scholarly journals Guest Editorial: Special Section on Optical Remote Sensing and Image Processing

1995 ◽  
Vol 34 (11) ◽  
pp. 3095
Author(s):  
Mohammad A. Karim
Author(s):  
Man Sing Wong ◽  
Xiaolin Zhu ◽  
Sawaid Abbas ◽  
Coco Yin Tung Kwok ◽  
Meilian Wang

AbstractApplications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.


2017 ◽  
Vol 26 (1) ◽  
pp. 011001 ◽  
Author(s):  
Aladine Chetouani ◽  
Robert Erdmann ◽  
David Picard ◽  
Filippo Stanco

2019 ◽  
Vol 11 (19) ◽  
pp. 2216
Author(s):  
Xin Huang ◽  
Jiayi Li ◽  
Francesca Bovolo ◽  
Qi Wang

This special issue hosts papers on change detection technologies and analysis in remote sensing, including multi-source sensors, advanced machine learning technologies for change information mining, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multi-source remote sensed data was used in change detection.


Sign in / Sign up

Export Citation Format

Share Document