The Contribution of the Texturing in the Processing of Optical Data

This study delves into the application of three distinct classification methodologies for extracting information from satellite imagery. The first utilizes traditional techniques with three channels: TM1, TM3, and TM4. The second combines textural indices from occurrence matrices with additive channels and raw radiometric channels. The third integrates these channels with Gabor filter imagery. The aim is to decipher the satellite images’ intrinsic data and comparatively analyze the methodologies’ effectiveness. Our results demonstrate that textural classification, especially with the Gabor filters, amplifies the discriminative capability, achieving a 5% enhancement in the classification rate. This is particularly impactful in differentiating themes like Urban and Sebkha1, emphasizing the potential of textural features in refining satellite image classification processes.