Thermal images often hide mixed signals, making accurate analysis challenging. However, segmentation and analysis are significantly compromised with the task of mixed pixels (a pixel containing the signals from several endmember sources). This study proposes a hybrid approach combining gradient-based thresholding (80 percentile and 85 percentile) and different clustering techniques (K-Means, Variational Bayesian GMM, Dirichlet Process GMM and Constrained GMM) to boost precision in mixed pixel identification. Results show that the gradient threshold has a positive effect on detection error (20.77 percentile), closely matching the values of K-Means (20.82 percentile) and Constrained GMM (20.69 percentile). The deviation from those methods to VBGMM and DP GMM is more moderate by 13.60 percentile. This study confirms the usefulness of an integrated approach for a more accurate interpretation of thermal images. Deep learning and multi-spectral will be researched to boost segmentation accuracy in the future.