Feature Extraction from Spatial Satellite and Aircraft Images

Feature extraction is performed on spatial satellite and aircraft images, with extracted features including area, perimeter, compactness, and eccentricity. During the feature extraction process, various algorithms and techniques can be applied, such as edge detection methods (Sobel, Canny operators), color analysis through HSV/RGB channel processing, and texture descriptors (GLCM, LBP). In code implementation, these features can be calculated using image processing libraries like OpenCV - where area represents the total number of pixels in the segmented region, perimeter is computed using chain code approximation or contour tracing algorithms, compactness is derived from (perimeter²/area) ratio indicating shape regularity, and eccentricity is calculated from moment invariants or ellipse fitting to describe elongation characteristics. Feature extraction serves as a crucial step in image processing and computer vision, enabling better understanding and analysis of image contents. By extracting these geometric and shape-based features, we can more effectively describe and compare differences and similarities between various images. Furthermore, feature extraction finds applications in object detection (using template matching or ML classifiers), image classification (through feature vectors fed into SVM/neural networks), and image retrieval systems, providing extensive possibilities for advanced image analysis and interpretation.