Object based image analysis on IDRISI [selva edition] -Arif Prasetyo1. IMPORT Data To IDRISI RASTER FORMAT (.RST) There are many format data can be imported to IDRISI format data and exported from IDRISI format raster data. It can be processed with: Menu bar > File > Import / Export
In this case, I was using LANDSAT imaginary data, and have been processed by layer stacking in ERDAS IMAGE with 6 band [1,2,3,4,5,and 7]. When we input 1 multi layer data, the output will produced 6 single layer / original band (1,2,3,4,5, and 7]. 2. SEGMENTATION a. Prepare background imaginary data Before segmentation, better we create composite color [RGB]in IDRISI. This software can compositing color from 3 single / band to create RGB color. It can be processed by chose Create Color Composite tool , or from IDRISI Open Dialog > Enhancement > Composite.
Arif Prasetyo Faculty of forestry – Bogor Agricultural University
[email protected] http://ayamforester.blogspot.com/
, or from Menu bar > Image Processing
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b. Segmentation Input data in this step is not composite color from above step (a), but 6 single band separately. The input is enable to contributing the others parameter, such as : NDVI, elevation, slope, soil type, etc, but in this case was using 6 parameter [original 6 single band LANDSAT imaginary].
Filename : Single band was used to segmentation parameter Weight : Priority for segmentation [same value = same priority] Similarity tolerance : Scale parameter to create object size [minimally one contain value (or more), must be non-negative]. Example : 15,30,50 Weights for the mean and the variance factors: to be used for evaluating the similarity between neighboring segments Load RGB layer and overlay with Segment layer [result from segmentation]
Arif Prasetyo Faculty of forestry – Bogor Agricultural University
[email protected] http://ayamforester.blogspot.com/
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Arif Prasetyo Faculty of forestry – Bogor Agricultural University
[email protected] http://ayamforester.blogspot.com/
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3. CHOSE SAMPLE OBJECT (TRAINING SAMPLE) SEGTRAIN can be opened in Menu bar > Image processing > Segmentation Classifier > SEGTRAIN 1. Select object to create a new training site file [pick new sample > select object] 2. Change color, typing Class ID [or arrange](1,2,3,dst), and typing Class name 3. If we want create new class, we have to back to 2 nd step.
4. If we want to create new sample with existing class, just typing Class ID or arrange the number of Class ID.
Arrange ID / typing Class ID
Arif Prasetyo Faculty of forestry – Bogor Agricultural University
[email protected] http://ayamforester.blogspot.com/
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5. Click Create button to save Training Sample
4. EXECUTING PROCESS There were some type classification method that be used to produced classification from training area, imaginary, and segment. 1) Maximum Likelihood Classification The Maximum Likelihood classification is based on the probability density function associated with a particular training site signature. Pixels are assigned to the most likely class based on a comparison of the posterior probability that it belongs to each of the signatures being considered. Menu bar > Hard Classifier > MAXLIKE
Arif Prasetyo Faculty of forestry – Bogor Agricultural University
[email protected] http://ayamforester.blogspot.com/
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2) K-Nearest Neighbor Classification (KNN) KNN is a k-nearest neighbor classifier that can perform both hard and soft classifications. KNN uses knearest neighbors from a subset of all of the training samples in determining a pixel’s class or the degree of membership of a class. For a hard classification, a pixel is assigned to the class which dominates the knearest neighbors. Menu bar > Hard Classifier > KNN.
3) SEGCLASS SEGCLASS is a majority rule classifier based on the majority class within a segment. It requires an already classified image and a segmentation image. Typically, the classified image is derived using a pixel-based classifier such as MAXLIKE or KNN with the segment-based training and signature files. The segmentation image is derived from the module SEGMENTATION. SEGCLASS can improve the accuracy of the pixel-based classification and produce a smoother map-like classification result while preserving the boundaries between segments.
Arif Prasetyo Faculty of forestry – Bogor Agricultural University
[email protected] http://ayamforester.blogspot.com/
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Imaginary + Segment SEGCLASS WITH MAXLIKE
SAMPLE
SEGCLASS WITH KNN Thanks and warmest regard Arif
Arif Prasetyo Faculty of forestry – Bogor Agricultural University
[email protected] http://ayamforester.blogspot.com/
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