WO2006050326A2 - Spectral geographic information system - Google Patents
Spectral geographic information system Download PDFInfo
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- WO2006050326A2 WO2006050326A2 PCT/US2005/039387 US2005039387W WO2006050326A2 WO 2006050326 A2 WO2006050326 A2 WO 2006050326A2 US 2005039387 W US2005039387 W US 2005039387W WO 2006050326 A2 WO2006050326 A2 WO 2006050326A2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
Definitions
- This invention relates to a system specifically adapted for use in spectral geographic information systems. More particularly, this invention relates to a system configured to translate large information databases into a more compact database for analysis using various hyperspectral techniques to facilitate the location of targets.
- Hyperspectral imagery consists of hundreds of "spectra," or measurements of reflected or emitted energy. Hyperspectral sensors scan many channels across a relatively narrow bandwidth and provide detailed information about target spatial and spectral patterns. Absorption and emission bands of given substances often occur within very narrow bandwidths. They allow high-resolution, hyperspectral sensors to distinguish the properties of the substances to a finer degree than an ordinary broadband sensor. The intensity of this energy can be measured at various wavelengths.
- MSI multispectral imaging
- multiple images of a scene or object are created using light from different parts of the spectrum. If the proper wavelengths are selected, multispectral images can be used to detect many militarily important items such as camouflage, thermal emissions and hazardous wastes to name a few.
- a primary goal of using multispectral/hyperspectral remote sensing image data is to discriminate, classify, identify as well as quantify materials present in the image.
- Another important application is subpixel target detection, which allows one to detect targets of interest with sizes smaller than the pixel resolution, and abundance estimation, which allows one to detect concentrations of different signature spectra present in pixels.
- the difficulty arises in the fact that a scene pixel is mixed linearly or nonlinearly by different materials resident in the pixel where direct applications of commonly used image analysis techniques generally do not work well.
- Hyperspectral imaging is a passive technique (i.e., depends upon the sun or some other independent illumination source) that creates a large number of images from contiguous, rather than disjoint, regions of the spectrum, typically, with much finer resolution. This increases sampling of the spectrum and provides a great increase in information. Many remote sensing tasks which are impractical or impossible can be accomplished with HSI. For example, detection of chemical or biological weapons, bomb damage assessment of underground structures, and foliage penetration to detect troops and vehicles are just a few potential HSI missions.
- Hyperspectral imaging technology uses hundreds of very narrow wavelength bands to "see" reflected energy from objects on the ground.
- spectral fingerprints This energy appears in the form of "spectral fingerprints" across the light spectrum; enables collection of much more detailed data; and produces a much higher spectral resolution of a scene than possible using other remote sensing technologies.
- special algorithms repetitive, problem-solving mathematical calculations — then assess them to differentiate various natural and manmade substances from one another.
- “Signature” libraries may also be used to identify specific materials — e.g., rooftops, parking lots, grass, or mud — by comparing a library's pre-existing reference catalogs with freshly taken hyperspectral images of the battlefield from space.
- Image processing equipment then portrays the various types of terrain and objects upon it in different colors forming a "color cube," each based on the wavelength of the reflected energy captured by the image. These colors are subsequently "translated” into maps that correspond to certain types of material or objects to detect or identify military targets such as a tank or a mobile missile launcher. Algorithms can also categorize types of terrain and vegetation (useful, for example, in counter-narcotic operations), detecting features such as disturbed soil, stressed vegetation, and whether the ground will support the movement of military vehicles.
- a method for target detection in a hyperspectral image comprising the steps of loading the hyperspectral image into computer readable memory.
- a database is generated based upon the hyperspectral image wherein the database comprises a shape file portion, a hyperspectral global data portion and a hyperspectral segment data portion.
- a target spectrum is compared to the hyperspectral segment data and the location of a match between said target spectra and said hyperspectral segment data is determined. Finally, the location of the targets is displayed.
- a method for the compression of an Adaptive Background/Foreground Analysis (AFBA) hyperspectral image database comprising the steps of reading the AFBA database into computer readable memory for processing, wherein the AFBA database comprises a file header portion, a global data portion and segment data records, and translating the AFBA database to a Spectral Geographic Information System (SGIS) database, wherein the SGIS database comprises a shape file portion, a hyperspectral global data portion and a hyperspectral segment data portion.
- SGIS Spectral Geographic Information System
- a method for the computer based analysis of hyperspectral images comprising the steps of loading a hyperspectral image into memory for processing by the computer.
- a computer readable database is created based on the hyperspectral image wherein the database comprises a shape file portion, a hyperspectral global data portion and a hyperspectral segment data portion.
- a computer program product tangibly stored on a computer-readable medium, for the analysis of hyperspectral images comprising instructions operable to cause a programmable processor to open and read a digital image file containing hyperspectral spectra information associated with a predetermined geographic location and then generate a database associated with the digital image file, the database comprising a shape file portion, a hyperspectral global data portion and a hyperspectral segment data portion.
- a method for the analysis of a hyperspectral image comprising the steps of reading the hyperspectral image into computer readable memory for processing, wherein the hyperspectral image comprises a plurality of pixels, each pixel comprising predetermined spectral data.
- the hyperspectral image is translated into a database file, wherein the database file comprises a shape file portion, a hyperspectral global data portion and a hyperspectral segment data portion.
- an endmember spectrum is selected wherein the endmember spectrum is representative of a predetermined military target and a match between the endmember spectra and the hyperspectral segment data portion is located.
- Adaptive Background/Foreground Analysis (AFBA) database to a Spectral Geographic Information System (SGIS) database comprising the steps of loading the AFBA database into computer readable memory and converting the AFBA database to an SGIS database.
- the conversion further comprises the steps of writing a global header portion to computer readable memory and then writing segment data to the computer readable memory, wherein the segment data is representative of each record contained in the AFBA database.
- a shape file header portion and a shape file index portion are written to the computer readable memory.
- Figure 1 is a flow diagram of a database translation in accordance with an embodiment of the invention.
- Figure 2 is a top level flow control diagram of a database converter in accordance with an embodiment of the invention
- Figure 3a is a detail block diagram of a hyperspectral image database converter in accordance with an embodiment of the invention
- Figure 3b is a continuation of a detail block diagram of a hyperspectral image database converter in accordance with an embodiment of the invention.
- FIG. 4 is a block diagram of a hyperspectral image database interface in accordance with an embodiment of the invention.
- FIG. 5 is a block diagram of the various software modules for the analysis and display of hyperspectral images in accordance with an embodiment of the invention
- Figure 6 is a typical screen shot of an image of a hyperspectral geographic image database in accordance with an embodiment of the invention.
- Figure 7 is a typical screen shot of an image of a hyperspectral geographic image database with target locations identified in accordance with an embodiment of the invention.
- Imaging applications where the collection of geographic imaging data is used for the location of specific target types. More specifically, the system lends itself particularly well to the identification and location of military targets typically associated with air to ground geographic imaging technology.
- the system described herein differs from the prior art because it provides a means to translate large graphic image data files into a compact, highly efficient database file structure that provides for the real time analysis and display of hyperspectral image data.
- Fig. 1 shows a block diagram for the conversion of a hyperspectral geographic image database.
- Current imaging technology captures the image data in a database structure for analysis by a technique commonly referred to as Adaptive Foreground/Background Analysis (AFBA) which can be described as a three- dimensional cube having RGB and grayscale image data.
- AFBA database structure is shown as block 12.
- the AFBA database typically may comprise a file header 12a, a section of global data 12b and an array of image data or segment data 12c. This database is typically very large and can be of the order of 260 megabytes in size.
- a translator 13 may be provided that transforms the large image database to a more efficient and much smaller Spectral Geographic Information System (SGIS) file which facilitates the rapid analysis and display of information.
- An SGIS database structure 14 may be comprised of a shape file portion 16, a hyperspectral global data portion 18 and hyperspectral segment data portion 20.
- the shape file portion 16 may be comprised of a shape library 16a, a shape index 16b and a hyperspectral database portion 16c.
- the shape library 16a may contain an array of polygons which may be used to describe the map image to be analyzed.
- the shape index 16b acts as the index to the polygon shapes contained in the library 16a and the hyperspectral database portion may be a database format file that locates the polygons to generate the map image to be analyzed.
- Fig. 2 shows the translation of the AFBA database to the SGIS database in more detail.
- the AFBA database 12 is read into memory.
- Each record of this database may include information which describes the geographic image and may include header data, spectral channel data, image targets, and image endmembers.
- Image target data may further include segment data such as segment vertices, channel indices, target indices, endmember indices, target-to-endmember match indices, target abundances and end member abundances.
