scholarly journals An Object-Based Semantic Classification Method for High Resolution Remote Sensing Imagery Using Ontology

2017 ◽  
Vol 9 (4) ◽  
pp. 329 ◽  
Author(s):  
Haiyan Gu ◽  
Haitao Li ◽  
Li Yan ◽  
Zhengjun Liu ◽  
Thomas Blaschke ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Classification Method ◽  
Semantic Classification ◽  
Remote Sensing Imagery ◽  
Object Based

The Object-based Method of Shadow Detection in High-resolution Remote Sensing Imagery

2015 ◽  
Vol 44 (12) ◽  
pp. 1228002 ◽  
Author(s):  
帅滔 SHUAI Tao ◽  
张洪艳 ZHANG Hong-yan ◽  
张良培 ZHANG Liang-pei
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Shadow Detection ◽  
Remote Sensing Imagery ◽  
Object Based

scholarly journals Mapping Mangrove Forests Based on Multi-Tidal High-Resolution Satellite Imagery

2018 ◽  
Vol 10 (9) ◽  
pp. 1343 ◽  
Author(s):  
Qing Xia ◽  
Cheng-Zhi Qin ◽  
He Li ◽  
Chong Huang ◽  
Fen-Zhen Su
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Satellite Imagery ◽  
High Tide ◽  
Spectral Signature ◽  
Support Vector ◽  
Mangrove Forests ◽  
Remote Sensing Imagery ◽  
Object Based ◽  
High Resolution Satellite Imagery

Mangrove forests, which are essential for stabilizing coastal ecosystems, have been suffering from a dramatic decline over the past several decades. Mapping mangrove forests using satellite imagery is an efficient way to provide key data for mangrove forest conservation. Since mangrove forests are periodically submerged by tides, current methods of mapping mangrove forests, which are normally based on single-date, remote-sensing imagery, often underestimate the spatial distribution of mangrove forests, especially when the images used were recorded during high-tide periods. In this paper, we propose a new method of mapping mangrove forests based on multi-tide, high-resolution satellite imagery. In the proposed method, a submerged mangrove recognition index (SMRI), which is based on the differential spectral signature of mangroves under high and low tides from multi-tide, high-resolution satellite imagery, is designed to identify submerged mangrove forests. The proposed method applies the SMRI values, together with textural features extracted from high-resolution imagery and geographical features of mangrove forests, to an object-based support vector machine (SVM) to map mangrove forests. The proposed method was evaluated via a case study with GF-1 images (high-resolution satellites launched by China) in Yulin City, Guangxi Zhuang Autonomous Region of China. The results show that our proposed method achieves satisfactory performance, with a kappa coefficient of 0.86 and an overall accuracy of 94%, which is better than results obtained from object-based SVMs that use only single-date, remote sensing imagery.

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.

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