SETTLEMENT MAPPING SYSTEMS

• US 20150016668A1
• Filed: 07/12/2013
• Published: 01/15/2015
• Est. Priority Date: 07/12/2013
• Status: Abandoned Application

• Current Assignee: UT-Battelle LLC
• Original Assignee: UT-Battelle LLC
• Inventors: Cheriyadat, Anil M., Bright, Eddie A., Chandola, Varun, Graesser, Jordan B., Bhaduri, Budhendra L.
• Application Number:
  US13/940,717
• Publication Number:
  US 20150016668A1
• Time in Patent Office: Days
• US Class Current: 382/103
• CPC Class Codes: G06V 10/50 by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
• CPC Class Codes: G06V 10/58 relating to hyperspectral data
• CPC Class Codes: G06V 20/176 Urban or other man-made structures

Abstract

A system detects settlements from images. A processor reads image data. The processor is programmed by processing only a portion of the image data designated a settlement by a user. The processor transforms the image data into a settlement classification or a non-settlement classification by discriminating pixels within the images based on the user'"'"'s prior designation. The system alters the appearance of the images rendered by processor to differentiate settlements from non-settlements.

Claims

1. A method of detecting settlements from satellite imagery using a computer processor that is preprogrammed comprising:

reading satellite imagery data;

designating only a portion of the satellite imagery data as a settlement;

transforming the satellite imagery data into a settlement classification or a non-settlement classification by discriminating pixels within a satellite image based on the designation of the portion of the satellite imagery data; and

altering the appearance of a visual display rendered by processing the satellite imagery data to differentiate settlements from non-settlements.

2. The method of claim 1 where the act of designating only a portion of the satellite imagery data designates less than about one percent of the pixels that comprise the satellite image.

3. The method of claim 1 where the act of designating only a portion of the satellite imagery data designates less than about five percent of the pixels that comprise the satellite image.

4. The method of claim 1 where the act of designating only a portion of the satellite imagery data comprises generating a discriminative model based on a feature analysis.

5. The method of claim 4 where the feature analysis comprises two or more of a histogram of oriented gradients, a gray level co-occurrence matrix, line support regions, a scale invariant feature transform, textrons, spectral ratios, and pseudo NDVI.

6. The method of claim 4 where the feature analysis comprises a histogram of oriented gradients, a gray level co-occurrence matrix, line support regions, a scale invariant feature transform, and textrons.

7. The method of claim 4 where the feature analysis comprises three or more of a histogram of oriented gradients, a gray level co-occurrence matrix, a scale invariant feature transform, textrons, and pseudo NDVI.

8. The method of claim 1 where the visual display comprises a visual map of the earth that highlights settlements through the superimposition if images.

20. The method of claim 1 where the one or more visual maps comprises a visual map of the earth that highlights settlements through the superimposition of images.

9. A programmable media comprising:

a graphical processing unit in communication with a memory element;

the graphical processing unit configured to detect one or more settlement regions from a bitmapped image based on the execution of programming code; and

the graphical processing unit further configured to identify one or more settlement through the execution of the programming code that generates one or more virtual maps that alters the appearance of all of the settlement regions in the one or more virtual maps based on a partial designation of the bitmapped image.

10. The programmable media of claim 9 where the graphical processing unit is configured to execute two or more multi-scale low level feature analysis to generate a discriminatory model based on training data.

11. The programmable media of claim 9 where the graphical processing unit is further configured to:

divide the bitmapped image into pixel blocks;

compute a multiscale feature for each pixel block;

map each pixel block to a dimensional vector; and

classify each pixel block into a settlement region or a non-settlement region.

12. The programmable media of claim 11 where the division of the bitmapped image is based on a neighborhood-based analysis.

13. The programmable media of claim 11 where the neighborhood-based analysis renders pixel labels in conditional random fields.

14. The programmable media of claim 9 where the graphical processing unit is further configured to:

filter the bitmapped image;

assign words to the filter response; and

render a visual image.

15. The programmable media of claim 9 where the partial designation of the bitmapped image comprises less than about one percent of the pixels that comprise the bitmapped image.

16. The programmable media of claim 9 where the graphical processing unit is configured to generate a discriminative model based on a programmed feature analysis.

17. The programmable media of claim 16 where the feature analysis comprises two or more of a histogram of oriented gradients, a gray level co-occurrence matrix, line support regions, a scale invariant feature transform, textrons, spectral ratios, and pseudo NDVI.

18. The programmable media of claim 16 where the feature analysis comprises a histogram of oriented gradients, a gray level co-occurrence matrix, line support regions, a scale invariant feature transform, and textrons.

19. The programmable media of claim 16 where the feature analysis comprises three or more of a histogram of oriented gradients, a gray level co-occurrence matrix, a scale invariant feature transform, textrons, and pseudo NDVI.