As an important deterministic error of the inertial measurement unit (IMU), the installation error has a serious impact on the navigation accuracy of the strapdown inertial navigation system (SINS). The impact becomes more severe in a highly dynamic application environment. This paper proposes a new IMU calibration model based on polar decomposition. Using the new model, the installation error is decomposed into a nonorthogonal error and a misalignment error. The compensation of the IMU calibration model is decomposed into two steps. First, the nonorthogonal error is compensated, and then the misalignment error is compensated. Based on the proposed IMU calibration model, we used a three-axis turntable to calibrate three sets of strapdown inertial navigation systems (SINS). The experimental results show that the misalignment errors are larger than the nonorthogonal errors. Based on the experimental results, this paper proposes a new method to simplify the installation error. This simplified method defines the installation error matrix as an antisymmetric matrix composed of three misalignment errors. The navigation errors caused by the proposed simplified calibration model are compared with the navigation errors caused by the traditional simplified calibration model. The 48-h navigation experiment results show that the proposed simplified calibration model is superior to the traditional simplified calibration model in attitude accuracy, velocity accuracy, and position accuracy.

Information regarding newly added construction land can be extracted from high-resolution remote sensing images. The retrieval accuracy of land cover changes across the country has improved, and the illegal use of land is actively monitored. To address the imbalance between positive and negative training samples in extracting information regarding newly added construction land, a method for identifying newly added construction land by weakening the weight of negative samples was proposed. A focal loss function was used to weaken the negative samples’ weights and improve the overfitting U-net. Since the two parameters of the focal loss function are not independent of each other, they need to be selected at the same time. Therefore, this paper developed a formula for selecting the balance factor α based on a large number of experimental results. First, the GF-2 image was combined with the historical land change survey data and monitoring vector results to construct a dataset, and then the training dataset was input into a fully convolutional neural network (CNN) integrated with feature fusion and a focal loss function. Finally, the accuracy of the trained network model was verified. To demonstrate the applicability of the method of determining the parameters of the focal loss function, the validation set was divided into four subsets for accuracy verification. The experimental results showed that the F1-score of newly added construction land information extracted by this method reached 0.913, which is 0.078 and 0.033 higher than those of the U-net and the improved U-net. The parameters obtained by the method proposed in this study achieved the best results on the four verification sets, which shows that the method for extracting newly added construction land information and that for selecting parameters have strong applicability.