This page has only limited features, please log in for full access.
Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.
Jianhua Wang; Qiming Qin; Zhongling Gao; Jianghua Zhao; Xin Ye. A New Approach to Urban Road Extraction Using High-Resolution Aerial Image. ISPRS International Journal of Geo-Information 2016, 5, 114 .
AMA StyleJianhua Wang, Qiming Qin, Zhongling Gao, Jianghua Zhao, Xin Ye. A New Approach to Urban Road Extraction Using High-Resolution Aerial Image. ISPRS International Journal of Geo-Information. 2016; 5 (7):114.
Chicago/Turabian StyleJianhua Wang; Qiming Qin; Zhongling Gao; Jianghua Zhao; Xin Ye. 2016. "A New Approach to Urban Road Extraction Using High-Resolution Aerial Image." ISPRS International Journal of Geo-Information 5, no. 7: 114.
The scaling effect correction of retrieved parameters is an essential and difficult issue in analysis and application of remote sensing information. Based on fractal theory, this paper developed a scaling transfer model to correct the scaling effect of the leaf area index (LAI) estimated from coarse spatial resolution image. As the key parameter of the proposed model, the information fractal dimension (D) of the up-scaling pixel was calculated by establishing the double logarithmic linear relationship between D-2 and the normalized difference vegetation index (NDVI) standard deviation (σNDVI) of the up-scaling pixel. Based on the calculated D and the fractal relationship between the exact LAI and the approximated LAI estimated from the coarse resolution pixel, a LAI scaling transfer model was established. Finally, the model accuracy in correcting the scaling effect was discussed. Results indicated that the D increases with increasing σNDVI, and the D-2 was highly linearly correlated with σNDVI on the double logarithmic coordinate axis. The scaling transfer model corrected the scaling effect of LAI with a maximum value of root-mean-square error (RMSE) of 0.011. The maximum absolute correction error (ACE) and relative correction error (RCE) were only 0.108% and 8.56%, respectively. The spatial heterogeneity was the primary cause resulting in the scaling effect and the key influencing factor of correction effect. The results indicated that the developed method based on fractal theory could effectively correct the scaling effect of LAI estimated from the heterogeneous pixels.
Ling Wu; Qiming Qin; Xiangnan Liu; Huazhong Ren; Jianhua Wang; Xiaopo Zheng; Xin Ye; Yuejun Sun. Spatial Up-Scaling Correction for Leaf Area Index Based on the Fractal Theory. Remote Sensing 2016, 8, 197 .
AMA StyleLing Wu, Qiming Qin, Xiangnan Liu, Huazhong Ren, Jianhua Wang, