Point cloud classification of airborne light detection and ranging (LiDAR) data is essential to extract geoinformation. Although deep learning provides a new approach for classification, the time-consuming training process and data dependence prevent its widespread application to point clouds. To solve these problems and leverage the potential of high-performing neural networks, we propose an airborne LiDAR point cloud classification method based on transfer learning. A strategy to generate feature images considering the point cloud spatial distribution is first introduced for applying traditional convolutional neural networks to point clouds. Then, transfer learning is used to extract multiscale and multiview deep features. A simple neural network classifier is designed to reduce dimensionality, fuse and learn high-level features, and postprocessing considering contextual information further improves the classification accuracy. We verified the performance of the proposed method through experiments on two airborne LiDAR data sets with different characteristics and containing eight classes. The results demonstrate that the proposed method can achieve a satisfactory classification accuracy with relatively short training time and less training samples than if using conventional methods.

Sphingolipid, ceramide for example, plays an essential role in regulating cancer cell death. Defects in the generation and metabolism of ceramide in cancer cells contribute to tumor cell survival and resistance to chemotherapy. Ceramide Transfer Protein (CERT) determines the ratio of ceramide and sphingomyelin in cells. Targeting CERT sensitizes solid cancer cells to chemotherapy. However, whether targeting CERT to induce ceramide accumulation thereby improving AML therapy efficiency remains elusive. Here, we show that knocking down CERT inhibits the growth and promotes the apoptosis of AML cells carrying FLT3-ITD mutation. Combining CERT inhibitor with FLT3 inhibitor exhibits synergistic effects on FLT3-ITD mutated acute myeloid leukemia (AML) cells. Additionally, co-treatment of HPA-12 and Crenolanib is effective in FLT3-ITD⁺ and FLT3-TKD⁺ AML patients. The synergistic effects are found to be mediated by the endoplasmic reticulum stress-GRP78/ATF6/CHOP axis and mitophagy. Our data provide an effective strategy to enhance the efficacy of FLT3 inhibitors in AML.

Photon-counting light detection and ranging is very sensitive to ambient interference, target features, and instrument performance, especially for long-distance detection of spaceborne laser altimeter and measurement of complex land-cover types with steep terrain. It is crucial to extract the signal photons on the ground surface from the collected photon point cloud (PPC). An adaptive signal photon detection method is presented in this article, which combines histogram statistics and boxplot analysis with density-based spatial clustering of applications with noise (DBSCAN), to denoise the PPC data with strong and weak noise obtained by ice, cloud, and land elevation satellite-2 laser altimeter. First, a coarse denoising with histogram of elevation is conducted on the raw PPC to reduce the calculation amount. Second, a fine denoising based on adaptive DBSCAN is used to extract the signal photons, where the key parameters of elliptic filter kernel are automatically determined according to the topographic data situation. We compared it with other methods, including local distance statistics (LDS), traditional and modified DBSCAN, traditional and modified ordering points to identify cluster structure (OPTICS), and ATL08 data. Some quantitative indicators, such as recall (R), precision (P), and F-score (F), are used to evaluate its performance. The results show that; 1) the adaptive DBSCAN has the best performance on preserving the vertical structural characteristics of ground objects, and 2) the adaptive DBSCAN in the mean R, P, and F of three land covers (i.e., mountain forest, urban, and water areas) can get up to the maximum are 0.9852, 0.9675, and 0.9761, respectively; followed by ATL08 data with 0.9773, 0.9412, and 0.9536, modified OPTICS with 0.9684, 0.9460, and 0.9586, and modified DBSCAN with 0.9613, 0.9474, and 0.9544; and then OPTICS with 0.9444, 0.9397, and 0.9378, and the DBSCAN with 0.9444, 0.9355, and 0.9554; the last one is LDS with 0.9382, 0.9261, and 0.9422. The proposed method provides an alternative approach for rapid and accurate processing of PPC on complex terrain.

A Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM)-aided geopositioning method is proposed to solve the problem of geopositioning without ground control points for Mapping Satellite-1 imagery. The method comprises coarse and accurate correction stages, and it compensates errors gradually. DEM extraction and DEM matching are important steps in both the stages, the objectives of which are to compensate the relative and absolute errors in an image, respectively. The SRTM DEM is integrated into all the processes to take full advantage of its consistent and high accuracy. Experimental results showed that this method could greatly improve geometry accuracy and obtain stable and highly accurate geopositioning for Mapping Satellite-1 images, regardless of the land area proportion (LAP) or the production mode. The planimetric and vertical accuracies were better than 8.1 and 5.2 m, respectively, which could satisfy the accuracy requirements of mapping at 1:50 000 scale. The computational efficiency depends on the LAP and target DEM resolution.

Photogrammetry based on high-resolution satellite image can acquire geospatial information within a large area rapidly and timely, but its geopositioning accuracy is highly dependent on ground control points. Under the background of global mapping, a public digital elevation model (DEM) assisted Chinese satellite image geopositioning scheme was proposed to realize satellite photogrammetry without ground control points. To make full use of public DEM advantages of consistent and high accuracy, public DEM was regarded as reference data and matched with the DEM extracted from image, and then the determined transformation parameters were applied to correct direct georeferencing results in object space. A fast least Z-difference method combined with least trimmed squares estimator was proposed to achieve DEM matching, which can not only automatically select corresponding point determination model, but also self-adaptively detect and eliminate difference between DEMs. Multi-groups of comparative experiments using Mapping Satellite-1 and ZiYuan-3 surveying satellite images were designed. Experimental results show that the DEM assisted geopositioning scheme exploits advantages of reference DEM, which can greatly improve the accuracy of photogrammetry without ground control points to a relatively high level. The geopositioning accuracy of image is largely determined by, but not confined to, the accuracy of reference DEM, but it is slightly affected by the resolution of reference DEM. If target DEM resolution is relatively high, the geopositioning accuracy of a single image assisted by Shuttle Radar Topography Mission (SRTM) DEM can satisfy the accuracy requirements of 1:50,000 scale mapping perfectly. This scheme also has good robustness and high computational efficiency.

The active disturbance rejection control (ADRC) extended to the fractional-order control system, fractional-order proportional–integral (PI) and proportional–integral–derivative (PID) controllers are the most commonly used controller. This paper presents a simple fractional-order control system combined with ADRC control system. By treating the fractional-order as a common disturbance and actively rejecting it, an ADRC can achieve the desired response. External disturbance, sensor noise, and parameter disturbance is also estimated using extended state observer. Simulation on ADRC fractional order model stability analyzes. The results showed that, ADRC fractional order control system object has good control performance.