Implementation Using MATLAB's Edge Function for Feature Extraction

Feature extraction from remote sensing images represents a crucial task in image processing. By leveraging MATLAB's Edge function, we can effectively achieve this objective. The Edge function enables detection of image boundaries, facilitating the extraction of feature contours through various edge detection algorithms such as Sobel, Canny, or Prewitt operators. Prior to feature extraction, remote sensing images require preprocessing steps including noise reduction and contrast enhancement to optimize edge detection results. The implementation typically involves loading the image using imread(), converting to grayscale if necessary, and applying preprocessing filters before executing the Edge function with specified parameters. By adjusting parameters like threshold values and detection method selection, we can control the precision and quantity of extracted edges. This process yields valuable positional and shape information of features from remote sensing imagery, establishing a foundation for subsequent analysis and applications such as land cover classification or object recognition. The code implementation may include steps like: edge(gray_image, 'Canny', threshold_value) where threshold_value determines sensitivity to gradient changes.