Solar radiation on hitting a target surface may be transmitted, absorbed or reflected. Different materials reflect and absorb differently at different wavelengths. The reflectance spectrum of a material is a plot of the fraction of radiation reflected as a function of the incident wavelength and serves as a unique signature for the material. In principle, a material can be identified from its spectral reflectance signature if the sensing system has sufficient spectral resolution to distinguish its spectrum from those of other materials. This premise provides the basis for multispectral remote sensing. Nguyen Dinh Duong (1997) proposed a method for decomposition of multi-spectral image into several sub-images based on modulation (spectral pattern) of the spectral reflectance curve. The hypothesis roots from the fact that different ground objects have different spectral reflectance and absorption characteristics which are stable for a given sensor. This spectral pattern can be considered as invariant and be used as one of classification rules.
TopColour Composites
While displaying the different bands of a multi spectral data set, images obtained in different bands is displayed in image planes (other than their own) the colour composite is regarded as False Colour Composite (FCC). High spectral resolution is important when producing colour components. For a true colour composite an image data used in red, green and blue spectral region must be assigned bits of red, green and blue image processor frame buffer memory. A colour infrared composite ‘standard false colour composite’ is displayed by placing the infrared, red and green in the red, green and blue frame buffer memory. In this healthy vegetation shows up in shades of red because vegetation absorbs most of green and red energy but reflects approximately half of incident Infrared energy. Urban areas reflect equal portions of NIR, R & G, and therefore they appear as steel grey.
There are three steps to generate colour composites from panchromatic images:
- 1.
Read the panchromatic images using geotiffread function.
- 2.
Channel the panchromatic images into a RGB image frame as required.
- 3.
Visualise the RGB image using imshow function.
TopNormalized Difference Geo-Spatial Indices
When dealing with images, the knowledge of the techniques of basic image processing always comes handy. Image processing, itself being a vast domain, we mentioned only those methods which are mainly related to our work, in the previous chapter. However, these methods can be applied to the multispectral satellite images with one band at a time. To enhance or extract features from satellite images which cannot be clearly detected in a single band, you have to use the spectral information of the object recorded in multiple bands. These images may be separate spectral bands from a single multispectral data set, or they may be individual bands from data sets that have been recorded at different dates or using different sensors. The operations of addition, subtraction, multiplication and division, are performed on two or more co-registered images of the same geographical area (Sreenivas & Chary, 2011). This section deals with multi-band operations.
The following operations will be treated:
- •
The use of ratio images to reduce topographic effects.
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Various indexes, some of which are more complex than ratio's only.
Various mathematical combinations of satellite bands, have been found to be sensitive indicators of the presence and condition of green vegetation. These band combinations are thus referred to as vegetation indices. Two such indices are the simple vegetation index (VI) and the normalized difference vegetation index (NDVI).
NDVI = (NIR – Red) / (NIR + Red)