In satellite laser altimetry, it is a challenging task to accurately extract peak positions from full waveforms due to the overlapped or weak peaks within the large laser footprints, which substantially affects the subsequent applications. In this paper, to improve the laser ranging resolution and accuracy, we propose a novel approach by combining deconvolution with Gaussian decomposition. The approach is applied in two main phases: 1) the deconvolution is first used to remove the system contribution (the transmit pulse spreading over several nanoseconds, system noise); and 2) Gaussian decomposition is then adopted to extract the peak parameters of each object. Experiments using simulated and ICESat waveforms were conducted to validate and evaluate the proposed approach by comparing it to the benchmark Gaussian decomposition technique. The results indicated that: 1) the combined approach can significantly improve the peak detection rate: the four combined methods found at least 15.8% more echoes in simulated forested areas; and 2) for ICESat waveforms, the quantitative evaluation and visual assessment of the Blind -Gaussian combination obtained more echoes (on average, approximately 2.5 components) than the other combinations (on average, approximately 1.5 and 1.2 components), and the derived relative object heights were very close to the results obtained from airborne LiDAR data. These results confirmed that the Blind -Gaussian combination is more accurate for the range retrieval of vegetated and urbanized landscapes.

Laminated rubber bearings are widely used for the mitigation of seismic damage of large-scale structures and equipment. However, owing to the flexibility in horizontal direction, the traditional contacted transducer is difficult to acquire the displacement data accurately in the three directions, respectively. In this paper, three-dimensional displacement measurement of laminated rubber bearing based on the large-scale shaking table is achieved by the use of a tri-camera high-speed videogrammetric system consisting of three complementary-metal-oxide-semiconductor (CMOS) cameras, one synchronous controller, and one pair of 1000 watt light sources, which are used to simultaneously acquire the tri-camera image sequences of laminated rubber bearing at a speed of 300 frames per second (fps). Firstly, this paper proposes a fast image block technique for detecting and tracking targets in tri-camera image sequences by integration of techniques morphological edge detection, attribute based ellipse extraction and least-squares-based fitting adjustment. Secondly, this paper presents an integrated bundle adjustment approach, which brings continuous tracking points into one collinearity condition equation, to reconstruct the three dimensional coordinates of continuous tracking points, for the purpose of improving the accuracy of three-dimensional coordinates of tracking points based on tri-camera image sequences. At last, an empirical experiment was conducted to measure the three-dimensional displacement of laminated rubber bearings on the shaking table by the use of the proposed method. The experimental results showed that the proposed method could obtain three-dimensional displacement of laminated rubber bearings with an accuracy of more than 0.5 mm.

Accurate land cover mapping and change analysis is essential for natural resource management and ecosystem monitoring. GlobeLand30 is a global land cover product from China with 30 m resolution that provides reliable data for many international scientific programs. Few studies have focused on systematically implementing this global land cover product in regional studies. Therefore, this paper presents an object-based extended change vector analysis (ECVA_OB) and transfer learning method to update the reginal land cover map using GlobeLand30 product. The method is designed to highlight small and subtle changes through the concept of uncertain area analysis. Updating is carried out by classifying changed objects using a change-detection-based transfer learning method. Land cover changes are analyzed and the factors affecting updating results are explored. The method was tested with data from Shanghai, China, a city that has experienced significant changes in the past decade. The experimental results show that: (1) the change detection and classification accuracy of the proposed method are 83.30% and 78.77%, respectively, which are significantly better than the values obtained for the multithreshold change vector analysis (MCVA) and the multithreshold change vector analysis and support vector machine (MCVA + SVM) methods; (2) the updated results agree well with GlobeLand30 2010, especially for cultivated land and artificial surfaces, indicating the effectiveness of the proposed method; (3) the most significant changes over the past decade in Shanghai were from cultivated land to artificial surfaces, and the total area containing artificial surfaces in Shanghai increased by about 55% from 2000 to 2011. The factors affecting the updating results are also discussed, which be attributed to the classification accuracy of the base image, extended change vector analysis, and object-based image analysis.

Global and local land-cover mapping products provide important data on land surface. However, the accuracy of land-cover products is the key issue for their further scientific application. There has been neglect of the relationship between inclusion probability and spatial heterogeneity in traditional spatially balanced sampling. The aim of this paper was to propose an improved spatially balanced sampling method using landscape pattern-based inclusion probability. Compared with other global land-cover datasets, Globeland30 has the advantages of high resolution and high classification accuracy. A two-stage stratified spatially balanced sampling scheme was designed and applied to the regional validation of GlobeLand30 in China. In this paper, the whole area was divided into three parts: the Tibetan Plateau region, the Northwest China region, and the East China region. The results show that 7242 sample points were selected, and the overall accuracy of GlobeLand30-2010 in China was found to be 80.46%, which is close to the third-party assessment accuracy of GlobeLand30. This method improves the representativeness of samples, reduces the classification error of remote sensing, and provides better guidance for biodiversity and sustainable development of environment.

Attitude jitter causes image motion and degrades geometric accuracy of high-resolution satellite images. This work studies the mechanism of the attitude jitter effect on the imaging geometry of three-line-array push-broom sensors onboard satellites, which is a typical configuration used for topographic mapping. Based on a rigorous physical imaging model, we derived quantitative models of the geometric effect of attitude jitter in the roll, pitch, and yaw angles on the image distortions of the forward, nadir, and backward view sensors, and the accuracy of the derived models is validated through comprehensive experiments and analyses. The experimental results reveal the following. First, the attitude jitter in the roll angle dominates the cross-track image deviation; it does not affect the along-track geometry of the nadir-view sensor but marginally affects the off-nadir-view sensors, and the image distortions share a linear relationship with the image column coordinates. Second, the attitude jitter in the pitch angle dominates the along-track image deviation, and the image distortions in the off-nadir-view images are relatively larger than those in the nadir-view images. The attitude jitter in the pitch angle does not affect the cross-track geometry of the nadir-view sensor but marginally affects the off-nadir-view sensors, and the image distortions share a linear relationship with the image column coordinates. Finally, the attitude jitter in the yaw angle mainly causes the cross-track image deviation in the off-nadir-view sensors, and the along-track image geometries of all the three view sensors are marginally affected to the same extent by the yaw angle variation.