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James C. Tilton Code 606.3 Computational & Information Sciences and Technology Office NASA Goddard Space Flight Center July 16, 2015 update National Aeronautics.

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Presentation on theme: "James C. Tilton Code 606.3 Computational & Information Sciences and Technology Office NASA Goddard Space Flight Center July 16, 2015 update National Aeronautics."— Presentation transcript:

1 James C. Tilton Code 606.3 Computational & Information Sciences and Technology Office NASA Goddard Space Flight Center July 16, 2015 update National Aeronautics and Space Administration www.nasa.gov

2 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. 4454-4467. 15 August 2013USGS Global Croplands Working Group Meeting

3 I MPROVED I NCORPORATION OF S PATIAL I NFORMATION I NTO HS EG HSeg Background (cont’d): HSeg modifies HSWO by also aggregating spectrally similar but spatially separated region objects into groups of region objects – called region classes. The HSeg Flowchart: S wght, ranging from 0 to 1, controls the relative importance of merges between adjacent regions versus non-adjacent regions. 15 August 2013USGS Global Croplands Working Group Meeting3

4 I MPROVED I NCORPORATION OF S PATIAL I NFORMATION I NTO HS EG HSeg Background (cont’d): The RHSeg approximation of HSeg has an efficient parallel implementation useful for processing large images: L r is determined as the number of times the input image must be subdivided to achieve a small enough image size for efficient processing with HSeg. The rhseg(L,X) function: N min is equal to ¼ the number of pixels in the subimage processed at the deepest level of recursion. 15 August 2013USGS Global Croplands Working Group Meeting4

5 Incorporating Edge Information into HSWO/HSeg/RHSeg 5 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, 1968. 15 August 2013USGS Global Croplands Working Group Meeting

6 Frei-Chen Edge Difference Operator 6 15 August 2013USGS Global Croplands Working Group Meeting

7 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 0.07. 7 15 August 2013USGS Global Croplands Working Group Meeting

8 Utilizing HSeg in the GFSAD30 Project 8 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

9 RHSeg/HSeg together with HSegLearn 9 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

10 RHSeg/HSeg together with HSegLearn 10 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

11 HSegLearn Example 11 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.

12 Pre-processing: 12 Develop pre-processing techniques to “enhance” the imagery data prior to processing with HSeg. For example 15x15 median filter: 15 August 2013USGS Global Croplands Working Group Meeting

13 Pre-processing: 13 HSeg result at about 840 region classes for the original (left) and 15x15 median filtered image (right): 15 August 2013USGS Global Croplands Working Group Meeting

14 Pre-processing – other computed features: 14 15 August 2013USGS Global Croplands Working Group Meeting NDVIsBands CombinationBand Names NDVI1WV2: (band7−band5)/(band7+band5)NIR1 and Red bands NDVI2WV2: (band8−band6)/(band8+band6)NIR2 and Red-Edge NDVI3WV2: (band8−band4)/(band8+band4)NIR2 and Yellow NDVI4WV2: (band6−band1)/(band6+band1)Red-Edge and Coastal Blue NDVI5WV2: (band6−band5)/(band6+band5)Red-Edge and Red

15 Utilizing HSeg in the GFSAD30 Project 15 Kamini Yadav is experimenting with using a Random Forest classification package available for the R programming language. To facilitate interfacing HSeg with this package, I am considering writing a program in R to read the HSeg output files. This will allow more convenient post-processing of HSeg image segmentation results to provide the needed features for the Random Forest classification. 15 August 2013USGS Global Croplands Working Group Meeting

16 Obtaining Very High Resolution NGA 16 The Web-Based Access and Retrieval Portal (WARP) will be definitely retired as of July 31, 2015. Just last week I obtained access to the new Net-Centric Geospatial- Intelligence Discovery Services facility with my PIV credentials. I also recently gained access to the DigitialGlobe EnhancedView Web Hosting Service. 15 August 2013USGS Global Croplands Working Group Meeting

17 15 August 2013USGS Global Croplands Working Group Meeting17

18 15 August 2013USGS Global Croplands Working Group Meeting18

19 SignOffPage 19 15 August 2013USGS Global Croplands Working Group Meeting

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