![]() This exercise will illustrate how to open ArcView Shape files and overlay them onto (A sample set of individual bands can be downloaded or you can provide This tutorial will illustrate how toĭo this. Times one will receive Landsat images from the EROS Center via ftp or on a DVD thatĬontains one file for each of the 4-10 Landsat bands. Image files together to be treated as one image file in an image window and/or createĪ new image file that incorporates all of the separate images. One can use the "Logically Linking" capability in MultiSpec to link several Image Files into a Single Multispectral Image File You can also do a supervised classification of an image file by selecting trainingĪreas for selected classes from known areas. Use the ISODATA algorithm for this exercise. The second is an iterative type called ISODATA. Similar pixels in the image into clusters or categories. Two Clustering algorithms are available in MultiSpec. Portion of the Display Specifications dialog box. In the multispectral image window by setting five different options in the Enhancement This exercise illustrates how you can control the enhancement of the image In this exercise, you will display an aircraft image file and view the data in several That there is a tutorial using an area around Arequipa, Peru in both Spanish and English in the second section of this page and there are some tutorials in Greek and Hungarian provided by MultiSpec users at the bottom ![]() Tutorials 1-4 & 6-7 were revised in October 2009 tutorials 8 & 9 were added in June/JulyĢ010 tutorial 5 was revised in September 2013. An Acrobat Reader for Macintosh, Windows, or Unix can be downloaded If you are using version 3 or later of Netscape Navigator and version 4 or later The documents are in Adobe Acrobat Format and can be read within your web viewer There is a separate pdf document for each tutorial. ![]() Supervised classifications view the results, learn how to combine separate imageįiles into one image file, overlay shape files onto images, enhance images and work Multispectral and thematic images, run unsupervised classification (ISODATA), run These tutorials will illustrate how to display _color(r, g, b)Ĭreate true color composite from a combination of red, green and blue bands satellite images.The following are some small tutorials or exercises that one can follow to gain (nir_agg, .)Ĭomputes Structure Insensitive Pigment Index which helpful in early disease detection in vegetation. Structure Insensitive Pigment Index (SIPI) # (nir_agg, red_agg)Ĭomputes Soil Adjusted Vegetation Index (SAVI). (nir_agg, red_agg)Ĭomputes Normalized Difference Vegetation Index (NDVI). Normalized Difference Vegetation Index (NDVI) # (nir_agg, swir1_agg)Ĭomputes Normalized Difference Moisture Index. Normalized Difference Moisture Index (NDMI) # 2(swir1_agg, .)Ĭomputes Normalized Burn Ratio 2 "NBR2 modifies the Normalized Burn Ratio (NBR) to highlight water sensitivity in vegetation and may be useful in post-fire recovery studies." _ (red_agg, .)Ĭomputes Enhanced Built-Up and Bareness Index (EBBI) which allows for easily distinguishing between built-up and bare land areas. (nir_agg, .)Ĭomputes Atmospherically Resistant Vegetation Index.Įnhanced Built=Up and Bareness Index (EBBI) # Multispectral # Atmospherically Resistant Vegetation Index (ARVI) #
0 Comments
Leave a Reply. |