scholarly journals Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

2018 ◽  
Vol 10 (2) ◽  
pp. 276 ◽  
Author(s):  
Xin Wang ◽  
Sicong Liu ◽  
Peijun Du ◽  
Hao Liang ◽  
Junshi Xia ◽  
...  
Keyword(s):  
Change Detection ◽  
Ensemble Learning ◽  
Spatial Resolution ◽  
Urban Areas ◽  
High Spatial Resolution ◽  
Multiple Features ◽  
Object Based

scholarly journals Geo-Object-Based Land Cover Map Update for High-Spatial-Resolution Remote Sensing Images via Change Detection and Label Transfer

2020 ◽  
Vol 12 (1) ◽  
pp. 174
Author(s):  
Tianjun Wu ◽  
Jiancheng Luo ◽  
Ya’nan Zhou ◽  
Changpeng Wang ◽  
Jiangbo Xi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Data Set ◽  
Label Transfer ◽  
Object Based ◽  
Training Samples ◽  
Map Updating

Land cover (LC) information plays an important role in different geoscience applications such as land resources and ecological environment monitoring. Enhancing the automation degree of LC classification and updating at a fine scale by remote sensing has become a key problem, as the capability of remote sensing data acquisition is constantly being improved in terms of spatial and temporal resolution. However, the present methods of generating LC information are relatively inefficient, in terms of manually selecting training samples among multitemporal observations, which is becoming the bottleneck of application-oriented LC mapping. Thus, the objectives of this study are to speed up the efficiency of LC information acquisition and update. This study proposes a rapid LC map updating approach at a geo-object scale for high-spatial-resolution (HSR) remote sensing. The challenge is to develop methodologies for quickly sampling. Hence, the core step of our proposed methodology is an automatic method of collecting samples from historical LC maps through combining change detection and label transfer. A data set with Chinese Gaofen-2 (GF-2) HSR satellite images is utilized to evaluate the effectiveness of our method for multitemporal updating of LC maps. Prior labels in a historical LC map are certified to be effective in a LC updating task, which contributes to improve the effectiveness of the LC map update by automatically generating a number of training samples for supervised classification. The experimental outcomes demonstrate that the proposed method enhances the automation degree of LC map updating and allows for geo-object-based up-to-date LC mapping with high accuracy. The results indicate that the proposed method boosts the ability of automatic update of LC map, and greatly reduces the complexity of visual sample acquisition. Furthermore, the accuracy of LC type and the fineness of polygon boundaries in the updated LC maps effectively reflect the characteristics of geo-object changes on the ground surface, which makes the proposed method suitable for many applications requiring refined LC maps.

Object-based feature extraction using high spatial resolution satellite data of urban areas

2010 ◽  
Vol 55 (1) ◽  
pp. 117-132 ◽  
Author(s):  
H. Taubenböck ◽  
T. Esch ◽  
M. Wurm ◽  
A. Roth ◽  
S. Dech
Keyword(s):  
Feature Extraction ◽  
Spatial Resolution ◽  
Satellite Data ◽  
Urban Areas ◽  
High Spatial Resolution ◽  
Object Based

An Object-Based Basic Farmland Change Detection Using High Spatial Resolution Image and GIS Data of Land Use Planning

2012 ◽  
Vol 500 ◽  
pp. 492-499 ◽  
Author(s):  
Peng Lin Zhang ◽  
Bo Lin Ruan ◽  
Jian Chao
Keyword(s):  
Land Use ◽  
Change Detection ◽  
Spatial Resolution ◽  
Land Use Planning ◽  
High Spatial Resolution ◽  
Rapid Development ◽  
Satellite Image ◽  
Object Based ◽  
Detection Approach ◽  
Gis Data

With the rapid development of urbanization in China, an effective and quick approach usedto identify changes in basic farmland becomes more and more important in land use resource managementfield. While a variety of change detection approaches using multi-temporal satellite image havebeen reported, few approaches using GIS data of land use planning and single-temporal high spatialresolution satellite image have been reported. This paper proposes an object-based basic farmlandchange detection approach using single-temporal high spatial resolution satellite image and GIS dataof land use planning. Compared with the pixel-based change detection approach, the object-based approachmust be more suitable for in high spatial resolution images. To test the validity of the proposedapproach, we apply it to the actual data, and primary results reveal that the proposed approach is valid.

Sign in / Sign up

Export Citation Format

Share Document