Image transformation techniques play a crucial role in remote sensing and digital image processing by converting original image data into new forms that enhance information content, improve visual interpretation, or prepare data for further analysis. These techniques mathematically manipulate pixel values, often combining information from multiple spectral bands, to highlight specific surface features or suppress unwanted effects such as noise and redundancy. One of the most widely used image transformation techniques is band ratioing. In this method, pixel values of one spectral band are divided by corresponding pixel values of another band. Band ratios help minimize the effects of illumination differences caused by terrain, shadows, or sensor angle, while emphasizing spectral differences related to surface materials. For example, ratios involving near-infrared and red bands are commonly used to enhance vegetation characteristics, as healthy vegetation strongly reflects near-infrared radiation while absorbing red light. Vegetation indices are specialized transformations derived from band ratios and arithmetic combinations of spectral bands. The most popular vegetation index is the Normalized Difference Vegetation Index (NDVI), which