scholarly journals Light Reflectance Characteristics and Video Remote Sensing of Pricklypear

1991 ◽  
Vol 44 (6) ◽  
pp. 587 ◽  
Author(s):  
J. H. Everitt ◽  
D. E. Escobar ◽  
M. A. Alaniz ◽  
M. R. Davis
Keyword(s):  
Remote Sensing ◽  
Light Reflectance ◽  
Video Remote Sensing

Space Target Luminosity Measurement Based on Video Remote Sensing Satellites

2019 ◽  
Vol 48 (12) ◽  
pp. 1228002-1228002
Author(s):  
Yan MA Yan MA ◽  
Chi MA Chi MA ◽  
Yan-hao XIE Yan-hao XIE ◽  
Fang WANG Fang WANG
Keyword(s):  
Remote Sensing ◽  
Video Remote Sensing ◽  
Space Target

Hierarchical probability models and Bayesian analysis of mine locations

2000 ◽  
Vol 32 (02) ◽  
pp. 315-330
Author(s):  
Noel Cressie ◽  
Andrew B. Lawson
Keyword(s):  
Remote Sensing ◽  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Process Model ◽  
Probability Models ◽  
Point Patterns ◽  
Spatial Point ◽  
Sensing System ◽  
Video Remote Sensing ◽  
Posterior Analysis

Based on remote sensing of a potential minefield, point locations are identified, some of which may not be mines. The mines and mine-like objects are to be distinguished based on their point patterns, although it must be emphasized that all one sees is the superposition of their locations. In this paper, we construct a hierarchical spatial point-process model that accounts for the different patterns of mines and mine-like objects and uses posterior analysis to distinguish between them. Our Bayesian approach is applied to minefield data obtained from a multispectral video remote-sensing system.

Cloud detection of space-borne video remote sensing using improved Unet method

2021 ◽  
Author(s):  
Chongbin Xu ◽  
Shengling Geng ◽  
Defang Wang ◽  
Mingquan Zhou
Keyword(s):  
Remote Sensing ◽  
Cloud Detection ◽  
Video Remote Sensing

Hierarchical probability models and Bayesian analysis of mine locations

2000 ◽  
Vol 32 (2) ◽  
pp. 315-330 ◽  
Author(s):  
Noel Cressie ◽  
Andrew B. Lawson
Keyword(s):  
Remote Sensing ◽  
Bayesian Analysis ◽  
Bayesian Approach ◽  
Process Model ◽  
Probability Models ◽  
Point Patterns ◽  
Spatial Point ◽  
Sensing System ◽  
Video Remote Sensing ◽  
Posterior Analysis

Based on remote sensing of a potential minefield, point locations are identified, some of which may not be mines. The mines and mine-like objects are to be distinguished based on their point patterns, although it must be emphasized that all one sees is the superposition of their locations. In this paper, we construct a hierarchical spatial point-process model that accounts for the different patterns of mines and mine-like objects and uses posterior analysis to distinguish between them. Our Bayesian approach is applied to minefield data obtained from a multispectral video remote-sensing system.

Distinguishing Brush and Weeds on Rangelands Using Video Remote Sensing

1992 ◽  
Vol 6 (4) ◽  
pp. 913-921 ◽  
Author(s):  
James H. Everitt ◽  
David E. Escobar ◽  
Mario A. Alaniz ◽  
Ricardo Villarreal ◽  
Michael R. Davis
Keyword(s):  
Remote Sensing ◽  
Plant Species ◽  
Quantitative Data ◽  
Near Infrared ◽  
Video System ◽  
Video Imagery ◽  
Video Images ◽  
Computer Analyses ◽  
Video Remote Sensing ◽  
Color Imagery

This paper describes the application of a relatively new remote sensing tool, airborne video imagery, for distinguishing weed and brush species on rangelands. Plant species studied were false broomweed, spiny aster, and Chinese tamarisk. A multispectral video system that acquired color-infrared (CIR) composite imagery and its simultaneously synchronized three-band [near-infrared (NIR), red, and yellow-green] narrowband images was used for the false broomweed and spiny aster experiments. A conventional color camcorder video system was used to study Chinese tamarisk. False broomweed and spiny aster could be detected on CIR composite and NIR narrowband imagery, while Chinese tamarisk could be distinguished on conventional color imagery. Quantitative data obtained from digitized video images of the three species showed that their digital values were statistically different (P = 0.05) from those of associated vegetation and soil. Computer analyses of video images showed that populations of the three species could be quantified from associated vegetation. This technique permits area estimates of false broomweed, spiny aster, and Chinese tamarisk populations on rangeland and wildland areas.

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