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Conventional Noise Clustering (NC) algorithms do not consider any spatial information in the image. In this study, three algorithms have been presented, Noise Local Information <i>c</i>-means (NLICM) and Adaptive Noise Local Information <i>c</i>-Means (ADNLICM), which use NC as the base classifier, and Noise Clustering with constraints (NC_S), which incorporates spatial information into the objective function of the NC classifier. These algorithms enhance the performance of classification by minimizing the effect of noise and outliers. The algorithms were tested on two study areas, Haridwar (Uttarakhand) and Banasthali (Rajasthan) in India. All three algorithms were examined using different parameters (distance measures, fuzziness factor, and δ). An analysis determined that the ADNLICM algorithm with Bray–Curtis distance measures, fuzziness factor m = 1.1, and δ = 10⁶, outperformed the other algorithm and achieved 91.53% overall accuracy. The optimized algorithm returned the lowest variance and RMSE for both study areas, demonstrating that the optimized algorithm works for different satellite images. The optimized technique can be used to categorize images with noisy pixels and heterogeneity for various applications, such as mapping, change detection, area estimation, feature recognition, and classification.