x layers matrix.

Usage

SARImage$window(row, col, window = 3)

Arguments

row, col: Pixel position.
window: Odd window side.

`SARImage$pauli()`

Pauli RGB composition scaled to [0, 1] (PolSAR images with three channels); for other images the first three intensities are used.

Usage

SARImage$pauli(stretch = 0.98, gamma = 0.5)

Arguments

stretch: Upper quantile used for linear stretching.
gamma: Gamma correction exponent.

`SARImage$plot()`

Plot the image (Pauli composition or one channel in dB).

Usage

SARImage$plot(type = c("pauli", "intensity"), channel = 1, main = NULL, ...)

Arguments

type: "pauli" or "intensity".
channel: Channel for type = "intensity".
main: Title.
...: Passed to graphics::rasterImage().

`SARImage$features()`

Local features for classification; see sar_features().

Usage

SARImage$features(window = 3, type = c("all", "gaussian", "wishart"))

Arguments

window: Odd window side.
type: Which features: "all", "gaussian" or "wishart".

`SARImage$entropy_map()`

Entropy map; see entropy_map().

Usage

SARImage$entropy_map(...)

Arguments

...: Passed to entropy_map().

`SARImage$as_tibble()`

Convert to a tibble with one row per pixel.

Usage

SARImage$as_tibble()

`SARImage$print()`

Print a summary.

Usage

SARImage$print(...)

Arguments

...: Ignored.

`SARImage$clone()`

The objects of this class are cloneable with this method.

Usage

SARImage$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

img <- SARImage$new(SanFrancisco150, looks = 4)
img
#> <SARImage> 150 x 150 PolSAR (3 x 3 covariance)
#>   channels: HH, HV, VV
#>   looks: 4
#>   mean HH  = 0.1735
#>   mean HV  = 0.04224
#>   mean VV  = 0.147
small <- img$crop(1:40, 1:40)
dim(small$intensities())
#> [1] 40 40  3
small$pixel(1, 1)
#>                            [,1]                       [,2]
#> [1,] 0.0049587982+0.0000000000i 0.0006074079-0.0001119103i
#> [2,] 0.0006074079+0.0001119103i 0.0003967038+0.0000000000i
#> [3,] 0.0113060614-0.0013223464i 0.0011964096-0.0005374640i
#>                         [,3]
#> [1,] 0.01130606+0.001322346i
#> [2,] 0.00119641+0.000537464i
#> [3,] 0.02823210+0.000000000i
head(small$features(window = 3))
#> # A tibble: 6 × 20
#>     row   col mean_HH  mean_HV mean_VV   cov_HH_HH cov_HH_HV cov_HH_VV cov_HV_HV
#>   <int> <int>   <dbl>    <dbl>   <dbl>       <dbl>     <dbl>     <dbl>     <dbl>
#> 1     1     1 0.00596 0.000472  0.0233 0.00000666    8.51e-8   2.08e-5   9.50e-9
#> 2     2     1 0.00588 0.000528  0.0224 0.00000536   -2.69e-8   1.74e-5   1.68e-8
#> 3     3     1 0.00526 0.000556  0.0206 0.00000457    6.27e-8   1.60e-5   1.43e-8
#> 4     4     1 0.00485 0.000490  0.0190 0.00000213    1.74e-8   6.28e-6   3.16e-8
#> 5     5     1 0.00427 0.000413  0.0177 0.000000935   3.21e-8   1.79e-6   1.56e-8
#> 6     6     1 0.00400 0.000462  0.0159 0.000000755  -1.35e-8   4.27e-7   4.03e-8
#> # ℹ 11 more variables: cov_HV_VV <dbl>, cov_VV_VV <dbl>, c11 <dbl>, c22 <dbl>,
#> #   c33 <dbl>, c12_re <dbl>, c12_im <dbl>, c13_re <dbl>, c13_im <dbl>,
#> #   c23_re <dbl>, c23_im <dbl>
# \donttest{
img$plot()

# }