CA2483615A1 - Method of feature identification and analysis - Google Patents
Method of feature identification and analysis Download PDFInfo
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- CA2483615A1 CA2483615A1 CA002483615A CA2483615A CA2483615A1 CA 2483615 A1 CA2483615 A1 CA 2483615A1 CA 002483615 A CA002483615 A CA 002483615A CA 2483615 A CA2483615 A CA 2483615A CA 2483615 A1 CA2483615 A1 CA 2483615A1
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- tree
- crown
- species
- image
- stand
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
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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
Abstract
A method for efficiently and accurately inventorying image features such as timber, including steps of segmenting digital images into tree stands, segmenting tree stands into tree crowns, each tree crown having a tree crown area, classifying tree crowns based on specifies, and analyzing the tree cro wn classification to determine information about the individual tree crowns and aggregate tree stands. The tree crown area is used to determine physical information such as tree diameter breast height, tree stem volume and tree height. The tree crown area is also used to determine the value of timber in tree stands and parcels of land using tree stem volume and market price of timber per species.
Claims (56)
1. A method of feature identification and classification, said method comprising:
providing a digital image;
segmenting aggregate features from said image;
segmenting individual features from said aggregate features, classifying said individual features; and outputting the results of the steps of segmenting and classifying into a computer storage medium.
providing a digital image;
segmenting aggregate features from said image;
segmenting individual features from said aggregate features, classifying said individual features; and outputting the results of the steps of segmenting and classifying into a computer storage medium.
2. The method of claim 1, wherein said digital image depicts a forested area.
3. The method of claim 1, wherein said digital image has a resolution of about 1 meter.
4. The method of claim 1, wherein said digital image has a resolution of about 0.5 meters.
5. The method of claim 1, wherein said aggregate and individual segmentation steps are performed on aggregate and individual features located within said forested area by seeded region growing by placing a seed point at a pixel located at the center of a homogenous area located on the digital image and growing the homogenous area based on gradient values of surrounding image pixels.
6. The method of claim 1, wherein said aggregate and individual segmentation steps are performed by seeded region growing based on all values of said digital image pixels.
7. The method of claim 1, wherein said classification step is performed using supervised classification based on user-specified training sites.
8. The method of claim 1, wherein said output results are based on application of a model to the input image such that feature attributes are generated using said classification and said model.
9. A method for classifying and analyzing a digital image containing aggregate and individual features, said method comprising:
providing a digital image;
segmenting the digital image to produce a aggregate image file, said aggregate image file comprising a first series of polygons with boundary lines;
correcting said first series of polygons by said polygons to create larger polygons;
segmenting individual features within said larger polygons;
classifying individual features; and analyzing individual features.
providing a digital image;
segmenting the digital image to produce a aggregate image file, said aggregate image file comprising a first series of polygons with boundary lines;
correcting said first series of polygons by said polygons to create larger polygons;
segmenting individual features within said larger polygons;
classifying individual features; and analyzing individual features.
10. The method of claim 9, wherein said aggregate features are tree stands.
11. The method of claim 9, wherein said individual features are tree crowns.
12. The method of claim 9, wherein said aggregate segmentation includes growing aggregate segments by placing a seed point at a pixel located at the center of a homogenous area located on the digital image and growing homogenous tree stands based on gradient values image pixels surrounding the seed point pixel.
13. The method of claim 9, wherein said correction includes unsupervised and manual correction.
14. The method of claim 9, wherein said unsupervised correction is performed by adjusting an unsupervised aggregate segmentation algorithm to produce said larger polygons.
15. The method of claim 9, wherein said manual correction further includes identifying boundary lines of first series polygon segments and merging said segments.
16. The method of claim 15, wherein said boundary line identification comprises drawing a circumscribed line or polygon by joining the start and end points of said circumscribed line to merge said first series polygons.
17. The method of claim 9, wherein said individual feature segmentation step further includes filtering distractions from said digitized input image by applying control parameters and seeded region growing of homogenous areas within a selected aggregate segment.
18. The method of claim 17, wherein said control parameters include a prefiltering parameter, a seed point threshold, and a cut ratio.
