scholarly journals Application of Object Based Classification and High Resolution Satellite Imagery for Savanna Ecosystem Analysis

2010 ◽  
Vol 2 (12) ◽  
pp. 2748-2772 ◽  
Author(s):  
Cerian Gibbes ◽  
Sanchayeeta Adhikari ◽  
Luke Rostant ◽  
Jane Southworth ◽  
Youliang Qiu
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Object Based ◽  
High Resolution Satellite Imagery

scholarly journals Designing an object based rule set for road detection from high resolution satellite imagery

2015 ◽  
Vol 3 (1) ◽  
pp. 77-94
Author(s):  
Maryam Nikfar ◽  
Mohammad Javad Valadan Zoej ◽  
Mehdi Mokhtarzade ◽  
Mahdi Aliyari Shoorehdeli ◽  
◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Road Detection ◽  
Object Based ◽  
Rule Set ◽  
High Resolution Satellite Imagery

scholarly journals OBJECT-BASED GREENHOUSE CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGERY: A CASE STUDY ANTALYA-TURKEY

2016 ◽  
Vol XLI-B7 ◽  
pp. 183-187 ◽  
Author(s):  
M. Coslu ◽  
N. K. Sonmez ◽  
D. Koc-San
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Accuracy Assessment ◽  
Digital Data ◽  
Classification Method ◽  
Classification Methods ◽  
Spectral Bands ◽  
Object Based ◽  
Rule Sets ◽  
High Resolution Satellite Imagery

Pixel-based classification method is widely used with the purpose of detecting land use and land cover with remote sensing technology. Recently, object-based classification methods have begun to be used as well as pixel-based classification method on high resolution satellite imagery. In the studies conducted, it is indicated that object-based classification method has more successful results than other classification methods. While pixel-based classification method is performed according to the grey value of pixels, object-based classification process is executed by generating imagery segmentation and updatable rule sets. In this study, it was aimed to detect and map the greenhouses from object-based classification method by using high resolution satellite imagery. The study was carried out in the Antalya province which includes greenhouse intensively. The study consists of three main stages including segmentation, classification and accuracy assessment. At the first stage, which was segmentation, the most important part of the object-based imagery analysis; imagery segmentation was generated by using basic spectral bands of high resolution Worldview-2 satellite imagery. At the second stage, applying the nearest neighbour classifier to these generated segments classification process was executed, and a result map of the study area was generated. Finally, accuracy assessments were performed using land studies and digital data of the area. According to the research results, object-based greenhouse classification using high resolution satellite imagery had over 80% accuracy.

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.

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