Imagery Data

Images are Rasters

  • The spatial raster data model represents the world with the continuous grid of cells (a.k.a. pixels)
  • This data model often refers to so-called regular grids, in which each cell has the same, constant size
  • Through its inherent model, this data naturally fits into the wide data structure
  • This connects directly to the previous lesson on wide vs long data
  • Emphasize that raster data is inherently a grid/matrix structure
  • Each pixel has a fixed location - this is why wide format makes sense
  • Regular grids are the foundation of most remote sensing data
  • We will focus on the regular grids only. However, several other types of grids exist, including rotated, sheared, rectilinear, and curvilinear grids (see Chapter 1 of Pebesma and Bivand (2023)).
Figure 3.1: Cell offset from origin (lower left)
Figure 3.2: Cell values (for example elevation)
Figure 3.3: Color representation of cell value

Types of raster data

  • Raster datasets usually represent continuous phenomena such as elevation, temperature, population density or spectral data.
  • Discrete features such as soil or land-cover classes can also be represented in the raster data model
  • Distinguish between continuous (elevation, temperature) and discrete (land cover classes) data
  • Both can be stored in raster format, but analysis approaches differ
  • Most remote sensing applications deal with continuous spectral values
  • Discrete classifications often result from processing continuous data

A continuous raster with the elevation of Luxembourg

A discrete raster with the municipalities of Luxembourg

A simple example: Elevation

      [,1] [,2] [,3] [,4] [,5] [,6]
 [1,]  275  282  373  342  357  326
 [2,]  230  318  316  351  345  346
 [3,]  164  337  258  342  363  350
 [4,]  168  337  261  354  358  364
 [5,]  202  322  250  380  362  373
 [6,]   NA  310  270  361  370  363
 [7,]   NA  277  310  291  375  365
 [8,]   NA  181  325  264  381  373
 [9,]   NA   NA  313  264  370  384
[10,]   NA   NA  298  285  370  380
[11,]  402   NA  333  293  356  382

A more complex example: Spectral data

  • Typically, RS imagery consists of more than 1 band
  • In this case, the data is stored in a 3 dimensional array (where band is the 3rd-dimension)
  • A RS image can contain any number of bands.
  • The most well known type of RS imagery consists of 3 Bands from the red, blue and green spectrum
  • Move from 2D (single band) to 3D (multi-band) concept
  • Each band captures different wavelengths of electromagnetic spectrum
  • RGB is familiar to students - good starting point
  • Modern satellites can have hundreds of bands (hyperspectral)
Figure 3.4: The bands stored internally
Figure 3.5: The RGB Composite

Each band is a 2D matrix

Figure 3.6: Band values
Figure 3.7: RGB True Color Image

Multispectral Datasets

  • Multiband datasets usually capture different parts of the EM spectrum
  • E.g. the Landsat image from the previous example has 6 bands capturing the following wavelengths:
    • Band 1: Blue (0.45 - 0.52 ยตm)
    • Band 2: Green (0.52 - 0.60 ยตm)
    • Band 3: Red (0.63 - 0.69 ยตm)
    • Band 4: Near-Infrared (0.77 - 0.90 ยตm)
    • Band 5: Short-wave Infrared (1.55 - 1.75 ยตm)
    • Band 7: Mid-Infrared (2.08 - 2.35 ยตm)
  • Point out that Band 6 (thermal) is missing from this list - was not included in this dataset
  • Each band serves different purposes: visible for true color, NIR for vegetation health, SWIR for moisture
  • These wavelength ranges are carefully chosen based on atmospheric windows and target phenomena
  • Students will learn to use different band combinations for different applications

NirGB Image

Figure 3.8: Band values
Figure 3.9: NIR-GB False Color Image

Representations of multispectral data

  • A true color image is created by using the Red (3), Green (2) and Blue (1) Band and mapping these to RGB
  • A false color image is created by mapping other bands to RGB
  • True color: what our eyes would see if we were there
  • False color: reveals information not visible to human eye
  • NIR false color is very common - vegetation appears red because it reflects strongly in NIR
  • Different false color combinations highlight different features (water, urban areas, etc.)
Figure 3.10: The R, G and B bands mapped to RGB
Figure 3.11: NIR, G and B bands mapped to RGB