Feature extraction based on segmentation technique with spectral band ratios and shape characteristics

Feature extraction is one of the main goals in many remote sensing analyses. After high-resolution imagery became more available, it became possible to extract more detailed and specific features. Thus, considerable image segmentation algorithms have been developed, because traditional pixel-based analysis proved insufficient for high-resolution imagery due to its inability to handle the internal variability of complex scenes. However, the individual segmentation method, which simply uses color layers, is limited in its ability to extract various target features with different spectral and shape characteristics. This study aims to evaluate a feature extraction method based on a segmentation technique with spectral band ratios and shape characteristics. We tested the extraction of diverse target features-such as buildings, vegetation, water, and shadows-from high-resolution multispectral satellite image and used the result to draw the appropriate band ratios and shape features for each specific feature extraction.