Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Shaobo Xia"'
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 13308-13323 (2024)
The micropulse multibeam photon-counting laser altimeter significantly improves the sampling density along the orbit while introducing abundant noise. Therefore, noise removal is crucial for the subsequent applications of the photon-counting laser al
Externí odkaz:
https://doaj.org/article/127b064d1428431fa5d7134674bb8fa8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 15, Pp 3533-3538 (2022)
Individual tree detection from airborne laser scanning (ALS) point clouds is the basis for forestry inventory and further applications. In the past decade, many methods have been developed to localize tree instances in ALS point clouds. These methods
Externí odkaz:
https://doaj.org/article/371e440906444691a17134469dd4030a
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 685-707 (2020)
To the best of our knowledge, the most recent light detection and ranging (lidar)-based surveys have been focused only on specific applications such as reconstruction and segmentation, as well as data processing techniques based on a specific platfor
Externí odkaz:
https://doaj.org/article/bfa39f00b8fa4328a5925f0ca01ebf9a
Publikováno v:
Atmosphere, Vol 13, Iss 8, p 1294 (2022)
Hybrid electrostatic precipitators consisting of electrostatic precipitation (ESP) and a bag filter are potential devices for ultralow emissions. The ESP captures and charges the particles; subsequently, the charged particles that escape enter the ba
Externí odkaz:
https://doaj.org/article/d4671980e3814f5491f0ee386826b519
Publikováno v:
Remote Sensing, Vol 13, Iss 3, p 338 (2021)
Tree localization in point clouds of forest scenes is critical in the forest inventory. Most of the existing methods proposed for TLS forest data are based on model fitting or point-wise features which are time-consuming, sensitive to data incomplete
Externí odkaz:
https://doaj.org/article/61a925ccea7843569c051cefe3ef2b27
Publikováno v:
Remote Sensing, Vol 12, Iss 1, p 135 (2020)
In outdoor Light Detection and Ranging (lidar)point cloud classification, finding the discriminative features for point cloud perception and scene understanding represents one of the great challenges. The features derived from defect-laden (i.e., noi
Externí odkaz:
https://doaj.org/article/5ed45e5376f9471080cddeb0d5cade6a
Publikováno v:
Forests, Vol 6, Iss 11, Pp 3923-3945 (2015)
Stem characteristics of plants are of great importance to both ecology study and forest management. Terrestrial laser scanning (TLS) may provide an effective way to characterize the fine-scale structures of vegetation. However, clumping plants, dense
Externí odkaz:
https://doaj.org/article/3c7870672abb42339ed21379da744ca0
Publikováno v:
Remote Sensing, Vol 11, Iss 10, p 1248 (2019)
This paper presents a novel framework to achieve 3D semantic labeling of objects (e.g., trees, buildings, and vehicles) from airborne laser-scanning point clouds. To this end, we propose a framework which consists of hierarchical clustering and highe
Externí odkaz:
https://doaj.org/article/5a5baca9baa44cd4a1116b4fd6c8ac38
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 5, Iss 4, p 45 (2016)
Forest biomass is an important parameter for quantifying and understanding biological and physical processes on the Earth’s surface. Rapid, reliable, and objective estimations of forest biomass are essential to terrestrial ecosystem research. The G
Externí odkaz:
https://doaj.org/article/329df0bc8ad44c8483d43f4c8cf83140
Publikováno v:
Remote Sensing, Vol 8, Iss 1, p 3 (2015)
Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact A
Externí odkaz:
https://doaj.org/article/1112f6c50b3d40b0904aef6fa5052b9a