James C. Tilton Code Computational & Information Sciences and Technology Office NASA Goddard Space Flight Center January 20, 2016 National Aeronautics and Space Administration

HSeg Background 2 HSeg produces a hierarchical set of image segmentations with the following characteristics: A set of segmentations that 1.consist of segmentations at different levels of detail, in which 2.the coarser segmentations can be produced from merges of regions from the finer segmentations, and 3.the region boundaries are maintained at the full image spatial resolution The HSeg algorithm is fully described in: James C. Tilton, Yuliya Tarabalka, Paul M. Montesano and Emanuel Gofman, “Best Merge Region Growing Segmentation with Integrated Non-Adjacent Region Object Aggregation,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 11, Nov. 2012, pp August 2013USGS Global Croplands Working Group Meeting

Incorporating Edge Information into HSWO/HSeg/RHSeg 3 Edge information is incorporated at three different stages: 1.An initialization stage in which the edge information directs a fast first-merge region growing process (Muerle-Allen)to quickly merge connected areas with edge values <= E t (set by user), and 2.The normal HSWO/HSeg best merge region growing stage in which the edge information influences the best merge decisions. 3.In performing processing window artifact elimination in RHSeg. J. L. Muerle, D. C. Allen, “Experimental Evaluation of Techniques for Automatic Segmentation of Objects in a Complex Scene,” in G. C. Cheng, et al. (Eds.), Pictorial Pattern Recognition, Thompson, Washington, DC, pp. 3-13, August 2013USGS Global Croplands Working Group Meeting

Frei-Chen Edge Difference Operator Result: A true color rendition of a 768x768 pixel section of Ikonos data from the Patterson Park/Inner Harbor area of Baltimore, MD. Frei-Chen Edge Difference Operator Result, maximum over spectral bands, thresholded at August 2013USGS Global Croplands Working Group Meeting

Utilizing HSeg in the GFSAD30 Project 5 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 pre-processing techniques to “enhance” the imagery data prior to processing with HSeg. 3.Develop post-processing analysis approaches for automated classification. 15 August 2013USGS Global Croplands Working Group Meeting

RHSeg/HSeg together with HSegLearn 6 HSegLearn takes as input a hierarchical set of image segmentations such as produced by the HSeg best-merge region growing segmentation program. Through HSegLearn, an analyst specifies a selected set of positive and negative examples of impervious land cover. HSegLearn searches the hierarchical set of segmentations for the coarsest level of segmentation at which the selected positive examples do not conflict with negative example locations and labels the image accordingly. 15 August 2013USGS Global Croplands Working Group Meeting

RHSeg/HSeg together with HSegLearn 7 HSegLearn 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 may make it easier to use HSegLearn in our cropland mapping application. 15 August 2013USGS Global Croplands Working Group Meeting

HSegLearn Example 8 15 August 2013USGS Global Croplands Working Group Meeting The HSegLearn RGB Image Panel displaying a subsection of a WorldView2 image in the vicinity of the Charlotte, NC International Airport. The HSegLearn Current Region Labels Panel after the analyst selected some positive example region objects (yellow) and some negative example objects (white). The HSegLearn Current Region Labels Panel after the analyst submitted the positive and negative example region objects for processing. The positive example regions are colored green and negative example regions are colored red. Note the generalization of the positive example regions.

Post-processing of RHSeg/HSeg results: 9 Mutlu has found that the RHSeg/HSeg image segmentation results contained too many small region objects. He requested that I develop post-processing approaches to eliminate small region objects while maintaining the overall RHSeg/HSeg image segmentation quality. I developed two new post-processing programs: (i)hsegnpixprune: selects a segmentation out of a segmentation hierarchy that has region objects no larger than a specified number of pixels, (ii)Modified hswo: performs best-merge region growing by the HSWO method in which merges between pairs of regions that both are larger than a “min_map_unit” size are not allowed. Must be initialized with the segmentation output from hsegnpixprune or from a segmentation selected from an RHSeg/HSeg segmentation hierarchy. 20 January 2016Global Croplands Working Group Meeting - GFSDA30

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 WorldView 2 image over Yolo County, CA from May 31, 2013.

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 RHSeg result with 289,214 region objects (hierarchical level 0).

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 HSegnpixprune result with 122,857 region objects (minimum_npix = 20,000).

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 HSWO result with 1742 region objects, initialized from 122,857 region object hsegnpixprune result (min_map_unit = 10,000).

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 RHSeg Hierarchical Level Select Hierarchical Level as earliest upward deflection of global dissimilarity – conservatively estimated as hierarchical level 16.

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 RHSeg result with 197,484 region objects (hierarchical level 16).

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 HSWO result with 1586 region objects, initialized from 197,484 region object RHSeg result (hlevel 16) (min_map_unit = 10,000).

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 HSWO result with 1742 region objects, initialized from 122,857 region object hsegnpixprune result (min_map_unit = 10,000).

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 RHSeg result with 289,214 region objects (hierarchical level 0).

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 HSWO result with 1766 region objects, initialized from 289,214 region object RHSeg result (hlevel 0) (min_map_unit = 10,000).

Post-processing Example: January 2016Global Croplands Working Group Meeting - GFSDA30 HSWO result with 1742 region objects, initialized from 122,857 region object hsegnpixprune result (min_map_unit = 10,000).

Question: Can HSeg be implemented on the Google Earth Engine? 21 Perhaps just HSeg and not RHSeg – don’t try to blend results across neighboring processing blocks. 20 January 2016Global Croplands Working Group Meeting - GFSDA30

Obtaining Very High Resolution NGA Image Data 22 The Web-Based Access and Retrieval Portal (WARP) was retired as of July 31, In mid-July I obtained access to the new Net-Centric Geospatial- Intelligence Discovery Services (NGDS) facility with my NASA PIV credentials, and I have maintained my access privileges since then. I have participated in some on-line training classes to familiarize myself with NGDS features. 20 January 2016Global Croplands Working Group Meeting - GFSDA30

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