Abstract. The classification accuracy of a remote sensing image should be assessed before the classification result is used for scientific investigation and policy decision. We proposed an accuracy assessment model based on spatial sampling to reflect region sensitivity of a remote sensing image. The proposed model aims to solve the following problems: (1) what sampling size should be selected for accuracy assessment; (2) where sample points should be distributed in a region; and (3) how to analyze the result of accuracy assessment. This assessment model was proposed based on gray-level co-occurrence matrix (GLCM) and considered both sampling size calculation and sample points distribution during the assessment. The overall accuracy and kappa coefficient derived from this model were very close to the true value derived from the total assessment, suggesting that the assessment accuracy of the model is close to that of total assessment. Compared with the percent sampling model, the model could quantify the relationship between GLCM-correlation parameter and sample size, thereby allowing producer and user to determine sample size according to spatial uniformity and heterogeneity. Compared with the random sampling model, the model could ensure that the sample points are uniformly distributed in the spatial region and proportionally distributed in different types of land cover. Taken together, the proposed model is suitable for the accuracy assessment of the classification result of a remote sensing image.

Portable articulated coordinate measuring machine (PACMM) is a kind of high accuracy coordinate measurement instrument and it has been widely applied in manufacturing and assembly. A number of key problems should be taken into consideration to achieve the required accuracy, such as structural design, mathematical measurement model and calibration method. Although the classical kinematic model of PACMM is the Denavit-Hartenberg (D-H) model, the representation of D-H encounters the badly-conditioned problem when the consecutive joint axes are parallel or nearly parallel. In this paper, a new kinematic model of PACMM based on a generalized geometric error model which eliminates the inadequacies of D-H model has been proposed. Furthermore, the generalized geometric error parameters of PACMM are optimized by the Levenberg-Marquard (L-M) algorithm. The experimental result demonstrates that the measurement of standard deviation of PACMM based on the generalized geometric error model has been reduced from 0.0627 mm to 0.0452 mm with respect to the D-H model.

The typical nonorthogonal coordinate measuring machine is the portable coordinate measuring machine (PCMM), which is widely applied in manufacturing. In order to improve the measurement accuracy of PCMM, structural designing, data processing, mathematical modeling, and identification of parameters of PCMM, which are essential for the measurement accuracy, should be taken into account during the machine development. In this paper, a kind of PCMM used for detecting the crucial dimension of automobile chassis has been studied and calibrated. The Denavit–Hartenberg (D–H) kinematic modeling method has often been used for modeling traditional robot, but the D–H error representation is ill-conditioned when it is applied to represent parallel joints. A modified four-parameter model combined with D–H model is put forward for this PCMM. Based on the kinematic model, Gauss–Newton method is applied for calibrating the kinematic parameters. The experimental results indicate the improvement of measuring accuracy and the effectiveness of the PCMM based on the proposed method.

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