19. The method of claim 18, wherein said prefiltering parameter is the size of a discrete gaussian filter mask between 0 and 30.
20. The method of 18, wherein said seed point threshold is a given local maximum value including RGB, luminance, color infrared or some other color space.
21. The method of claim 20, wherein said seed point threshold is between 0 and 256 or is some other color value parameter.
22. The method of claim 18, wherein the cut ratio is a threshold greyscale value between 0 and 256 or some other color value parameter.
23. The method of claim 9, wherein said classification includes supervised classification.
24. The method of claim 23, wherein said supervised classification further comprises identifying class or species of individual features and using a training procedure to classify all individual features based on the identified individual features.
25. The method of claim 9, wherein said analysis further comprises selecting a model based on said class or species of said individual features and applying said model to the classified individual features.
26. A method of timber identification and classification, said method comprising:
providing a digital image;
identifying trees in said image, and generating physical data and valuation information about said trees.
providing a digital image;
identifying trees in said image, and generating physical data and valuation information about said trees.
27. The method of claim 26, wherein said physical data includes tree crown area, tree diameter breast height, tree height, and tree stem volume.
28. The method of claim 26, wherein said tree identification further comprises tree stand delineation.
29. The method of claim 26, wherein said tree identification further comprises species classification.
30. The method of claim 29, wherein said valuation information is market value of said timber per tree and per tree stand based on market value of each identified tree species.
31. A computer assisted method of timber inventory analysis, said method comprising:
a) providing a digital input image;
b) segmenting tree stands within said digital input image;
c) correcting said stand segments;
d) segmenting individual tree crowns within said stand segments;
e) classifying species of said individual tree crowns, f) correcting misclassification of said classified species, and g) performing tree crown data analysis to generate physical and statistical data and valuation information about said tree crowns.
a) providing a digital input image;
b) segmenting tree stands within said digital input image;
c) correcting said stand segments;
d) segmenting individual tree crowns within said stand segments;
e) classifying species of said individual tree crowns, f) correcting misclassification of said classified species, and g) performing tree crown data analysis to generate physical and statistical data and valuation information about said tree crowns.
32. A computer assisted method of timber inventory analysis, said method comprising:
providing a digital image of a forested area;
segmenting tree stands within said digital image, said delineation comprising performing a gradient image analysis on said digital image to identify homogenous areas, selecting seed points in the center of each homogenous area, performing seeded region growing within each homogenous area based on gradient values
providing a digital image of a forested area;
segmenting tree stands within said digital image, said delineation comprising performing a gradient image analysis on said digital image to identify homogenous areas, selecting seed points in the center of each homogenous area, performing seeded region growing within each homogenous area based on gradient values
33 surrounding said seed points, drawing boundary lines around segmented homogenous tree stand, and saving resulting tree stand segments as vector files;
segmenting tree crowns within said stand segment vector files, said method comprising selecting an image with a stand segment vector file overlaid, filtering the digital image to eliminate unwanted detail, selecting seed points at the center of each tree crown present within the selected stand segment vector file, performing seeded region growing within each tree crown based on pixel values surrounding said seed points, drawing boundary lines around each segmented tree crown, and saving the resulting crown boundaries as vector files;
classifying tree species of said segmented tree crowns, said method comprising selecting an image with a crown vector file overlaid, manually identifying tree crowns of tree species present within the image, assigning species to the remaining unidentified tree crowns, and appending said species assignments to the crown vector file;
analyzing tree crown data, said method comprising selecting a crown vector file which includes species assignments of tree crowns, calculating the tree crown area of each tree, selecting a data model, calculating tree specific parameters based on the tree
segmenting tree crowns within said stand segment vector files, said method comprising selecting an image with a stand segment vector file overlaid, filtering the digital image to eliminate unwanted detail, selecting seed points at the center of each tree crown present within the selected stand segment vector file, performing seeded region growing within each tree crown based on pixel values surrounding said seed points, drawing boundary lines around each segmented tree crown, and saving the resulting crown boundaries as vector files;
classifying tree species of said segmented tree crowns, said method comprising selecting an image with a crown vector file overlaid, manually identifying tree crowns of tree species present within the image, assigning species to the remaining unidentified tree crowns, and appending said species assignments to the crown vector file;
analyzing tree crown data, said method comprising selecting a crown vector file which includes species assignments of tree crowns, calculating the tree crown area of each tree, selecting a data model, calculating tree specific parameters based on the tree
34 crown area and the data model, and storing resulting data in a vector file.
