The spectral-spatial relationship of materials in a hyperspectral image cube is exploited to partially automate the creation of Geographic Information System (GIS) layers. The topological neighborhood preservation property of the Self Organizing Map (SOM) is clustered into six (partially overlapping) neighborhoods that are mapped into the image domain to locate in-scene structures of similar material type. GIS layers are abstracted through spatial logical and morphological operations on the six image domain material maps and a novel road finding algorithm connects road segments under significant tree-occlusion resulting in a contiguous road network. It is assumed that specific knowledge of the scene (e.g. endmember spectra) is not available. The results are eight separate high-quality GIS layers (Vegetation, Trees, Fields, Buildings, Major Buildings, Roadways, and Parking Areas) that follow the scene features of the hyperspectral image and are separately and automatically labeled. The material maps resulting from clustering the SOM have an 84.3% average accuracy, which increases to 93.9% after spatial processing into GIS layers

Howard, Torsten E.

Mendenhall, Michael J.

Peterson, Gilbert L.

English

The spectral-spatial relationship of materials in a hyperspectral image cube is exploited to partially automate the creation of geographic information system (GIS) layers. The topological neighborhood preservation property of the self-organizing map (SOM) is clustered into six (partially overlapping) neighborhoods that are mapped into the image domain to locate in-scene structures of similar material type. GIS layers are abstracted through spatial logical and morphological operations on the six image domain material maps and a novel road finding algorithm connects road segments under significant tree-occlusion resulting in a contiguous road network. It is assumed that specific knowledge of the scene (e.g. endmember spectra) is not available. The results are eight separate high-quality GIS layers (vegetation, trees, fields, buildings, major buildings, roadways, and parking areas) that follow the scene features of the hyperspectral image and are separately and automatically labeled. The material maps resulting from clustering the SOM have an 84.3% average accuracy, which increases to 93.9% after spatial processing into GIS layers

Howard, T. E.

Mendenhall, M. J.

Peterson, G. L.

NEUROSURGERY ENTHUSIASTIC WOMEN SOCIETY

Abstracting GIS layers from hyperspectral imagery

AFTI Scholar (Air Force Institute of Technology)

