1. James C. Tilton Code 606.3 Computational & Information Sciences and Technology Office NASA Goddard Space Flight Center January 30, 2015 update National Aeronautics and Space Administration www.nasa.gov

2. Utilizing HSeg in the GFSAD30 Project 2 At least three possibilities: 1.Use RHSeg/HSeg together with HSegLearn to perform computer assisted photointerpretation of high resolution imagery data (< 5m) to develop ground reference data. 2.Develop post-processing analysis approaches for Landsat TM data for automated classification (the previous work by Panshi Wang of U of MD for LCLUC project for urban mapping is very complicated, and may not be appropriate). 3.Develop post-processing analysis approached for high resolution imagery data (<5m) for automated classification (starting the with hierarchical segmentation “pruning” of Edoardo Pasolli). January 30, 2015GFSAD30 Project Meeting

3. Utilizing HSeg in the GFSAD30 Project 3 RHSeg/HSeg together with HSegLearn: This combination was developed for and used extensively in a NASA LCLUC program funded project to map urbanization in 2000 and 2010 at the 30m Landsat TM scale to generate 30m scale ground reference data from 1-2m scale satellite imagery data (Quickbird and WorldView). HSegLearn has been modified to “ignore” region objects of size less than an analyst specified number of pixels. This should make it easier to use HSegLearn in our cropland mapping application. January 30, 2015GFSAD30 Project Meeting

4. Utilizing HSeg in the GFSAD30 Project 4 Develop post-processing analysis approached for high resolution imagery data (<5m) for automated classification: I have written and tested the “hsegprune” program on a Quickbird data set selected by Kamini Yadav: 2009/Site19-Chowchill,CA,USA. The hsegprune program operates on either region classes or region objects. The hsegprune program successfully selects a single image segmentation out of the HSeg/RHSeg segmentation hierarchy by analyzing the stability of the region standard deviation and region boundary pixel ratio features. January 30, 2015GFSAD30 Project Meeting

5. Utilizing HSeg in the GFSAD30 Project 5 Develop post-processing analysis approached for high resolution imagery data (<5m) for automated classification: I have also written and tested the “hsegprune2” program on a Quickbird data set selected by Kamini Yadav: 2009/Site19-Chowchill,CA,USA. The segmentation hierarchy pruning approach implemented in “hsegprune2” was developed by Edoardo Pasolli, and is described in: Jinha Jung, Edoardo Pasolli, Saurabh Prasad, James C. Tilton, and Melba Crawford, “A framework for land cover classification using discrete return LiDAR data: Adopting pseudo-waveform and hierarchical segmentation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 2, Feb. 2014, pp. 491-502. January 30, 2015GFSAD30 Project Meeting

6. Utilizing HSeg in the GFSAD30 Project 6 January 30, 2015GFSAD30 Project Meeting

7. Utilizing HSeg in the GFSAD30 Project 7 January 30, 2015GFSAD30 Project Meeting

8. Utilizing HSeg in the GFSAD30 Project 8 Future work in developing post-processing analysis approached for high resolution imagery data (<5m) for automated classification: Explore new spatial features that might be useful in identifying agricultural fields from the pruned HSeg/RHSeg segmentation hierarchies: For example, spatial pattern analysis features such as described in: José M. Peña-Barragán, Moffatt K. Ngugi, Richard E. Plant, Johan Six, “Object-based crop identification using multiple vegetation indices, textural features and crop phenology,” Remote Sensing of Environment, 115 (2011) 1301-1316. I welcome anyone else’s suggestions for promising spatial analysis features to investigate. January 30, 2015GFSAD30 Project Meeting

9. SignOffPage 9 9 January 30, 2015GFSAD30 Project Meeting