A novel method was developed to classify hyperspectral remote sensing image based on independent component analysis (ICA) and support vector machine (SVM) algorithms. The characteristic information of the hyperspectral remote sensing image captured by PHI (made in China, with 80 bands) was extracted by ICA algorithm, and SVM classifier was established with the extracted image data (20 spectral dimensions). After kernel function selecting and parameter optimizing, it was found that the SVM algorithm(RBF kernel function; parameter C=103, γ=0.05)with accuracy 94.512 7% and kappa coefficient 0.935 1 has the best classification result, better than the results of four kinds of conventional algorithms, including neural net classification (accuracy 39.475 8% and kappa coefficient 0.315 5), spectral angle mapper classification (accuracy 80.282 6% and kappa coefficient 0.770 9), minimum distance classification (accuracy 85.462 7% and kappa coefficient 0.827 7) and maximum likelihood classification (accuracy 86.015 6% and Kappa coefficient 0.835 1). In order to control the “pepper and salt” phenomenon which appeared in classification map frequently, the classification result of SVM (RBF kernel) was operated by the method of clump classes using the morphological operators, and that the classification map closer to actual situation was acquired, with the accuracy and kappa coefficient increasing to 94.758 4% and 0.938 0, respectively. The study indicated that the ICA combined with SVM was an preferred method for hyperspectral remote sensing image classification, and clump classes was a effective method to optimized the classification result.