Object-Oriented Oriented Method to Classify the Land Use and Land Cover in San Antonio using ecognition Object-Oriented Oriented Image Analysis

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1 Object-Oriented Oriented Method to Classify the Land Use and Land Cover in San Antonio using ecognition Object-Oriented Oriented Image Analysis Jayar S. Griffith ES6973 Remote Sensing Image Processing and Analysis

2 Object Oriented Image Analysis Object Oriented Classification Review Example Work Flow Study Area Methods Analysis Results

3 Xie, 2005 ES6973

4 Xie, 2005 ES6973

5 LULC Land cover refers to the type of material present on the landscape (e.g., water, sand, crops, forest, wetland, human-made materials such as asphalt). Land use refers to what people do on the land surface (e.g., agriculture, commerce, settlement).

6 Laliberte et al. 2004Remote Sensing of Env. Xie, 2005 ES6973

7 Study Area Study Area San Antonio ETMp27r40y01m7d21 A subset of ETM+ with path27 row40 acquired on 7/21/2001 (Xie, 2004 Lab 3 ES5053)

8 Methods ecognition Guided Tour as Example: Tour 1: Landsat TM subset of Orange County (California, USA) Input Image: ETMp27r40y01m7d21 (San Antonio) In this guide you will learn how to: 1. Load and display raster data 2. Perform an image segmentation 3. Insert the nearest neighbor classifier into the class description 4. Classify 5. Perform classification quality assessment ecogniton User Guide 2004 Definiens Imaging

9 ecognition Professional New Project Layer Mixing Class Segmentation Hierarchy Sample Start Editor Classification

10 Load and Display Raster Create New Project Load Raster image and set band sequence Create

11 Edit Layer Mixing Edit Layer Mixing Setting RGB order Apply histogram stretch and three layer mixing

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13 Segmentation Image Segmentation is a partitioning of an image into constituent parts using image attributes such as pixel intensity, spectral values, and/or textural properties (Xie, 2005 ES6973 Lect.10) Scale: Indirectly related to the size of the created objects Color: Pixel Value Shape: Compactness and Smoothness Pixel Neighborhood Function Layer Weights

14 Creating a knowledge base by means of the Class Hierarchy Create new class hierarchy Four Classes: 1. Impervious Surface 2. Water 3. Agriculture 4. Rural

15 Edit Classes Inserting the Classifier ecognition offers two different classifiers: nearest neighbor or membership functions Classifier used: Nearest Neighbor

16 Declare Training Areas Declaring sample objects Nearest neighbor classification in ecognition is similar to supervised classifications in common image analysis software. You have to declare training areas, which are typical representatives of a class. In ecognition such training areas are referred to as samples or sample objects. Agriculture = Yellow Impervious Surface = Red Rural = Green Water = Blue

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20 Classifying the image objects in the scene

21 Classified Image & Original Image

22 Checking for each image object the best class evaluation result (best classification result) Accuracy Assesment Create Statistics

23 Statistics Error Matrix based on Samples

24 Checking the classification results against predefined test areas Load TTA Mask of known test area Compare TTA Mask with classification

25 Results 1. ecognition Basics 2. Load and display raster data 3. Perform an image segmentation 4. Insert the nearest neighbor classifier into the class description 5. Classify 6. Perform classification quality assessment

26 Further Study Improve Classification Class Hierarchy Class Description and Rules Field Data Trial and error Final Classification to other years of the same area to measure LULC

27 References Gitas, I. Z., Mitri, G. H., Ventura G., Object based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR Imagery. Remote Sensing of Environment. Harold, M., Guenther, S., Clarke, C. C., Mapping Urban Areas in the Santa Barbara South Coast using Ikonos data and ecognition. Vol.4/Nr. 1. Available online at: Laliberte, A. S., Rango, A., Havstad, K. M., Paris, J.F., Beck, R.F., McNelly, R., Gonzales, A. L., Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico. Remote Sensing of Environment Mansor, S., Wong, T.H., Shariff, Abdul R.M., Object Oriented Classification for Land Cover Mapping. Available online at: Moeller, M.S., Stefanov, W.L., Netzband, Characterizing Land cover changes in a Rapidly Growing Metropolitan Area Using Long Term Satellite Imagery. ASPRS Annual Conference Proceedings Xie, ES6973 Lecture 10 Object Oriented Classification. Remote Sensing Image Process and Analysis Definiens Imaging User Guide 4. ecognition

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