When it comes to the application of the photon data gathered by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), accurately removing noise is crucial. In particular, conventional denoising algorithms based on local density are susceptible to missing some signal photons when there is uneven signal density distribution, as well as being susceptible to misclassifying noise photons near the signal photons; the application of deep learning remains untapped in this domain as well. To solve these problems, a method for extracting signal photons based on a GoogLeNet model fused with a Convolutional Block Attention Module (CBAM) is proposed. The network model can make good use of the distribution information of each photon’s neighborhood, and simultaneously extract signal photons with different photon densities to avoid misclassification of noise photons. The CBAM enhances the network to focus more on learning the crucial features and improves its discriminative ability. In the experiments, simulation photon data in different signal-to-noise ratios (SNR) levels are utilized to demonstrate the superiority and accuracy of the proposed method. The results from signal extraction using the proposed method in four experimental areas outperform the conventional methods, with overall accuracy exceeding 98%. In the real validation experiments, reference data from four experimental areas are collected, and the elevation of signal photons extracted by the proposed method is proven to be consistent with the reference elevation, with R² exceeding 0.87. Both simulation and real validation experiments demonstrate that the proposed method is effective and accurate for extracting signal photons.

Urban change detection (CD) using remote sensing images is of great significance for monitoring and analyzing the spatial and temporal distribution of changes within cities, which can be used to guide urban management. In order to reduce false detections and improve the CD automation, a novel CD framework for high resolution images is proposed in this paper. Firstly, a novel multi-level matching feature is presented, combining structural invariant features with multi-scale dense matching features to comprehensively describe the invariant properties of ground objects at different levels. Secondly, a newly automatic training sample extraction strategy is proposed, in which sufficient and accurate no-change samples can be obtained by Gaussian-weighted Dempster-Shafer evidence theory and L1-norm, meanwhile, typical change samples can be extracted by sequential spectral change vector analysis. Utilizing the automatic extracted samples, the final CD results are obtained using four supervised classifiers respectively (k-nearest neighbor, support vector machine, rotation forest, and extra-trees). To validate the proposed CD framework, experiments are conducted in four datasets with spectral variability, spectral confusion between the changed objects and unchanged backgrounds, and mis-registration. The results demonstrate that the proposed multi-level matching feature and automatic sample extraction strategy can obtain better results with different types of supervised classifiers, and can effectively improve automation of CD, which is applicable to the large spatial extent scene.

Monitoring the shipyard production state is of great significance to shipbuilding industry development and coastal resource utilization. In this article, it is the first time that satellite remote sensing (RS) data is utilized to monitor the shipyard production state dynamically and efficiently, which can make up for the traditional production state data collection mode. According to the imaging characteristics of optical remote sensing images in shipyards with a different production state, the characteristics are analyzed to establish reliable production state evidence. Firstly, in order to obtain the characteristics of the production state of optical remote sensing data, the high-level semantic information in the shipyard is extracted by transfer learning convolutional neural networks (CNNs). Secondly, in the evidence fusion, for the conflict evidence from the core sites of the shipyard, an improved DS evidence fusion method is proposed, which constructs the correlation metric to measure the degree of conflict in evidence and designs the similarity metric to measure the credibility of evidence. Thirdly, the weight of all the evidence is calculated according to the similarity metric to correct the conflict evidence. The introduction of the iterative idea is motivated by the fact that the fusion result aligns more closely with the desired result, the iterative idea is introduced to correct the fusion result. This method can effectively solve the conflict of evidence and effectively improve the monitoring accuracy of the shipyard production state. In the experiments, the Yangtze River Delta and the Bohai Rim are selected to verify that the proposed method can accurately recognize the shipyard production state, which reveals the potential of satellite RS images in shipyard production state monitoring, and also provides a new research thought perspective for other industrial production state monitoring.

Antarctic mapping with satellite images is an important basic task for polar environmental monitoring. Since the first Chinese marine satellite was launched in 2002, China has formed three series of more than 10 marine satellites in orbit. As global operational monitoring satellites of ocean color series, HY-1C and HY-1D have good coverage characteristics and imaging performance in polar regions, and they provide an effective tool for Antarctic monitoring and mapping. In this paper, Antarctic images acquired by the HY-1 satellite Coastal Zone Imager (CZI) sensor were used to study color matching in the mosaic process. According to the CZI characteristics for Antarctic imaging, experiments were carried out on the illuminance nonuniformity of a single image and color registration of multiple images. A gray-level segmentation color-matching method is proposed to solve the problem of image overstretching in the Antarctic image color-matching process. The results and statistical analysis show that the proposed method can effectively eliminate the color deviation between HY-1 Antarctic images, and the mosaic results have a good effect.

Wind shear coefficients (WSCs) are fundamental parameters for wind resource assessment, wind turbine design, and offshore wind farm operation. This work focuses on positive wind shear coefficients (WSCs); hereafter, WSC refers exclusively to positive-value wind shear coefficients derived from positive shear profiles. However, the characteristics of positive WSCs in the offshore waters of Zhuanghe have not yet been systematically investigated. In this study, two years of in situ observational data (2015–2017) were analyzed to characterize the temporal variability of positive WSCs under different atmospheric stability conditions. The results indicate that the atmospheric boundary layer in the study area exhibits pronounced seasonal and diurnal variations in stability, with stable and unstable conditions showing a distinct annual cycle. Increasing wind speed promotes neutral conditions as mechanically generated turbulence becomes dominant over buoyancy effects. The WSC ranges from 0.01 to 0.32 and exhibits pronounced seasonal and monthly variability. Under neutral and unstable atmospheric conditions, the diurnal variation in WSC is relatively consistent among different measurement heights, whereas stable conditions produce appreciable vertical differences in WSC throughout most of the year. These findings improve our understanding of offshore wind shear characteristics across various atmospheric stability regimes. Furthermore, they offer valuable guidance for offshore wind resource assessment, wind profile modeling, and the overall design and operation of wind farms in the Zhuanghe region.