Abstract: 【Objective】Visualization of remotely sensed data is a key step to determine the interpretation quality of remotely sensed data. Visual interpretation is an important way of applying remotely sensed technology in current production practice. To exploring a method for generating visual features which based on learning samples, so that visual and visual interpreting applications can be organically combined to achieve supervised visualization. 【Method】Using the newly proposed regression reconstruction of principal component transform coefficients(RRPCTC)to constructed the model,three new features are obtained,which are respectively assigned red,green and blue three colors in order to generate false color images for visualization.【Result】The image produced by RRPCTC is improved to a certain extent compares with the ordinary principal component transformation(OPCT)method and the original near infrared-short wave infrared-red 3-band combination. It is helpful for visual interpretation. The classification accuracy of RRPCTC is improved by 6.20% compared with that of the OPCT and by 7.82% compared with that of the original 3-band combination. The changes of sums of squares of deviations of the first 3 PCs respectively and the deviation matrix before and later reconstruction show that the reconstruction makes the differences smaller inner classes and larger between classes.【Conclusion】All these quantitatively prove that the separability of the RRPCTC is better than that of OPCT and that of original 3-band combination.