1. Object-Based Crop Identification for Collecting Reference Data from VHRI RHSeg and Ecognition Software Kamini Yadav and Russ Congalton

2. Site 19: Chowchilla, USA, 9 th May 2009

3. Scale parameter: 62 Spectral: Mean NIR Geometry: Number of pixels Relative border Texture: Grey-Level Co-occurrence matrix Contrast Ecognition Hseg Prune

4. Estimation of Scale Parameter ROC-LV graphs show sudden oscillations between peaks and plunges, on descendant trends, whereas LV graphs are far smoother The peaks in an ROC-LV curve indicate the levels where LV increases as segments delineate their correspondents on the ground Site 19 Site 20 Local Variance within Objects along the Scale parameters

5. Site 23: Nebraska, USA, 9th June 2009

6. Future Considerations Strategy to define the spectral, texture and spatial features or shape parameters for accurate crop Identification Selecting textural features from different bands of very high resolution Images Availability of VHRI for distinct growing- season periods Evaluate reflectance and texture values on images as affected by different spectral response and planting pattern within crop-fields along crop calendar stages To formulate a decision tree for crop identification based on spectral, textural features of different crops

7. Decision Tree for Object based Crop Identification Barragan J. et al. 2011

8. Thank You