Download Essential Image Processing and GIS for Remote Sensing by Jian Guo Liu PDF

By Jian Guo Liu

Crucial photograph Processing and GIS for distant Sensing is an available evaluation of the topic and effectively attracts jointly those 3 key parts in a balanced and accomplished demeanour. The e-book presents an outline of crucial concepts and a range of key case reviews in a number of software areas.Key strategies and ideas are brought in a transparent and logical demeanour and defined during the provision of various proper conceptual illustrations. Mathematical aspect is saved to a minimal and in basic terms observed the place worthy for ease of figuring out. Such ideas are defined via logic phrases instead of in rigorous mathematical element whilst explaining snapshot processing and GIS suggestions, to allow scholars to know the necessities of a notoriously tough topic area. The e-book is obviously divided into 3 components, with the 1st half introducing crucial picture processing suggestions for distant sensing. the second one half appears at GIS and starts with an summary of the techniques, constructions and mechanisms in which GIS operates. ultimately the 3rd half introduces distant Sensing purposes. through the booklet the relationships among GIS, picture Processing and distant Sensing are sincerely pointed out to make sure that scholars may be able to observe a few of the ideas which were lined correctly. The latter chapters use various suitable case experiences to demonstrate quite a few distant sensing, snapshot processing and GIS functions in practice.  

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Extra resources for Essential Image Processing and GIS for Remote Sensing

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For RGB colour composition, if the average brightness of one image band is significantly higher or lower than the other two, the composite image will show obvious colour bias. To eliminate this, the three bands used for colour composition must have an equal value range and mean. The balance contrast enhancement technique (BCET) is a simple solution to this problem. Using a parabolic function derived from an input image, BCET can stretch (or compress) the image to a given value range and mean without changing the basic shape of the image histogram.

A better approach is to shift the value range of the denominator image upwards, by 1, to avoid 0. For an 8 bit image, this shift changes the image DN range from 0–255 to 1–256 which just exceeds 8 bits. This was a problem in the older generation of image processing systems before the 1990s but is no longer in most modern image processing software packages where the image processing is performed based on the double-precision, floating-point data type. A ratio image Y is an image of real numbers instead of integers.

8 Using a diagram, describe a ratio image in terms of a coordinate transformation between Cartesian and polar coordinates. 9 Explain the principles of topographic suppression using the image ratio technique. 10 What is NDVI and how is it designed? 11 Describe the design and functionality of the Landsat TM or ETMþ iron oxide and clay indices. 12 Try the normalized differencing approach, similar to NDVI, to enhance iron oxide and clay minerals. Compare the results with the relevant ratio indices and explain why the ratio-based indices are more effective for these two minerals.

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