There are many problems in existing reconstruction-based super-resolution algorithms, such as the lack of texture-feature representation and of high-frequency details. Multi-scale detail enhancement can produce more texture information and high-frequency information. Therefore, super-resolution reconstruction of remote-sensing images based on adaptive multi-scale detail enhancement (AMDE-SR) is proposed in this paper. First, the information entropy of each remote-sensing image is calculated, and the image with the maximum entropy value is regarded as the reference image. Subsequently, spatio-temporal remote-sensing images are processed using phase normalization, which is to reduce the time phase difference of image data and enhance the complementarity of information. The multi-scale image information is then decomposed using the L₀ gradient minimization model, and the non-redundant information is processed by difference calculation and expanding non-redundant layers and the redundant layer by the iterative back-projection (IBP) technique. The different-scale non-redundant information is adaptive-weighted and fused using cross-entropy. Finally, a nonlinear texture-detail-enhancement function is built to improve the scope of small details, and the peak signal-to-noise ratio (PSNR) is used as an iterative constraint. Ultimately, high-resolution remote-sensing images with abundant texture information are obtained by iterative optimization. Real results show an average gain in entropy of up to 0.42 dB for an up-scaling of 2 and a significant promotion gain in enhancement measure evaluation for an up-scaling of 2. The experimental results show that the performance of the AMED-SR method is better than existing super-resolution reconstruction methods in terms of visual and accuracy improvements.

Interferometric Synthetic Aperture Radar (InSAR) is one of the most sufficient technologies to provide global digital elevation modeling (DEM). It unwraps the interferometric phase to provide observations for phase-to-height conversion. However, the phase gradient has great influence on the phase unwrapping quality, thereby affecting the topographic mapping accuracy. Multi-look processing can improve the reliability of phase unwrapping by reducing the noise phase gradient. Nevertheless, it reduces the spatial resolution while increasing the height phase gradient, thus lowering the reliability of height values. In this paper, we propose a multi-look iteration algorithm to suppress the noise and maintain the reliability of height values. First, we use a large number of looks (NL) to suppress the noise phase gradient and obtain a coarse DEM. Then taking this coarse DEM as a reference, we remove the topographic phase from the interferogram with a small NL, which will reduce height phase gradient and ensure the accuracy of phase unwrapping. Finally, we obtain DEM products with high precision and fine resolution. We validate the proposed algorithm using both simulated and real data, and obtain DEM products in a greatly undulated region using TanDEM-X data. Results show that the proposed method is capable of providing DEM with resolution of 4 m and accuracy of 1.73 m.

The pointing bias of the laser altimeter would change because of the launch vibration, variations in the space environment or other factors, which is one of the most important factors affecting the geometric accuracy of a laser altimeter. To calibrate pointing bias and improve the measuring accuracy of spaceborne laser altimeter, this paper proposed an in-orbit calibration method based on terrain matching with pyramid-search for this spaceborne laser altimeter. First, we used published digital terrain data as a reference to match with stripes of a point cloud obtained by the geolocation model of the laser altimeter, and then the optimal matching terrain could be determined by a pyramid search. Finally, the pointing bias of the laser altimeter was calibrated. The proposed method was applied in calibration experiment of ZY3-02 laser altimeter. Several tracks of ZY3-02 laser data and advanced Land Observation Satellite Digital Surface Model (AW3D30) with a 30-m grid size were deployed for calibration and validation. After calibration, the systematic error was effectively eliminated. The elevation accuracy of the laser altimeter was improved from more than 100 to 3 m approximately, and the algorithm efficiency with pyramid search was improved by 10 times at least. The experimental result demonstrates that the proposed method is an effective means to calibrate the current spaceborne laser altimeters.

The three-dimensional (3D) geographic locations of street furniture such as traffic signs comprise the basic content of 3D city construction, and such information is indispensable for periodic statistics for road management and maintenance. This paper presents a novel solution for acquiring 3D information on small traffic signs based on Mobile Mapping System (MMS) data. Firstly, a lightweight backbone network called VGG-L under an optimized Faster Region-based Convolutional Neural Network (R-CNN) detection framework is proposed for the detection of small traffic signs. An Urban Traffic Sign Detection (UTSD) dataset is created based on panoramic images obtained from test areas. Detection results from the UTSD dataset show that VGG-L outperforms other popular networks and achieves a mean average precision of 75.4%, which is 4.2—14.8% higher than those of VGG16, MobileNet, ResNet, and YOLOv3. Secondly, a novel depth-map-based 3D spatial geo-location method is proposed for obtaining the 3D geographic locations of the objects. Then, a center-based method is proposed to automatically extract the final 3D vector of the target. Experimental results illustrate that the proposed method performs 3D positioning and vectorization of the milestones, circular and triangular traffic signs, accurately and effectively, achieving greater than 86% recall and precision for the three types of targets in the test areas. The experiments demonstrate that the overall 3D information acquisition scheme is feasible and has great application potential.

Successfully launched on 30 May 2016, ZY3-02 is the first Chinese surveying and mapping satellite equipped with a lightweight laser altimeter. Calibration is necessary before the laser altimeter becomes operational. Laser footprint location prediction is the first step in calibration that is based on ground infrared detectors, and it is difficult because the sample frequency of the ZY3-02 laser altimeter is 2 Hz, and the distance between two adjacent laser footprints is about 3.5 km. In this paper, we build an on-orbit rigorous geometric prediction model referenced to the rigorous geometric model of optical remote sensing satellites. The model includes three kinds of data that must be predicted: pointing angle, orbit parameters, and attitude angles. The proposed method is verified by a ZY3-02 laser altimeter on-orbit geometric calibration test. Five laser footprint prediction experiments are conducted based on the model, and the laser footprint prediction accuracy is better than 150 m on the ground. The effectiveness and accuracy of the on-orbit rigorous geometric prediction model are confirmed by the test results. The geolocation is predicted precisely by the proposed method, and this will give a reference to the geolocation prediction of future land laser detectors in other laser altimeter calibration test.