Textural analysis of IRS-1D panchromatic data for land cover classification

Manifestation of texture in high spatial resolution optical satellite data has an inherent potential to provide land cover information. A study has been conducted to explore this utility with IRS-1D panchromatic (PAN) data through its grey values and the derived textural information. A typical test site with a wide range of textures was subjected to textural analysis by the Grey Level Co-occurrence Matrix (GLCM) approach. Entropy at an Inter Pixel Distance (IPD) of 1 and Correlation at an IPD of 9 were found to be the optimised textural features. The data has been independently classified using PAN grey levels, textural information, and PAN combined texture, which showed significantly improved classification accuracy with the addition of textural information. Similar results were obtained over two other independent sites, validating the consistent performance of the optimised textural features.