In order to improve the classification accuracy and reduce computation complexity, a hyperspectral remote sensing data classification method based on sparse nonnegative leastsquares coding was proposed. By adopting nonnegative leastsquares, the test samples were expressed as a linear combination of training samples, and the obtained coefficients were used as its feature vector. As a result of the nonnegative constraint, the feature vectors were sparse, which can not only improve the efficiency of the proposed algorithm, but also enhance the discrimination performance of algorithm. At last, the minimizing residual was used to classify the test samples. The experimental verifications of the proposed method were carried out on AVIRIS Indian Pines and Salinas Valley hyperspectral remote sensing data, the classification accuracies of the proposed method were 85.31% and 99.56%, and the Kappa coefficients were 0.8163 and 0.9867, respectively. The proposed method was compared with PCA, SVM and SRC in terms of classification accuracy and Kappa coefficients on two databases, experiment results showed that the proposed method was superior to PCA, SVM and SRC. The proposed approach was valuable for hyperspectral data classification with low computational cost and high classification accuracy, it was a better method of hyperspectral remote sensing data classification.