The radiometric calibration of remote sensors is a basis and prerequisite of information quantification in remote sensing. This paper proposes a method for outdoor relative radiometric calibration using gray scale tar...The radiometric calibration of remote sensors is a basis and prerequisite of information quantification in remote sensing. This paper proposes a method for outdoor relative radiometric calibration using gray scale targets. In this method, the idea of two substitutions is adopted. Sunlight is used to replace the integrating sphere light source, and gray scale targets are used to re-place the diffuser. In this way, images at different radiance levels obtained outdoors can calculate the relative radiometric cali-bration coefficients using the least square method. The characteristics of this method are as follows. Firstly, compared with la-boratory calibration, it greatly reduces the complexity of the calibration method and the test cost. Secondly, compared with the existing outdoor relative radiometric calibration of a single radiance level, it uses test images of different radiance levels to re-duce errors. Thirdly, it is easy to operate with fewer environmental requirements, has obvious advantages in the rapid calibra-tion of airborne remote sensors before or after flight and is practical in engineering. This paper theoretically and experimental-ly proves the feasibility of this method. Calibration experiments were conducted on the wide-view multispectral imager (WVMI) using this method, and the precision of this method was evaluated by analyzing the corrected images of large uniform targets on ground. The experiment results have demonstrated that the new method is effective and its precision meets the re-quirement of the absolute radiometric calibration.展开更多
On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits o...On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine（SVM） with radial based function（RBF） kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants（AMI） and multi-scale auto-convolution（MSA） in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.11174017)the National High Technology Research and Development Program of China("863" Program)(Grant No.2008AA121806)
文摘The radiometric calibration of remote sensors is a basis and prerequisite of information quantification in remote sensing. This paper proposes a method for outdoor relative radiometric calibration using gray scale targets. In this method, the idea of two substitutions is adopted. Sunlight is used to replace the integrating sphere light source, and gray scale targets are used to re-place the diffuser. In this way, images at different radiance levels obtained outdoors can calculate the relative radiometric cali-bration coefficients using the least square method. The characteristics of this method are as follows. Firstly, compared with la-boratory calibration, it greatly reduces the complexity of the calibration method and the test cost. Secondly, compared with the existing outdoor relative radiometric calibration of a single radiance level, it uses test images of different radiance levels to re-duce errors. Thirdly, it is easy to operate with fewer environmental requirements, has obvious advantages in the rapid calibra-tion of airborne remote sensors before or after flight and is practical in engineering. This paper theoretically and experimental-ly proves the feasibility of this method. Calibration experiments were conducted on the wide-view multispectral imager (WVMI) using this method, and the precision of this method was evaluated by analyzing the corrected images of large uniform targets on ground. The experiment results have demonstrated that the new method is effective and its precision meets the re-quirement of the absolute radiometric calibration.
基金supported by the National High Technology Research and Development Program (863 Program) (2010AA7080302)
文摘On the basis of scale invariant feature transform（SIFT） descriptors,a novel kind of local invariants based on SIFT sequence scale（SIFT-SS） is proposed and applied to target classification.First of all,the merits of using an SIFT algorithm for target classification are discussed.Secondly,the scales of SIFT descriptors are sorted by descending as SIFT-SS,which is sent to a support vector machine（SVM） with radial based function（RBF） kernel in order to train SVM classifier,which will be used for achieving target classification.Experimental results indicate that the SIFT-SS algorithm is efficient for target classification and can obtain a higher recognition rate than affine moment invariants（AMI） and multi-scale auto-convolution（MSA） in some complex situations,such as the situation with the existence of noises and occlusions.Moreover,the computational time of SIFT-SS is shorter than MSA and longer than AMI.
