Deep learning-based approaches for land cover segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. Due to the tedious and labor-intensive labeling process, transferring the models trained with label-rich source data to nonannotated target data becomes a popular problem in recent years. Because of the domain shift, the difference between the source and target distributions can degrade the accuracy on target data if the training occurs directly in a source domain without proper domain adaptation (DA). In this letter, we propose a U-Net based network for DA in the context of semantic segmentation. The model is trained in the source domain with ground truth and test in the target domain without any annotations. We introduce the layer alignment method and the feature covariance loss function to alleviate the domain shift between different domains. To further enhance the adapted model, we adopt a self-training method to improve segmentation performance. Experimental results on the images from the 2018 IEEE Geoscience and Remote Sensing Society (GRSS) data fusion contest and the International Society for Photogrammetry and Remote Sensing (ISPRS) 2-D semantic labeling contest data set reveal the effectiveness of the proposed model. By reducing the domain distribution difference, our method shows better performance compared with the mainstream unsupervised DA methods.

High-resolution satellite images (HRSIs) obtained from onboard satellite linear array cameras suffer from geometric disturbance in the presence of attitude jitter. Therefore, detection and compensation of satellite attitude jitter are crucial to reduce the geopositioning error and to improve the geometric accuracy of HRSIs. In this work, a generative adversarial network (GAN) architecture is proposed to automatically learn and correct the deformed scene features from a single remote sensing image. In the proposed GAN, a convolutional neural network (CNN) is designed to discriminate the inputs, and another CNN is used to generate so-called fake inputs. To explore the usefulness and effectiveness of a GAN for jitter detection, the proposed GANs are trained on part of the PatternNet dataset and tested on three popular remote sensing datasets, along with a deformed Yaogan-26 satellite image. Several experiments show that the proposed model provides competitive results. The proposed GAN reveals the enormous potential of GAN-based methods for the analysis of attitude jitter from remote sensing images.

In order to enhance the accuracy and the robustness of the attitude determination and control system in observation satellites, a new way to fuse gyro and star tracker measurement with image registration is described. In this method, a novel and complete framework is proposed to estimate the on-orbit attitude variations from multi-spectrum remote sensing images. An extended Kalman filter is derived to calibrate the gyro bias drift and the star tracker error. The new framework is tested with realistically simulated data and remote sensing images based on JL-1 satellite. Simulation and experiment results indicate that based on the image registration, the satellite attitude variations could be detected in real time and applied for the accurate gyro and star tracker bias calibration.

An effective on-board cloud detection method in small satellites would greatly improve the downlink data transmission efficiency and reduce the memory cost. In this paper, an ensemble method combining a lightweight U-Net with wavelet image compression is proposed and evaluated. The red, green, blue and infrared waveband images from Landsat-8 dataset are trained and tested to estimate the performance of proposed method. The LeGall-5/3 wavelet transform is applied on the dataset to accelerate the neural network and improve the feasibility of on-board implement. The experiment results illustrate that the overall accuracy of the proposed model achieves 97.45% by utilizing only four bands. Tests on low coefficients of compressed dataset have shown that the overall accuracy of the proposed method is still higher than 95%, while its inference speed is accelerated to 0.055 second per million pixels and maximum memory cost reduces to 2Mb. By taking advantage of mature image compression system in small satellites, the proposed method provides a good possibility of on-board cloud detection based on deep learning.

Both Global Navigation Satellite Systems and X-ray pulsar-based navigation system use relative position information to provide accurate spacecraft navigation. To supplement that navigation information, an absolute reference position from other types of measurements should be provided; however, that may increase the complexity or decrease the autonomy of the navigation system. To overcome those drawbacks, this paper proposes an initial orbit determination method using only relative position increment measurements and their corresponding epoch. The proposed method can be summarised as three steps: first, determining the orbital plane from a set of relative position vectors; next, determining the shape of the orbit by solving an ellipse fitting problem in two dimensions; finally, estimating the absolute position and all six orbital elements according to Kepler's equation and geometric relations. Monte Carlo simulations were done to analyse the feasibility and features of the proposed method. The results show that the algorithm we used does not need an initial guess and is robust. It can obtain an acceptable result from a 20% portion of an orbit when the signal-to-noise ratio is set at 15 dB with determining the inclination and right ascension of the ascending node quickly and accurately.

针对脉冲星导航技术中延时估计这一关键问题, 提出了频域上直接使用脉冲星信号测量到达时间集合进行时延估计的方法——多谐波脉冲星信号时延估计(MHSPE)方法. 该方法建立在频域上相位时延的极大似然估计的基础上, 通过高次谐波对脉冲星观测信号提取出各谐波相位的极大似然估计, 然后取频谱上各谐波的幅值进行归一化作为各谐波相位的权值, 最后取各谐波相位的加权平均作为该时刻的相位估计. 理论上证得MHSPE算法对相位的估计是无偏、一致的, 相比于频域上一次谐波的极大似然估计, MHSPE方法的信噪比随谐波数m的增加而增加, 当各谐波幅值相同时, 信噪比可提高m1/2倍; 与脉冲星信号时延的克拉美罗界比较, 脉冲星信号时域的导数在频域上的反映就是各谐波分量的数量, 因此随着谐波次数的增加脉冲星信号时延估计可极大趋近克拉美罗界. 采用RXTE航天器对Crab脉冲星的实测数据检验MHSPE方法的性能, 实验结果表明, 针对低信噪比的脉冲星信号, MHSPE可获得高精度的相位估计, 随观测时间增加, 估计精度快速收敛于克拉美罗界.