‘Digitally’ ‘Reading’ a Satellite Image

Satellite images contain loads of information regarding the land area in their coverage. These are like mines having tons of metal but you have to explore, process and convert them into finished products to make them usable and to get desired information.

Information extraction from satellite images requires basic knowledge of image interpretation, skills in image processing and compatible software which can convert data into information. We have already discussed in detail regarding elements of visual image interpretation in the section Reading a Satellite Image.

With the advancement of technology digital image processing is also advanced a lot and is being practiced for information extraction from satellite images very effectively. What we often skip in visual image interpretation due to limitation of our eyes can be bring out using digital image processing methods.

In digital image processing number of algorithms are used to process satellite images. These algorithms digitally manipulate the raw images and convert them into desired information. These are mostly used to emphasize and extract features of our interests. For example- vegetation indices are applied for deriving valuable information regarding vegetation.

Digital image processing, in simple word, is playing with the digital numbers (DN) of pixels. But we have to be master of this play if we want to get desirable results. It is as easy and as difficult as playing with simple numbers. Image processing techniques are based on our day-to-day addition, subtraction, multiplication and division operators. A good knowledge of statistics is also required because in many of image processing techniques statistics is very frequently applied. For example- Principal Component Analysis (PCA) of satellite images is statistics based process. PCA is widely used to extract useful information from multiple bands filtering noise from the data.

Vegetation Index

Vegetation indices are very frequently and commonly used for vegetation related studies. These indices are (mostly) based on 'ratioing' of infrared and red bands. This is because vegetation reflects a large number of EMRs in infrared regions while absorbs EMRs in red region.

Some of the vegetation indices are ratio vegetation index (RVI), normalized difference vegetation index (NDVI), transformed vegetation index (TVI) etc. NDVI is particularly helpful in analysis of vegetation health and vegetation cover density.

Principal Component Analysis (PCA)

PCA techniques are used to ‘compile’ information from a large number of bands to lesser number of bands. Suppose we have to extract information from five bands of Landsat ETM+ image. When we do PCA- it will analyze all the five bands and remove redundant information to provide output in the form of PCA images. The first PCA image will contain most of the information and the information content will keep on decreasing in second, third and subsequent images. In other words we can say PCA compresses multiple band information into one or two images. Need not to say this technique enhances variance in the satellite images.