1. James C. Tilton Code 606.3 Computational & Information Sciences and Technology Office NASA Goddard Space Flight Center November 20, 2014 update National Aeronautics and Space Administration www.nasa.gov

2. Obtaining High Resolution Imagery Data from NGA 2 In attempting to obtain 8-band WorldView data for another project, I discovered that such data CANNOT be obtained via the NGA WARP facility. WARP only has 4-band WorldView data. However, we can obtain the 8-band data through something called a “CIDR request.” This is actually a USGS facility: https://cidr.cr.usgs.gov/login/ If you have a USGS EarthExplorer or GloVis account, you already have an account with the CIDR Tool. I was successful in obtaining several 8-band WorldView2 data sets for another project I am involved with. 20 November 2014GFSAD30 Telecon

3. Obtaining High Resolution Imagery Data from NGA 3 An unresolved issue: The NGA is retiring the WARP system in favor of the new Net Centric GEOINT Discovery Services (NGDS) system that is accessed through their GEOAxIS system. This retirement will apparently be final as of Nov. 30, 2014. The preferred mode of access through GEOAxIS is with a Personal Identity Verification (PIV) card. I have encountered technical problems in doing this, and am actively working to resolve this issue together with the GEOAxIS support team. It is my understanding that both Jun and Pardha already have access to the new NGDS system, and are able to download images from NGDS. 20 November 2014GFSAD30 Telecon

4. Utilizing HSeg in the GFSAD30 Project 4 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 – starting with previous work by Panshi Wang of U of MD for LCLUC project for urban mapping. 3.Develop post-processing analysis approached for high resolution imagery data (<5m) for automated classification – this would be new work. 20 November 2014GFSAD30 Telecon

5. Utilizing HSeg in the GFSAD30 Project 5 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). This combination should be usable “as is” for similar purposes in the GFSAD30 project. 20 November 2014GFSAD30 Telecon

6. Utilizing HSeg in the GFSAD30 Project 6 Develop of post-processing analysis approaches for Landsat TM data for automated classification: I am working with Panshi Wang of U of MD for LCLUC project to port his approach for urban mapping from Python to C++. I have already ported the initial step of his process to an efficient C++ program: hsegsizeobj.cc. Since we plan to run his code on the NCCS Discover system, I have confirmed that the Boost C++ Library Panshi’s code requires is available on Discover. 20 November 2014GFSAD30 Telecon

7. Utilizing HSeg in the GFSAD30 Project 7 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. I’ve shared this hsegprune code and tests results with Kamini Yadav as a prelude to her working with me on-site at NASA Goddard Dec. 15-19. 20 November 2014GFSAD30 Telecon

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: McGarigal, K., and B. J. Marks. 1995. Fragstats: Spatial Pattern Analysis Program for Quantifying Landscape Structure. Gen. Tech. Rep. PNW-GTR-351. Portland, U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 122 p. (http://www.fs.fed.us/pnw/pubs/pnw_gtr351.pdf)http://www.fs.fed.us/pnw/pubs/pnw_gtr351.pdf I welcome anyone else’s suggestions for promising spatial analysis features to investigate. 20 November 2014GFSAD30 Telecon

9. SignOffPage 9 9 20 November 2014GFSAD30 Telecon