33. The method of claim 32, wherein said digital image has a resolution of about 0.7 to about 1.5.
34. The method of claim 32, wherein said digital image has a resolution of about 0.2 to about 0.7.
33. The method of claim 32, wherein said digital image has a resolution of about 0.7 to about 1.5.
34. The method of claim 32, wherein said digital image has a resolution of about 0.2 to about 0.7.
35. The method of claim 32, wherein said tree stand segmentation further comprises the step of subsampling the image to 2-5 meters per pixel.
36. The method of claim 32, wherein said tree stand segmentation further comprises the step of prefiltering the image to eliminate unnecessary details in the image.
37. The method of claim 32, wherein said tree stand segmentation is performed by an automatic segmentation algorithm.
38. The method of claim 32, wherein said tree stand segmentation further comprises seeded region growing based on pixel values.
39. The method of claim 32, wherein said tree stand segmentation further comprises the step of automatic unsupervised segmentation adjustment using the automatic segmentation algorithm.
40. The method of claim 32, wherein said tree stand segmentation further comprises manual segmentation adjustment by drawing a line or polygon to merge adjacent tree stands.
41. The method of claim 32, wherein said tree crown segmentation further comprises filtering the digital image by assigning values to control parameters to eliminate unwanted detail from the selected image with stand vector file overlay.
42. The method of claim 32, wherein said filtering includes a prefiltering parameter, a seedpoint threshold, and a cut ratio.
43. The method of claim 32, wherein said tree crown segmentation further comprises stopping seeded region growing when all gradient values above a cut ratio filtering parameter are selected and all gradient values below the cut ratio are excluded from said tree crown segments.
44. The method of claim 32, wherein said tree crown segmentation further comprises seeded region growing based on gradient values.
45. The method of claim 32, wherein said tree crown segmentation further comprises stopping seeded region growing when all pixel values above a cut ratio are selected and all gradient values below the cut ratio are excluded from said tree crown segments.
46. The method of claim 32, wherein said tree species classification further comprises identification of 1 to 5 tree species.
47. The method of claim 32, wherein said tree species classification further comprises a training procedure to assign species to unidentified tree crowns based on the manual identification.
48. The method of claim 47, wherein said manual identification includes identification of up to 5 tree crowns per species.
49. The method of claim 47, wherein said manual identification includes identification of 2-3 tree crowns per species.
50. The method of claim 32, wherein said classification further comprises supervised nearest neighborhood classification.
51. The method of claim 32, wherein said classification is manually corrected.
52. The method of claim 32, wherein said tree crown data analysis further comprises selecting a model based on the species classified within the selected crown vector file.
53. The method of claim 32, wherein tree crown data analysis further comprises generation of diameter breast height per tree and for the entire stand segment, volume per tree and for the entire stand segment, and height per tree.
54. The method of claim 32, wherein tree crown data analysis further comprises generation of timber value of each classified species within the selected stand segment, and timber value of the entire stand segment.
55. The method of claim 32, wherein tree crown data analysis further comprises storage of the resulting data for each tree in the crown vector file.
56. The method of claim 32, wherein tree crown data analysis further comprises storage of the resulting data for the entire stand segment in the stand vector file.
