A hybrid scheme for the image segmentation of high-resolution images is proposed in this study. Our methodology is based on combining both supervised and unsupervised segmentation. The entire process is performed in the frequency domain, rather than the spatial domain, using the Shift Invariant Shearlet Transform (SIST). Initially, the input image is filtered using an anisotropic filter to enhance the texture features. Then, it is separated into low and high sub-band frequencies using SIST. Subsequently, we built a feature vector from coarser coefficients complemented with texture information extracted from high-frequency coefficients of the input image. SOM is used for the preliminary classification of the input image coefficients, and the network training process is performed using the previously built feature vector. Lastly, the modified PCNN is used to augment the SOM results to reduce the over-segmentation artefacts. We used the Berkeley Segmentation Database (BSR) and Quick-Bird Satellite images to validate the results. It was found that the proposed scheme is superior to the Fuzzy-C-Means-based, SOM-based, and PCNN-based segmentation algorithms in terms of quantitative criteria and visual interpretation. 
Image segmentation scheme based on SOM–PCNN in frequency domain