SEGMENTATION WITH IDRISI (SELVA EDITION)

Arif Prasetyo Faculty of forestry – Bogor Agricultural [email protected] http://ayamforester.blogspot.com/

Object based image analysis on IDRISI [selva edition]-Arif Prasetyo-

1. IMPORT Data To IDRISI RASTER FORMAT (.RST)There are many format data can be imported to IDRISI format data and exported from IDRISI formatraster 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 ERDASIMAGE with 6 band [1,2,3,4,5,and 7]. When we input 1 multi layer data, the output will produced 6single layer / original band (1,2,3,4,5, and 7].2. SEGMENTATIONa. Prepare background imaginary dataBefore segmentation, better we create composite color [RGB]in IDRISI. This software can compositingcolor from 3 single / band to create RGB color. It can be processed by chose Create Color Composite

tool , or from IDRISI Open Dialog , or from Menu bar > Image Processing> Enhancement > Composite.


b. SegmentationInput data in this step is not composite color from above step (a), but 6 single band separately. Theinput is enable to contributing the others parameter, such as : NDVI, elevation, slope, soil type, etc, butin 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]


3. CHOSE SAMPLE OBJECT (TRAINING SAMPLE)SEGTRAIN can be opened in Menu bar > Image processing > Segmentation Classifier > SEGTRAIN1. 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 name3. If we want create new class, we have to back to 2nd step.

4. If we want to create new sample with existing class, just typing Class ID or arrange the number ofClass ID.

Arrange ID / typing Class ID


5. Click Create button to save Training Sample

4. EXECUTING PROCESSThere 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 aparticular training site signature. Pixels are assigned to the most likely class based on a comparison ofthe posterior probability that it belongs to each of the signatures being considered.

Menu bar > Hard Classifier > MAXLIKE


2) K-Nearest Neighbor Classification (KNN)

KNN is a k-nearest neighbor classifier that can perform both hard and soft classifications. KNN uses k-nearest neighbors from a subset of all of the training samples in determining a pixel’s class or the degreeof membership of a class. For a hard classification, a pixel is assigned to the class which dominates the k-nearest neighbors. Menu bar > Hard Classifier > KNN.

3) SEGCLASSSEGCLASS is a majority rule classifier based on the majority class within a segment. It requires an alreadyclassified image and a segmentation image. Typically, the classified image is derived using a pixel-basedclassifier such as MAXLIKE or KNN with the segment-based training and signature files. Thesegmentation image is derived from the module SEGMENTATION. SEGCLASS can improve the accuracyof the pixel-based classification and produce a smoother map-like classification result while preservingthe boundaries between segments.


Thanks and warmest regard

Arif

Imaginary +Segment

SAMPLE

SEGCLASSWITH MAXLIKE

SEGCLASSWITH KNN

SEGMENTATION WITH IDRISI (SELVA EDITION)

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