Applications Claiming Priority (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US10/139,001 | 2002-05-03 | ||
| US10/139,001 US7212670B1 (en) | 2002-05-03 | 2002-05-03 | Method of feature identification and analysis |
| PCT/US2003/014023 WO2003094109A2 (en) | 2002-05-03 | 2003-05-02 | Method od feature identification and analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CA2483615A1 true CA2483615A1 (en) | 2003-11-13 |
| CA2483615C CA2483615C (en) | 2012-07-17 |
Family
ID=29399295
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2483615A Expired - Lifetime CA2483615C (en) | 2002-05-03 | 2003-05-02 | Method of feature identification and analysis |
Country Status (7)
| Country | Link |
|---|---|
| US (1) | US7212670B1 (en) |
| EP (1) | EP1573669A2 (en) |
| AU (1) | AU2003228868A1 (en) |
| CA (1) | CA2483615C (en) |
| NO (1) | NO20044938L (en) |
| WO (1) | WO2003094109A2 (en) |
| ZA (1) | ZA200409668B (en) |
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| GB2371194B (en) * | 2000-10-06 | 2005-01-26 | Canon Kk | Image processing apparatus |
| US6873998B1 (en) * | 2000-10-18 | 2005-03-29 | Navteq North America, Llc | System and method for updating a geographic database using satellite imagery |
| EP1368972A2 (en) * | 2001-03-07 | 2003-12-10 | Internet Pro Video Limited | Scalable video coding using vector graphics |
-
2002
- 2002-05-03 US US10/139,001 patent/US7212670B1/en not_active Expired - Lifetime
-
2003
- 2003-05-02 AU AU2003228868A patent/AU2003228868A1/en not_active Abandoned
- 2003-05-02 WO PCT/US2003/014023 patent/WO2003094109A2/en not_active Application Discontinuation
- 2003-05-02 EP EP03726642A patent/EP1573669A2/en not_active Withdrawn
- 2003-05-02 CA CA2483615A patent/CA2483615C/en not_active Expired - Lifetime
-
2004
- 2004-11-12 NO NO20044938A patent/NO20044938L/en not_active Application Discontinuation
- 2004-11-30 ZA ZA200409668A patent/ZA200409668B/en unknown
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2005059867A1 (en) * | 2003-12-18 | 2005-06-30 | 1626628 Ontario Limited | System, apparatus and method for mapping |
| EP1614087A1 (en) * | 2003-12-18 | 2006-01-11 | 1626628 Ontario Limited | System, apparatus and method for mapping |
| EP1614087A4 (en) * | 2003-12-18 | 2009-03-04 | 1626628 Ontario Ltd | System, apparatus and method for mapping |
| US7856312B2 (en) | 2003-12-18 | 2010-12-21 | 1626628 Ontario Limited | System, apparatus and method for mapping |
| CN1795478B (en) * | 2003-12-18 | 2011-07-06 | 1626628安大略有限公司 | System, apparatus and method for mapping |
| US7979205B2 (en) | 2003-12-18 | 2011-07-12 | 1626628 Ontario Limited | System, apparatus and method for mapping |
| EP2381434A1 (en) * | 2003-12-18 | 2011-10-26 | 1626628 Ontario Limited | Apparatus for mapping |
| US8346471B2 (en) | 2003-12-18 | 2013-01-01 | 1626628 Ontario Limited | System, apparatus and method for mapping |
| CN102169650B (en) * | 2003-12-18 | 2013-04-10 | 1626628安大略有限公司 | System, apparatus and method for mapping |
| US8606518B2 (en) | 2003-12-18 | 2013-12-10 | 1626628 Ontario Limited | System, apparatus and method for mapping |
| US9292741B2 (en) | 2003-12-18 | 2016-03-22 | 1626628 Ontario Limited | System, apparatus and method for mapping |
| US20200082312A1 (en) * | 2017-09-27 | 2020-03-12 | Earth Observation Inc. | System and method for preserving forests and certifying the supply chain of lumber |
Also Published As
| Publication number | Publication date |
|---|---|
| AU2003228868A1 (en) | 2003-11-17 |
| WO2003094109A3 (en) | 2006-05-18 |
| NO20044938L (en) | 2005-01-19 |
| ZA200409668B (en) | 2005-09-27 |
| WO2003094109A2 (en) | 2003-11-13 |
| AU2003228868A8 (en) | 2003-11-17 |
| US7212670B1 (en) | 2007-05-01 |
| EP1573669A2 (en) | 2005-09-14 |
| CA2483615C (en) | 2012-07-17 |
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