Yancong Wei
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View article: Multimodal sensor dataset from vehicle-mounted mobile mapping system for comprehensive urban scenes
Multimodal sensor dataset from vehicle-mounted mobile mapping system for comprehensive urban scenes Open
Mobile mapping is the research trend in the mapping field due to its superior time efficiency compared to traditional fixed mapping methods. It is an important digital base for numerous applications, such as high-definition (HD) maps, digi…
View article: Diagnosis of array antennas based on near-field data using Faster R-CNN
Diagnosis of array antennas based on near-field data using Faster R-CNN Open
In this paper, a source reconstruction method for detecting failures in array antenna elements using near-field data based on Faster region-convolutional neural network (Faster R-CNN) is introduced. The proposed method leverages the conver…
View article: Plane-Assisted Indoor Lidar Slam
Plane-Assisted Indoor Lidar Slam Open
View article: CMPL-Calib: Automatic Spatiotemporal Calibration for LiDAR-360 Camera Fusion via Cross-Modal Panoramic Localization
CMPL-Calib: Automatic Spatiotemporal Calibration for LiDAR-360 Camera Fusion via Cross-Modal Panoramic Localization Open
View article: AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSION
AUTOMATIC EXTRACTION OF ROAD CENTERLINES AND EDGE LINES FROM AERIAL IMAGES VIA CNN-BASED REGRESSION Open
Extracting roads from aerial images is a challenging task in the field of remote sensing. Most approaches formulate road extraction as a segmentation problem and use thinning and edge detection to obtain road centerlines and edge lines, wh…
View article: Spatial–Spectral Fusion by Combining Deep Learning and Variational Model
Spatial–Spectral Fusion by Combining Deep Learning and Variational Model Open
In the field of spatial-spectral fusion, the model-based method and the deep\nlearning (DL)-based method are state-of-the-art. This paper presents a fusion\nmethod that incorporates the deep neural network into the model-based method\nfor …
View article: CLOUD DETECTION BY FUSING MULTI-SCALE CONVOLUTIONAL FEATURES
CLOUD DETECTION BY FUSING MULTI-SCALE CONVOLUTIONAL FEATURES Open
Clouds detection is an important pre-processing step for accurate application of optical satellite imagery. Recent studies indicate that deep learning achieves best performance in image segmentation tasks. Aiming at boosting the accuracy o…
View article: A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening Open
Pan-sharpening is a fundamental and significant task in the field of remote sensing imagery processing, in which high-resolution spatial details from panchromatic images are employed to enhance the spatial resolution of multi-spectral (MS)…
View article: Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network
Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network Open
In the field of fusing multi-spectral and panchromatic images (Pan-sharpening), the impressive effectiveness of deep neural networks has been recently employed to overcome the drawbacks of traditional linear models and boost the fusing acc…