MATLAB Road Extraction from Remote Sensing Images Using Hough Detection

The Hough transform serves as a robust technique for detecting linear features in digital images. In MATLAB implementations for remote sensing applications, this algorithm is particularly effective for road extraction tasks. The core mechanism involves converting image points from Cartesian coordinates (x, y) to a parameter space (Hough space) where they can be represented as sinusoidal curves. The intersection points of these curves in the parameter space correspond to potential lines in the original image. Key MATLAB functions for implementation include: - hough(): Generates the Hough transform matrix from edge-detected images - houghpeaks(): Identifies peak points in the Hough transform representing dominant lines - houghlines(): Extracts line segments based on the detected peaks For road extraction applications, the workflow typically involves: 1. Preprocessing input遥感 images using edge detection operators like Canny or Sobel 2. Applying the Hough transform to identify candidate road boundaries 3. Implementing post-processing to connect discontinuous segments and remove false positives The algorithm demonstrates particular strength in detecting incomplete or partially obscured road networks, making it invaluable for analyzing satellite and aerial imagery. Through parameter optimization (theta and rho resolution adjustments), users can balance detection accuracy and computational efficiency. The Hough transform remains a fundamental technique in image processing with extensive applications across remote sensing, computer vision, and automated mapping systems.