scholarly journals Prediction of crop biophysical variables with panel data techniques and radar remote sensing imagery

2021 ◽  
Vol 205 ◽  
pp. 76-92
Author(s):  
Clara Simón de Blas ◽  
Rubén Valcarce-Diñeiro ◽  
Ana E. Sipols ◽  
Nilda Sánchez Martín ◽  
Benjamín Arias-Pérez ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Panel Data ◽  
Radar Remote Sensing ◽  
Remote Sensing Imagery

Balance learning for ship detection from synthetic aperture radar remote sensing imagery

2021 ◽  
Vol 182 ◽  
pp. 190-207
Author(s):  
Tianwen Zhang ◽  
Xiaoling Zhang ◽  
Chang Liu ◽  
Jun Shi ◽  
Shunjun Wei ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Radar Remote Sensing ◽  
Remote Sensing Imagery ◽  
Ship Detection ◽  
Aperture Radar

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

Machine learning-based crop recognition from aerial remote sensing imagery

2021 ◽  
Vol 15 (1) ◽  
pp. 54-69
Author(s):  
Yanqin Tian ◽  
Chenghai Yang ◽  
Wenjiang Huang ◽  
Jia Tang ◽  
Xingrong Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Remote Sensing Imagery ◽  
Aerial Remote Sensing
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