Air Force Institute of Technology AFIT Scholar Faculty Publications 8-2009 Abstracting GIS Layers from Hyperspectral Imagery Torsten E. Howard Air Force Institute of Technology Michael J. Mendenhall Air Force Institute of Technology Gilbert L. Peterson Air Force Institute of Technology Follow this and additional works at: https://scholar.afit.edu/facpub  Part of the Signal Processing Commons Recommended Citation T. E. Howard, M. J. Mendenhall and G. L. Peterson, "Abstracting GIS layers from hyperspectral imagery," 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, Grenoble, France, 2009, pp. 1-4, doi: 10.1109/WHISPERS.2009.5289023. This Conference Proceeding is brought to you for free and open access by AFIT Scholar. It has been accepted for inclusion in Faculty Publications by an authorized administrator of AFIT Scholar. For more information, please contact richard.mansfield@afit.edu. ABSTRACTING GIS LAYERS FROM HYPERSPECTRAL IMAGERYTorsten E. Howard, Michael J. Mendenhall, and Gilbert L. PetersonDepartment of Electrical & Computer EngineeringAir Force Institute of TechnologyWright-Patterson AFB, Ohio 45433ABSTRACTThe spectral-spatial relationship of materials in a hyperspectralimage cube is exploited to partially automate the creation of Geo-graphic Information System (GIS) layers. The topological neigh-borhood preservation property of the Self Organizing Map (SOM)is clustered into six (partially overlapping) neighborhoods that aremapped into the image domain to locate in-scene structures ofsimilar material type. GIS layers are abstracted through spatiallogi-cal and morphological operations on the six image domain materialmaps and a novel road finding algorithm connects road segments un-der significant tree-occlusion resulting in a contiguous road network.It is assumed that specific knowledge of the scene (e.g. endmemberspectra) is not available. The results are eight separate high-qualityGIS layers (Vegetation, Trees, Fields, Buildings, Major Buildings,Roadways, and Parking Areas) that follow the scene features of thehyperspectral image and are separately and automatically labeled.The material maps resulting from clustering the SOM have an 84.3%average accuracy, which increases to 93.9% after spatial processinginto GIS layers.Index Terms- Geographic Information Systems, Hyperspec-tral Image Processing, Self-Organizing Map, Morphological Opera-tions1. INTRODUCTIONGeographic Information Systems (GISs) use layers of vector data(shape files) to represent features in a scene. The often manual pro-cess begins with an orthorectified RGB image and outlines spatialfeatures of interest. The resulting layers are then viewed indepen-dent of the imagery providing an abstraction of the scene that is oftenmore usable to the observer than the original image itself.The previously described manual process is prone to human er-ror and is time consuming. Time and cost considerations limit fea-ture shapes to regular polygons and result in a slow update cycle.The manual effort is further complicated by scene clutter leavingthe abstraction open to interpretation. In order to verify the results,ground truth data is collected by visiting the area of interest. As newimagery is collected and orthorectified, existing shape files are oftenmanually adjusted and updated to reflect cumulative scene changes.As stated in [1], "' ... the biggest challenge is the currency of data,the authenticity of data,' and the primary complaint is 'that's not myhouse'''.Hyperspectral imagery, with its high spectral fidelity, holds thepossibility of partially or fully automating and significantly improv-ing this process, reducing time and cost of creating shape files, andincreasing the quality of the feature outlines. Special attention needsto be paid to the road network, and as noted in [2], "... primarily inurban areas the concern is with the road pattern, as this determinesu.s. Government work not protected by u.s. copyrightthe framework in which the complexities of urban land use changeusually take place."This work combines both spectral and spatial analysis of hy-perspectral imagery to automate shape file generation. The expec-tation is that our automated process will reduce human error, in-crease shape files creation rate, increase fidelity of shapes from sim-ple polygons to more meaningful feature outlines, and close theshape file / imagery gap as shape files are derived directly from thehyperspectral image.1.1. Spectral AnalysisA myriad of approaches exist for clustering hyperspectral data. Twonotable unsupervised clustering methods are K-means clustering andKohonen's Self-Organizing Map (SOM) [3]. Spectral analysis withK-Means clustering poses several challenges. The most notable aredetermining the number of clusters K and spatially connecting theresults. K-Means clustering is used to spectrally cluster pixels in [4],where K was varied from 2-16 yielding 101-695 separate spatialregions. Connecting these regions into meaningful abstractions thatare separately labeled is an insurmountable task.The SOM offers a more meaningful spectral clustering and pro-vides an intuitive mechanism for mapping spectral clusters to thespatial image domain. Examples of this by the authors in [5] vi-sualize the clustering of the SOM neighborhood topology and thecorresponding image domain representation. Data in the SOM areorganized on a two-dimensional lattice ofprocessing elements (PEs).Each PE has associated with it a prototype vector that is a quantiza-tion of the data. One may consider the representation of the SOM ashaving "pure" spectra mapped to given SOM PEs and mixed spectramapped to PEs between the pure