The techniques with ERDAS IMAGINE include: 1. Data correction - radiometric and geometric correction 2. Radiometric enhancement - enhancing images based on the values of individual pixels 3. Spatial enhancement - enhancing images based on the values of individual and neighboring pixels 4. Spectral enhancement - enhancing images by transforming the values of each pixel on a multi-band basis Spectral Enhancement Compress spectral bands of data that are similar Extract new bands of data that are more interpretable Apply mathematical transforms and algorithms Display a wider variety of information in the three available color combinations (R,G,B) Some of these enhancements can be used to prepare data for classification. However, this is a risky practice unless you are very familiar with your data, and the changes that you are making to it. Anytime you alter values, you risk losing some information. 1
Principal Components Analysis (PCA) Principal components analysis (or PCA) is often used as a method of data compression. It allows redundant data to be compacted into fewer bands that is, the dimensionality of the data is reduced. 2
Principal Components Analysis (PCA) The bands of PCA data are non-correlated and independent, and are often more interpretable than the source data. The process is easily explained graphically with an example of a two-band scatter-plot, which shows the relationships of data file values in two bands. If both bands have normal distributions, an ellipse shape results. 3
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PC1 PC2 PC3 PC4 PC5 PC6 5
Landsat TM bands 4,5,3 in RGB PCA bands 1,2,3 in RGB 6
Eigenvalues: 868.47951526623 149.9110021935107 40.51377151352024 5.713177499967178 4.347115381974151 3.125243136380135 0.8475352179660821 Although there are n output bands in a principal components analysis, the first few bands accounts for a high proportion of the variance in the data in some cases almost 100%. Therefore, PCA is useful for compressing data into few bands. What do the data in new bands mean? 7
Tasseled Cap or Kauth-Thomas Transformation The Tasseled Cap transformation offers a way to optimize data viewing for vegetation studies. Research has produced three data structure axes which define the vegetation information content (Crist et al 1986, Crist & Kauth 1986): This transformation produces from original MSS data space to a new four-dimensional feature space, called: The soil brightness index (B), Greenness vegetation index (G), Yellow stuff index (Y), and Non-such (N) Tasseled Cap or Kauth-Thomas Transformation The transformation consists of linear combinations of the four MSS bands to produce a set of four new variables. 8
Tasseled Cap Transformation 1985: The Transformation was extended using Landsat TM data: 1. Brightness: a weighted sum of all bands, defined in the direction of the principal variation in soil reflectance. 2. Greenness: orthogonal to brightness, a contrast between the near-infrared and visible bands. Strongly related to the amount of green vegetation in the scene. 3. Wetness: relates to canopy and soil moisture (Lillesand and Kiefer 1987). (TM Band 4) (TM Band 3) (TM Band 3) The name suggests the characteristics the indices were intended to measure. 9
Tasseled Cap Transformation A simple calculation (linear combination) then rotates the data space to present any of these axes to the user. These rotations are sensor-dependent, but once defined for a particular sensor, the same rotation will work for any scene taken by that sensor. For Landsat-4 TM, for example, the calculations are: Brightness =.3037(TM1) +.2793)(TM2) +.4743 (TM3) +.5585 (TM4) +.5082 (TM5) +.1863 (TM7) Greenness = -.2848 (TM1) -.2435 (TM2) -.5436 (TM3) +.7243 (TM4) +.0840 (TM5) -.1800 (TM7) Wetness =.1509 (TM1) +.1973 (TM2) +.3279 (TM3) +.3406 (TM4) -.7112 (TM5) -.4572 (TM7) Haze =.8832 (TM1) -.0819 (TM2) -.4580 (TM3) -.0032 (TM4) -.0563 (TM5) +.0130 (TM7) Source: Modified from Crist et al 1986, Jensen 1996 10
The Tasseled Cap transformation is a global vegetation index. Theoretically, it may be used anywhere in the world to disaggregate the amount of soil brightness, vegetation, and moisture content in individual pixels in a Landsat MSS or TM image. Other Vegetation Indices Since 1960's, much of the remote sensing efforts in vegetation has gone into the development of vegetation indices. Vegetation indices can be defined as dimensionless, radiometric measures that function as indicators of relative abundance and activity of green vegetation, often including leaf-area-index (LAI), percentage green cover, chlorophyll content, green biomass, and absorbed photosynthetically active radiation. There are more than 20 vegetation indices in use. 11
Normalized Difference Vegetation Index (NDVI) The NDVI index was widely adopted and applied to the original Landsat MSS digital remote sensing data. 12
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