Three-dimensional optical remote sensing using optical scanning holography

2005 ◽  
Author(s):  
Ting-Chung Poon
Keyword(s):  
Remote Sensing ◽  
Three Dimensional ◽  
Optical Remote Sensing ◽  
Optical Scanning ◽  
Optical Scanning Holography

Three-dimensional remote sensing by optical scanning holography

2001 ◽  
Vol 40 (30) ◽  
pp. 5474 ◽  
Author(s):  
Bradley W. Schilling ◽  
Glen C. Templeton
Keyword(s):  
Remote Sensing ◽  
Three Dimensional ◽  
Optical Scanning ◽  
Optical Scanning Holography

scholarly journals Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽  
2020 ◽  
Vol 18 (1) ◽  
pp. 951-960
Author(s):  
Haiqing Zhang ◽  
Jun Han
Keyword(s):  
Remote Sensing ◽  
Recognition Rate ◽  
Three Dimensional ◽  
Remote Sensing Image ◽  
Optical Remote Sensing ◽  
Specific Class ◽  
Three Dimensional Model ◽  
Image Information ◽  
Information Classification ◽  
Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

Three-dimensional edge extraction in optical scanning holography

2016 ◽  
Author(s):  
Yonghong Zong ◽  
Changhe Zhou ◽  
Jianyong Ma ◽  
Wei Jia ◽  
Jin Wang
Keyword(s):  
Three Dimensional ◽  
Edge Extraction ◽  
Optical Scanning ◽  
Optical Scanning Holography

scholarly journals Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Guangjian Yan ◽  
Hailan Jiang ◽  
Jinghui Luo ◽  
Xihan Mu ◽  
Fan Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Inclination Angle ◽  
Three Dimensional ◽  
Good Choice ◽  
Optical Remote Sensing ◽  
Angle Distribution ◽  
Area Index ◽  
Leaf Inclination

Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.

scholarly journals A Data Augmentation Strategy Based on Simulated Samples for Ship Detection in RGB Remote Sensing Images

2019 ◽  
Vol 8 (6) ◽  
pp. 276 ◽  
Author(s):  
Yiming Yan ◽  
Zhichao Tan ◽  
Nan Su
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Data Augmentation ◽  
Three Dimensional ◽  
Small Sample ◽  
Optical Remote Sensing ◽  
Ship Detection ◽  
Training Samples ◽  
Augmentation Strategy ◽  
Three Dimensional Models

In this paper, we propose a data augmentation method for ship detection. Inshore ship detection using optical remote sensing imaging is a challenging task owing to an insufficient number of training samples. Although the multilayered neural network method has achieved excellent results in recent research, a large number of training samples is indispensable to guarantee the accuracy and robustness of ship detection. The majority of researchers adopt such strategies as clipping, scaling, color transformation, and flipping to enhance the samples. Nevertheless, these methods do not essentially increase the quality of the dataset. A novel data augmentation strategy was thus proposed in this study by using simulated remote sensing ship images to augment the positive training samples. The simulated images are generated by true background images and three-dimensional models on the same scale as real ships. A faster region-based convolutional neural network (Faster R-CNN) based on Res101netwok was trained by the dataset, which is composed of both simulated and true images. A series of experiments is designed under small sample conditions; the experimental results show that better detection is obtained with our data augmentation strategy.

Sign in / Sign up

Export Citation Format

Share Document