spectra. This produces distinguish-able regions that can be correlated with specific materials of interest(MOIs). By identifying the regions on the SOM lattice and mappingthem to the spatial domain, a binary spatial image material map canbe developed which identifies the location of each material. Thisspectral-to-spatial mapping is reliable due to the global ordering andtopological preservation properties ofthe SOM. These material mapsare the basis for the spatial analysis using logical and morphologicaloperations.1.2. Spatial AnalysisMorphological close and open operations performed on the materialmaps with a known structuring element (SE) result in GIS layers.A SE is a binary valued matrix that can be as small as a (1 x 2)or (2 x I). The SE, similar to a sliding window, selects every sub-set of the material map equal in spatial size to the SE. The SE sizeis chosen to correspond to the minimum scene feature sizes (e.g.,where a minimum building size of 3 x 3 pixels prescribes a 3 x 32. METHODOLOGYThe scene used in this work is a 400 x 400 pixel synthetic hy-perspectral image cube with 194 spectral bands in the range 397.5- 2480.2nm shown in Fig. I. The image is resampled spectrallyto reflect a NASAIJPL AVIRIS-acquired image. It is atmospheri-cally corrected using the empirical line method and irrecoverablylost spectra due to atmospheric water absorption are removed .2.1. Self Organizing MapThe process begins with a clustering of the hyperspectral image us-ing a SaM where the learning parameters are specified in Table 1.The Normalized Difference Vegetative Index (NDVI) and exemplarsfrom build ing roofs and roadways seed the algorithm to cluster theSaM neighborhoods and label PEs with a material type. An exampleof three SaM clustered regions is shown in Fig. 2(a) .40 x 40 PEsDc = 0.8 / Dc = 5 X 10- 4(T = 1.0 / (T = 5 X 10- 46 X 106SizeInitial /final learn ratesInitial /final neighborhood widthsTraining steps2.6. Parking Lots Shape FileExemplar roadway spectra from the hyperspectral image as used inSec. 2.5 determines the SaM region in Fig.2(m) and the roadwaymaterial map in Fig. 2(n) . Parking areas are defined as having a spa-tial area between 2 x 2 and 10 x 10 pixels, which prescribes theTable 1. SaM learning parametersParameter Value2.4. Non-Tree Vegetation and Fields Shape FilesThe non-broad-leaf tree vegetative region shown in Fig. 2(e) resultswhen the tree region in Fig. 2(b) is logically deleted from the greenvegetation region show in Fig.2(a). The assoeiated material mapshown in Fig. 2(f) consists primarily of grasses. A field is defined ashaving a spatial area between 5 x 5 and 24 x 24 pixels, which pre-scribes the SE size. The fields shape file is generated by performing20 iterative open/close morphological operations while increasingthe SE by one in each dimension from the minimum (5 x 5) to themaximum size (24 x 24). The final step is to logically remove theroad network shape file shown in Fig. 2(p) (created later in Sec. 2.7).The final fields shape file (less road network) is shown in Fig. 2(g).The final non-tree vegetation shape file shown in Fig. 2(h) is cre-ated by logically deleting three shape files from the vegetation mate-rial map in Fig. 2(f) and then performing a morphological close witha 3 x 3 SE for aesthetic appeal. The three shape files are the treeshape file in Fig. 2(d), the final build ings shape file in Fig. 2(k) (cre-ated later in Sec. 2.5) and the road network shown in Fig. 2(p) (fromSec. 2.7) .2.5. Buildings and Major Buildings Shape FilesTo create the builidngs shape file, the £2- norm between an exem-plar rooftop spectra (manually) selected from the hyperspectral im-age and each PE's weight vector is computed. The distances arethresholded using a value of 2 generating the segmentation of theSaM shown in Fig.2(i). This segmentation is used to create therooftop material map in Fig. 20).The maps in Figs.2(f,g) and Figs.2(n,0) (created later) arelogically added to create a negative image mask (NIM) shownin Fig. 3(a) indicating where buildings are not located. The inverseof the NIM is added to the rooftop material map from Fig. 20) (alsoshown in Fig. 3(b)) result ing in the buildings map in Fig. 3(c) whichis then morphologically opened with a 3 x 3 SE resulting in Fig. 3(d).The minimum building size is ~ 3 x 3 which prescribes the SE size.Finally, the road network from Fig.2(p) (created later) is logicallyremoved resulting in in the building shape file in Fig. 2(k) .A major building is (arbitrarely) defined as five times the areaof a typical small building (c.g., a large shed) giving way to a 7 x 7pixel region. As such, the buildings shape file shown in Fig.3(d)is opened with a 7 x 7 SE deleting smaller building structures. Amorphological dilation using a 5 x 5 SE followed by a morpholog-ical close using a 3 x 3 SE produces the major build ings shape filein Fig. 2(1). For aesthetic appeal , the dilation increases the size of themajor buildings to make them more prominent, and the close ensuresthe shapes are without visual distractions from missing pixels.