Detecting roads in aerial images using feature-based classifiers

• US 20060078205A1
• Filed: 10/08/2004
• Published: 04/13/2006
• Est. Priority Date: 10/08/2004
• Status: Active Grant

• Current Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Original Assignee: Mitsubishi Electric Research Laboratories, Inc. (Mitsubishi Electric Corporation)
• Inventors: Porikli, Fatih M., Shao, Jie
• Granted Patent: US 7,359,555 B2
• Time in Patent Office: Days
• US Class Current: 382/204
• CPC Class Codes: G03B 15/006 Apparatus mounted on flying objects
• CPC Class Codes: G06F 18/254 of classification results, e.g. of results related to same input data
• CPC Class Codes: G06V 20/13 Satellite images

Abstract

A method detects roads in an aerial image of ground topology by determining low-level features, such as intensities and gradients, for each pixel in the aerial image, determining middle-level features, such as an orientation for each pixel from the low-level features, and determining high-level features from the middle-level features. Each high-level feature is assigned a probability, and the probabilities of the high-level features for each pixel are normalized and aggregated to a single probability that the pixel is associated with a road.

Claims

1. A method for detecting roads in an aerial image of ground topology, comprising:

determining low-level features for each pixel in an aerial image of ground topology;

determining middle-level features for each pixel from the low-level features;

placing a first window over each pixel, the first window including adjacent pixels to each pixel;

determining high-level features for all of the pixels in the first window from the middle-level features of all of the pixels in the first window; and

assigning a single probability to each pixel based on the high-level features, the single probability indicating a likelihood that the pixel is associated with a road.

2. The method of claim 1, in which the low-level features are intensity values of the pixels, and gradients of the intensity values, and the mid-level features include orientations, edges and contours related to visual characteristics of the ground topology, and the high-level features include orientation, contour, color, line, and texture features.

3. The method of claim 1, in which the visual characteristics are related to physical characteristics of the ground topology, the physical characteristics including geometrical, textural, and contextual features.

4. The method of claim 1, further comprising:

filtering the aerial image in a pre-processing step by applying an adaptive 2D Gaussian low-pass Wiener filter to each pixel, the pixel using a 5×

5 pixel neighborhood to estimate a mean and a standard deviation of a local gradient of each pixel.

5. The method of claim 1, further comprising:

partitioning the aerial image into non-overlapping blocks; and

determining an orientation of each pixel in each block.

6. The method of claim 1, in which the probabilities form a probability image, and further comprising:

filtering the probability image a median filter.

7. The method of claim 1, in which the low-level features include intensity of the pixels, and further comprising:

filtering the intensity values of the pixels;

determining local intensity gradients of each pixel;

constructing a gradient magnitude image from the local gradients; and

filtering the gradient magnitudes to construct an orientation image, each pixel in the orientation image having an orientation.

8. The method of claim 1, in which the first windows overlap.

9. The method of claim 7, further comprising:

constructing an edge image from the gradient magnitude image, the edge image including edge pixels; and

constructing a contour image from the edge image.

10. The method of claim 9, further comprising:

constructing an orientation histogram within a second window centered at each pixel using the local gradients; and

constructing contours by connecting the edge pixels.

11. The method of claim 10, in which the orientation histogram associated with each second window has twelve bins in a range between 0 and π

radians, the radians corresponding to the edge pixels.

12. The method of claim 11, in which a first high-level feature is a maximum value of the orientation histogram, a second high-level feature is an entropy of the orientation histogram, a third high-level feature is an entropy of a weighted orientation histogram, a fourth high-level feature is a mean of the orientation histogram, a fifth high-level feature is a variance of the orientation histogram, a sixth high-level feature is a similarity measure between the orientation histogram and an idealized single-modal density function model of the road, and a seventh high-level feature is a weighted similarity measure.

13. The method of claim 1, further comprising:

normalizing values associated with the high level features to a common range of normalized probabilities.

14. The method of claim 13, in which the normalization uses linear and non-linear functions.

15. The method of claim 13, in which the assigning of the single probability to each pixel further comprises:

aggregating the normalized probabilities of the high-level features for each pixel.