Noise has to be taken into account in the algorithms of classification, target detection and anomaly detection. Recent studies indicate that noise estimation is also crucial in subspace identification of Hyper Spectral Images (his). Several techniques were proposed for noise estimation including: multiple linear regression based techniques, spectral unmixing and remixing etc. The noise in HSI is widely accepted to be a spatially stationary random process. But the variance of the noise varies from one wavelength to another. Two types of noise are considered: the first one is the circuitry noise (thermal noise) which is signal independent. The second one is the photonic noise (shot noise) which is signal dependent. The latter is considered to be the dominant one. A reliable way to accurately estimate the noise requires the identification of a large uniform region in the image. To this end, we propose a region growing technique. At the end of this process, a certain number of regions with different sizes and uniformities are obtained. The next step consists of identifying the most uniform region having the largest area. Once the most uniform and largest region of the scene is identified the next step is to apply an ideal low pass filter to this region. This yields an estimate of the noise-free data, hence the noise itself by calculating the difference. It is also possible to apply the well-known scatter plot technique. Experiments suggest that the proposed scheme produces comparable results to its competitors. A major advantage of the technique is the automated identification of an homogenous region.