In this paper, a novel study is proposed for the feature extraction of high volumes of remote sensing images by using a simulated annealing feature extraction (SAFE) approach. For hyperspectral imagery, complete modular eigenspace (CME) has been developed by clustering highly correlated hyperspectral bands into a smaller subset of band modular based on greedy algorithm. Instead of greedy paradigm as adopted in CME approach, this paper introduces a simulated annealing (SA) approach for hyperspectral imagery. It presents a framework which consists of three algorithms, referred to as SAFE, CME and the feature scale uniformity transformation (FSUT). SAFE selects the sets of non-correlated hyperspectral bands based on SA algorithm while utilizing the inherent separability of different classes in hyperspectral images to reduce dimensionality and further to effectively generate a unique CME feature. The proposed SA features avoids the bias problems of transforming the information into linear combinations of bands as does the traditional principal components analysis and provides a fast procedure to simultaneously select the most significant features according to a scheme of SA. The experimental results show that the SAFE approach is effective and can be used as an alternative to the existing feature extraction algorithms.