A new method to estimate SNR of remote sensing imagery

2017 ◽  
Author(s):  
Bo Zhu ◽  
Chuanrong Li ◽  
Xinhong Wang ◽  
Chaoliang Wang
Keyword(s):  
Remote Sensing ◽  
New Method ◽  
Remote Sensing Imagery

New method for assessment of geometry correction of remote sensing imagery

2007 ◽  
Author(s):  
Huaguo Zhang ◽  
Weigen Huang ◽  
Dongling Li ◽  
Changbao Zhou
Keyword(s):  
Remote Sensing ◽  
New Method ◽  
Remote Sensing Imagery ◽  
Geometry Correction

scholarly journals An Integrated Use of Topography with RSI in Gully Mapping, Shandong Peninsula, China

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Fuhong He ◽  
Tao Wang ◽  
Lijuan Gu ◽  
Tao Li ◽  
Weiguo Jiang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Accuracy Assessment ◽  
Color Space ◽  
Study Data ◽  
Kappa Coefficient ◽  
New Method ◽  
Positional Accuracy ◽  
Error Matrix ◽  
Remote Sensing Imagery ◽  
Fused Image

Taking the Quickbird optical satellite imagery of the small watershed of Beiyanzigou valley of Qixia city, Shandong province, as the study data, we proposed a new method by using a fused image of topography with remote sensing imagery (RSI) to achieve a high precision interpretation of gully edge lines. The technique first transformed remote sensing imagery into HSV color space from RGB color space. Then the slope threshold values of gully edge line and gully thalweg were gained through field survey and the slope data were segmented using thresholding, respectively. Based on the fused image in combination with gully thalweg thresholding vectors, the gully thalweg thresholding vectors were amended. Lastly, the gully edge line might be interpreted based on the amended gully thalweg vectors, fused image, gully edge line thresholding vectors, and slope data. A testing region was selected in the study area to assess the accuracy. Then accuracy assessment of the gully information interpreted by both interpreting remote sensing imagery only and the fused image was performed using the deviation, kappa coefficient, and overall accuracy of error matrix. Compared with interpreting remote sensing imagery only, the overall accuracy and kappa coefficient are increased by 24.080% and 264.364%, respectively. The average deviations of gully head and gully edge line are reduced by 60.448% and 67.406%, respectively. The test results show the thematic and the positional accuracy of gully interpreted by new method are significantly higher. Finally, the error sources for interpretation accuracy by the two methods were analyzed.

Estimations for Percentage of Impervious Area by the Use of Satellite Remote Sensing Imagery

1994 ◽  
Vol 29 (1-2) ◽  
pp. 135-144 ◽  
Author(s):  
C. Deguchi ◽  
S. Sugio
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Imagery ◽  
Satellite Remote Sensing ◽  
Aerial Photographs ◽  
Estimation Errors ◽  
Remote Sensing Imagery ◽  
Impervious Area ◽  
Urbanized Areas

This study aims to evaluate the applicability of satellite imagery in estimating the percentage of impervious area in urbanized areas. Two methods of estimation are proposed and applied to a small urbanized watershed in Japan. The area is considered under two different cases of subdivision; i.e., 14 zones and 17 zones. The satellite imageries of LANDSAT-MSS (Multi-Spectral Scanner) in 1984, MOS-MESSR(Multi-spectral Electronic Self-Scanning Radiometer) in 1988 and SPOT-HRV(High Resolution Visible) in 1988 are classified. The percentage of imperviousness in 17 zones is estimated by using these classification results. These values are compared with the ones obtained from the aerial photographs. The percent imperviousness derived from the imagery agrees well with those derived from aerial photographs. The estimation errors evaluated are less than 10%, the same as those obtained from aerial photographs.

PIXELS: HOW CLASSROOM-BASED AND FIELD-BASED LEARNING IMPACT STUDENTS’ SENSE OF SCALE AND UNDERSTANDING OF REMOTE SENSING IMAGERY

2017 ◽  
Author(s):  
Heather L. Petcovic ◽  
◽  
Laura Tinigin ◽  
Allen Pope ◽  
Natalie Bursztyn ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Imagery
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