Not my tutorial but since it is in a video format, I thought was interesting to post it. It works and it is really well explained. Quite useful to me. Found it when I really needed it. Enjoy! Credits go to the author of the video tutorial.
Differences between compiling in MS Visual C++ 6.0 and compiling in MS Visual Studio 2010
To reclassify an image into classes could be useful when we need to investigate what is going on inside the cloud of pixels. At this time I am sharing an example on how to do that using Arcmap. The example shows the next step on the NDVI analysis presented in the previous post Acknowledgments are for Buitron and Fernandez (2102), and the copyrights statement is shown on the bottom of the page.
Simple steps to calculate the NDVI, using Erdas. NDVI stands for Normalized Difference Vegetation Index; it is an index commonly used to investigate the characteristics of the vegetation, biomass, and others.
The following tutorial presents detailed explanation accompanied with snapshots. The acknowledgements are the same of the previous entries (Buitron and Fernandez, 2012). The copyrights are shown in the bottom of the page.
From an engineering perspective, the simplest use of optical remotely sensed imagery is on the interpretation and identification of historical changes of land surface features (e.g., on the area covered by a certain kind of forest). Using simple words, such task can be accomplished by classifying the pixels of the imagery into as many groups as needed, or more properly, as allowed by the resolution of the imagery. There are two kinds of classification procedures: the unsupervised classification and the supervised classification. The supervised classification is principally used to extract qualitative information out from the imagery, having enough knowledge of the physical characteristics of some pixels, which can be used to characterize or classify other pixels within the study area. On the other hand, the unsupervised classification, simply generates clusters of pixels, that qualitatively characterize the study area. The latter is commonly used when there is not enough knowledge on the physical characteristics of the pixels (e.g., when performing multi temporal analysis of surface areas along a wide historical lapse of time, for which there is limited knowledge and/or information). An interesting example can be found following the link of the figure above. In this entry, I am presenting a tutorial of how to perform the latter (unsupervised classification). The material has been originally prepared by the consultants C. Buitrón and J. Fernández, under my supervision. This tutorial follows the series published on the entries presented before on this blog. Enjoy!