- the rather large AFBA database is converted to the smaller more efficient SGIS database.
- a file header portion is generated.
- the translator 13 For each record of the AFBA database 12, the translator 13 takes the number of vertices and the coordinates of the vertices to generate the shape file portion 16 of the SGIS database.
- the translator 13 creates the hyperspectral global data portion 18. This may require the translator 13 to write field names for each channel, wavelength and FWHM.
- the translator 13 may also add records to the SGIS database for each channel the channel number, the wavelength and the FWHM.
- the translator 13 will also create names for the targets and endmembers.
- the SGIS database structure may be of any well known type such as DB3 or the like. [031]
- the translator 13 writes the segment data 20 to the SGIS database.
- Segment data 20 may comprise for each record of the image database the byte position, the segment pixel, vertices, channels, targets, endmembers, target- endmember matches, target abundances and endmember abundances.
- the translator 13 writes the shape file portion 16 to the SGIS database.
- the shape file library 16a is created.
- the polygon points and polygon parts that comprise the shape file library 16a are created and written to the SGIS database 14.
- the shape index 16b is generated and written to the SGIS database 14.
- the hyperspectral database portion 16c is created by first creating a database header and then for each record in the AFBA database the shape attributes are written as values in the SGIS database.
- FigS. 3a and 3b further describe the AFBA to SGIS conversion algorithm in still more detail.
- the AFBA database contains the data associated with a large geographic image file that may be analyzed by a computer to determine target locations.
- the data structure of the AFBA database 12 is loaded.
- the data structure for the SGIS database 14 is loaded.
- the shape file portion 16 is created which includes the steps shown at block 116 where the corner coordinates of shape are determined based on the AFBA database.
- the number of shapes in the SGIS database 14 is set to the same number of segments found in the segment data 12c in the AFBA database 12.
- the array of records is created for the shape library 16a and space is provided to hold record header and record content for each shape.
- a long integer array is created to contain the shape index 16b information.
- a short integer array is created to contain the shape library 16a size.
- the translator 13 enters a main loop which is repeated for each shape in the shape library 16a. [034] Referring now to Fig. 3b, the main loop 126 is described. For each shape, the translator 13 will cycle through the following steps to populate the SGIS database with the geographic imaging data.
- the database record structure is created.
- the number of polygon vertices associated with the current shape is determined based on the data contained in the AFBA database 12.
- memory is provided to contain the data associated with the polygon vertices.
- the vertices data contained in the AFBA database 12 is translated to the shape file structure 16 of the SGIS database.
- the image data from the AFBA database 12 is analyzed to determine the number of times the current polygon can be used in the geographic data being converted.
- record space in the SGIS database is allocated to contain the data associated with the polygon placement based on the image data.
- rings, holes, or both, that may be contained in the polygon are repaired to form a continuous planar polygon with no voids.
- the points associated with the polygon are set in the shape file 16 and at block 146 the coordinates of the bounding box which determines the actual shape of the polygon are determined.
- the length of the database record is determined and set by the byte length of the polygon and at block 150 error checking is performed and this is verified by checking the cumulative running byte count.
- the number of vertices and locations of the current shape are written to the SGIS database.
- the translator 13 returns to the beginning of the main loop 126 and repeats for each shape found in the shape library 16a.
- the AFBA database 12 may contain the file header portion 12a, the global data portion 12b and the segment data 12c.
- the global data portion 12b is shown in more detail at block 202 and may comprise wavelength data, wavelength Full-Width Half-Maximum (FWHM), target spectra which describe the targets being searched for and endmember spectra.
- the segment data 12c is shown in more detail at block 204 and each segment may contain segment vertices, channel indices, target indices, endmember indices, target to endmember match indices, target cluster rejection filter, target abundances and endmember abundances.
- GUI graphical user interface
- a main form which allows the user to select from a number of menu choices which provides the services that may load the SGIS database 12 and display the background map, shapes, and set up the layers, and generally act as the starting point for the creation and analysis of the SGIS database 14.
- form 1 is provided which allows a user to select the region of a map image to be analyzed. Typically the user may use a mouse to click and drag a box to determine the region of a map the user wishes to analyze. Based on this selection, at block 304 a menu is displayed to the user which indicates the target abundances and endmember abundances that are contained in the region selected in block 302.
- the endmember spectral data associated with the selected region may be displayed as a chart for further analysis.
