**4. Conclusion**

In the present study, the spatial structure of local image texture is computed using HE. The contrast around the pixel is measured using VAR. Afterward, the image is transformed using HE and VAR together for measuring the texture. A threshold δ is used to extract textured and non-textured region from the image. The classification algorithm ISODATA is used to classify the textured region taking into account HE, VAR and intensity values of the textured area's individual pixels. Whereas ISODATA clustering algorithm classifies the extracted non-textured region of the image. The HE and VAR value of individual pixels is not regarded for classification in the event of non-textured region. From the research outcomes, it is discovered that the suggested technique is helpful to extract earth surface characteristics from complicated remote sensing images that contain both textured and non-textured areas. Moreover, it can be considered as an intuitively appealing and unsupervised clustering algorithm for extracting features from remotely sensed images. As a result, the method is potentially useful to extract earth surface features by clustering high spatial resolution panchromatic images more efficiently.