(I)NDVI = NIR - REDNIR + RED2.2. Vegetation Shape FileVegetation PEs are identified by way ofthe NDVI expressed in Eq. 1where RED is the average of the bands between 580nm and 690nmand NIR is the average between 725nm and 1l00nm. The vegeta-tion region of the SaM is the green area shown in Fig. 2(a) where athreshold of NOV I > 0.3 is applied to the SaM PEs. This region isnext divided into tree and non-tree vegetative regions .2.3. Tree Shape FileBroad-leaf trees are a subset of vegetation segmented using the stan-dard deviation of the near-infrared scatter normalized between 950and 1250nm. The standard deviation is thresholded at a value of 0.2where values above that threshold are declared as trees, the resultsofwhich are shown in Fig. 2(b). The resulting material map for treesis shown in Fig.2(c). To improve aesthetic appeal , the tree map isclosed with a 3 x 3 SE producing the result s in Fig. 2(d). The sizeof the SE is chosen to correspond with the minimum tree size givena spatial resolution of ~ 1.5m/pixel.Fig. 1. Synthetic hyperspectral image cube.SE). The morphological open and close operations are used in twoways: to improve aesthetic appeal of the GIS layers; and to isolatelarger features (e.g., fields). A morphological close fills in the spacebetween pixels clustered in an area smaller than the size of the SE.An open deletes pixels smaller than the size of the SE. Iterative openand close operations with a SE that increases in each dimens ion byone dur ing each iteration isolates larger scene features .Morphological operations perform similar to a conceptual un-derstanding of a scene as an observer sees it. That is, when a personviews a binary image, the tendency is to cluster close-lying pixels (amorphological close) into recognizable shapes, and to ignore speckleand other artifacts (a morphological open) that have no distinguish-able shape or order.(a) SOM topology (b) SOM with tree threshold (e) Tree material map(e) SOM with vegetation (less trees) threshold (I) Vegetation (less trees) material map '" (g) Final GIS fields shape file(d) Final GIS tree shape file(h) Final GIS vegetation (less trees) shape file(I) Final GIS major buildings shape file(m) SOM with roadway threshold (n) Roadway material map (0) Final GIS parking areas shape file (p) Final GIS roadway shape fileFig. 2. A pictor ial view of the process presented in this work that generates GIS shape files from a hyperspectral image cube.. '.......:.:~ : !.. . .{ ~ . .I .~[ !..~:. "'. '.(a) Negative image mask (NIM) (b) Rooftop material map (a) False color composite (b) Road network, parking areas, and bnildings(c) Rooftop map (white) and NIM (yellow) (d) After morphological open (3 x 3 SE)Fig. 3. Process to create the buildings shape file.SE size. The iterative morphological open/close method describedin Sec. 2.4 (used to abstract the fields shape fiIe) is used on the road-way material map from Fig. 2(n) to produce the parking areas shapefile in Fig. 2(0).2.7. Roadway Shape FileThe roadway shape file results from a novel road finding algo-rithm that requires a reduced roadway material map, a NIM, anda maximum NIM threshold. The reduced material map results af-ter performing a morpholog ical open operation with a 3 x 3 SEon the roadway material map from Fig.2(n) and logically delet-ing Figs. 2(f,g,I,0) and Fig. 3(d). The SE size prevents roads in thisscene, which are two-lane roads and typically on the order of 4-5pixels wide (roughly 6-7.5m), from being deleted. Possible non-roadnetwork pixels less than the SE size are removed (e.g. driveways). ANIM is constructed as in Sec. 2.5 with Figs. 2(f,g,I,0) and Fig.3(d).The road finding algorithm operates iteratively. During each it-eration, it considers every pair of roadway pixels and compares thenumber ofNIM pixels between them to the NIM threshold. The NIMthreshold starts at 0 and is incremented to some maximal value (8 inthis work). When the maximum threshold is reached, the algorithmlogically removes the buildings shape file, Fig. 3(d), from the NIM,resets the NIM threshold to 0, and iterates. The road finding algo-rithm requires an experimentally determined 16 iterations, resultingin the GIS roadway shape file presented in Fig. 2(p).3. RESULTS & CONCLUSIONEight specific GIS shape files - Fields, Trees, Vegetation, Non-TreeVegetation, Buildings, Major Buildings, Roadways, and Parking Ar-eas - arc abstracted from the hyperspectral image. Six are presented(c) Vegetation (less trees) and fields (d) TreesFig. 4. False color composite and final GIS layers.in Fig. 3. Spectral analysis resulted in an 84.3% average accuracyincreasing to 93.9% (as compared with ground truth) after spatialanalysis with logical and morpholog ical operat ions. It is importantto understand that the resulting shape files arc not intended to bea pixel-for-pixel representation of the ground truth and we did notmaximize the pixel-level classification . Rather, the process is de-signed for the qualitatively excellent understanding of the scene to ahuman observer as demonstrated in Fig. 4.4. DISCLAIMERThe views expressed in this thesis arc those of the author and do notreflect the official policy or position of the United States Air Force,Department of Defense, or the United States Government.5. REFERENCES[1] K2 Climb and Explorersweb, "Alpine style in science, solarpanels in space, google maps and the japanese man," May 2006.[2] Merrill K. Ridd and James D. Hipple, Eds., Remote Sensing ofHuman Settlements, vol. 5, American Society for Photogram-metry and Remote Sensing, 3 edition, 2006.[3] T. Kohonen, Self-Organizing Maps, Springer-Verlag, Berlin,Germany, 1995.[4] A. W. Meyer, D. W. Paglieroni, and C. Astaneh, "K-meansre-c1ustering algorithm ic options with quantifiable performancecomparisons ," The International Society for Optical Engineer-ing, Photonics, January 2003.[5] K. Tasdemir and E. Merenyi, "Exploiting data topology in visu-alization and clustering of self-organizing maps," It-"'EE Trans-actions on : Acceptedfor future publication Neural Networks.

Abstracting GIS Layers from Hyperspectral Imagery

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Abstracting GIS Layers from Hyperspectral Imagery

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