- a table of data may be displayed which shows the channel, wavelength and FWHM of each endmember in the selected region.
- the user may change the look and contents of the splash screen associated with program 301.
- the shape attributes associated with the selected region may be displayed.
- module 1 is provided which performs the various conversion and analysis functions of the program 301. These functions, as previously discussed, may include determining the shape record structure, the segment records, reading the global data, shape data and segment data.
- module 2 is provided which also performs various conversion and analysis functions and may include determining target and endmember abundances, endmember spectra, spectral subsets and target abundances. Module 2 also displays the forms of data previously described.
- a typical screen shot of a geographic map image 400 is shown.
- this image 400 comprises an underlying background map 402 which may be a grayscale or RGB image captured for analysis.
- An array of shapes or polygons 404 are displayed onto the map 402 based upon the SGIS conversion. The location and orientation of the shapes 404 are also determined by the SGIS conversion.
- Fig. 7 shows the location of specific targets 406 based on the criteria established by the endmember spectral data criteria that was preselected by the user.
- the SGIS database 14 is analyzed to determine the location on the map 402 (Fig. 6) of targets that match the endmember spectral criteria that may be established in the program 301. In this manner, the user can select a predetermined target based on endmember spectral data criteria and quickly locate areas of a map that match the selected endmember spectral data criteria. The exact longitude and latitude of each occurrence of a target can then be determined.
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EP05821339A EP1812875A2 (en) | 2004-11-02 | 2005-11-01 | Spectral geographic information system |
CA2586589A CA2586589C (en) | 2004-11-02 | 2005-11-01 | Spectral geographic information system |
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US10/979,514 | 2004-11-02 | ||
US10/979,514 US7450761B2 (en) | 2004-11-02 | 2004-11-02 | Spectral geographic information system |
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EP (1) | EP1812875A2 (en) |
CA (1) | CA2586589C (en) |
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ITTO20070620A1 (en) * | 2007-08-31 | 2009-03-01 | Giancarlo Capaccio | SYSTEM AND METHOD FOR PRESENTING VISUAL DATA DETACHED IN MULTI-SPECTRAL IMAGES, MERGER, AND THREE SPACE DIMENSIONS. |
US8326048B2 (en) | 2007-10-04 | 2012-12-04 | Microsoft Corporation | Geo-relevance for images |
KR100963797B1 (en) * | 2008-02-27 | 2010-06-17 | 아주대학교산학협력단 | Method for realtime target detection based on reduced complexity hyperspectral processing |
WO2014078858A1 (en) * | 2012-11-19 | 2014-05-22 | Altria Client Services Inc. | Hyperspectral imaging system for monitoring agricultural products during processing and manufacturing |
CN104463897B (en) * | 2014-12-30 | 2017-11-10 | 电子科技大学 | A kind of method of hyper-spectral target detection |
US10197504B2 (en) | 2016-10-10 | 2019-02-05 | Altria Client Services Llc | Method and system of detecting foreign materials within an agricultural product stream |
US10268889B2 (en) * | 2017-03-31 | 2019-04-23 | The Boeing Company | System for target material detection |
JP6843439B2 (en) | 2017-05-17 | 2021-03-17 | エバ・ジャパン 株式会社 | Information retrieval system and method, information retrieval program |
CN108268902A (en) * | 2018-01-26 | 2018-07-10 | 厦门大学 | High spectrum image transformation and substance detection identifying system and method based on recurrence plot |
JP6951753B2 (en) * | 2018-03-27 | 2021-10-20 | エバ・ジャパン 株式会社 | Information search system and program |
CN112036258B (en) * | 2020-08-07 | 2022-05-13 | 广东海洋大学 | Remote sensing image target detection algorithm based on projection zero-ization recurrent neural network |
CN112949416B (en) * | 2021-02-04 | 2022-10-04 | 东华理工大学 | Supervised hyperspectral multiscale graph volume integral classification method |
CN116128982B (en) * | 2022-12-26 | 2023-09-26 | 陕西科技大学 | Color grading/color measurement method, system, equipment and medium based on hyperspectral image |
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US20060093223A1 (en) | 2006-05-04 |
US7450761B2 (en) | 2008-11-11 |
WO2006050326A3 (en) | 2006-10-19 |
CA2586589C (en) | 2013-01-08 |
CA2586589A1 (en) | 2006-05-11 |
EP1812875A2 (en) | 2007-08-01 |
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