Monday, 27 October 2014

eCognition tutorial: Image object fusion in eCognition: Example of water bodies classification

eCognition is a powerful software for analysis for remote sensing images. Many people have this false impression that eCognition is all about segmentation. That’s not true. Segmentation is a just a part of it big chain that may involve segmentation, temporary classification, fusion, exploitation of contextual information etc. Many people believe that segmentation should be perfect at first time which is a fallacy. You can modify your segments as more information becomes available during the analysis. Typically in my any project I use segmentation at least 10 times. Yes at least 10 times .One of  the underutilized feature of eCognition is “Image Object Fusion” algorithm.The algorithm is an essential part of "iterative segmentation and classification" approach of GEOBIA. In this blog, I will show an example of image object fusion for classification of water bodies in a small remote sensing image. The step involves:
  1. Segmentation
  2. Initial classification of water
    1. Establishment of customized feature RationNIR 
      • RationNIR = (meanNIR/(meanR+meanB+meanG+meanNIR))*100
    2. Classify objects that satisfy property
      • RationNIR  less than 15 
      • Area greater than 10 pixels ( to avoid small shadows)
  3. Image object fusion using PPO ( Parent Process Object) to get whole water body
    1. Starting from water classified in step 2, check neighboring water objects and if difference between water object and neighboring objects is less than 5 in RatioNIR. Run in a loop.
    2. Process all water objects. 
Original image
Initial segmentation
Initial water classification
Final segmentation after image object fusion
Final water classification using image object fusion
Very powerful image object fusion algorithm

Step 3 is a one liner algorithm using "Image object Fusion".  Notice various parameters like Class filter, candidates classes, fitting function threshold, use absolute fitting value and weighted sum.To help you understand, I have made a "gif" to elaborate what is going on.

Blue: Initial water
Yellow: Active object
Red: Fused water after image object fusion

Here is the whole Rule set for water classification with image object fusion.

1 comment:

  1. Hello, is it possible to share the ruleset for this operation. Thanks.