Clasificacion Con Envi

ENVI Tutorial: Classification Methods
Classification Methods Files Used in this Tutorial Examining a Landsat TM Color Image Reviewing Image Colors Using the Cursor Location/Value Examining Spectral Plots Exploring Unsupervised Classification Methods Applying K-Means Classification Applying ISODATA Classification Exploring Supervised Classification Methods Selecting Training Sets Using Regionsof Interest (ROI) Applying Parallelepiped Classification Applying Maximum Likelihood Classification Applying Minimum Distance Classification Applying Mahalanobis Distance Classification Collecting Endmember Spectra Applying Binary Encoding Classification Exploring Spectral Classification Methods Exploring Rule Images Post Classification Processing Extracting Class Statistics Generating a ConfusionMatrix Clumping and Sieving Combining Classes Overlaying Classes Editing Class Colors Working with Interactive Classification Overlays Overlaying Vector Layers Converting a Classification to a Vector Adding Classification Keys Using Annotation 2 2 3 3 4 4 6 6 7 9 9 9 10 10 11 11 12 14 15 17 17 18 19 20 20 22 23 24 24 25

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ENVI Tutorial: Classification Methods

Classification MethodsThis tutorial provides an introduction to classification procedures using Landsat TM data from Cañon City, Colorado. Results of both unsupervised and supervised classifications are examined and post classification processing including clump, sieve, combine classes, and accuracy assessment are discussed.

Files Used in this Tutorial
ENVI Resource DVD: Data\can_tm File
can_tmr.img can_tmr.hdrcan_km.img can_km.hdr can_iso.img can_iso.hdr classes.roi

Description Cañon City, Colorado TM reflectance image ENVI header for above K-means classification ENVI header for above ISODATA classification ENVI header for above Regions of interest (ROI) for supervised classification

can_pcls.img Parallelepiped classification can_pcls.hdr ENVI header for above can_bin.img can_bin.hdr can_sam.imgcan_sam.hdr can_rul.img can_rul.hdr can_sv.img can_sv.hdr

Binary encoding result ENVI header for above SAM classification result ENVI header for above Rule image for SAM classification ENVI header for above Sieved image ENVI header for above

can_clmp.img Clump of sieved image can_clmp.hdr ENVI header for above can_comb.img Combined classes image can_comb.hdr ENVI header for above can_ovr.imgcan_ovr.hdr can_v1.evf can_v2.evf

Classes overlain on gray scale image ENVI header for above Vector layer generated from class #1 Vector layer generated from class #2

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ENVI Tutorial: Classification Methods

Examining a Landsat TM Color Image
This portion of the exercise will familiarize you with the spectral characteristics of the Landsat TM data of Cañon City, Colorado, USA. Colorcomposite images will be used as the first step in locating and identifying unique areas for use as training sets in classification. Before attempting to start the program, ensure that ENVI is properly installed as described in the Installation Guide that shipped with your software. 1. From the ENVI main menu bar, select File > Open Image File. 2. Navigate to the Data\can_tm directory, select thefile can_tmr.img from the list, and click Open. The Available Bands List appears on your screen. 3. Click on the RGB Color radio button in the Available Bands List. Red, Green, and Blue fields appear in the middle of the dialog. 4. Select Band 4, Band 3, and Band 2 sequentially from the list of bands at the top of the dialog by clicking on the band names. The band names are automatically enteredin the Red, Green, and Blue fields. 5. Click Load RGB to load the image into ENVI. 6. Examine the image in the display group.

Reviewing Image Colors
The color image displayed below can be used as a guide to classification. This image is the equivalent of a false color infrared photograph. Even in a simple three-band image, it’s easy to see that there are areas that have similar spectral...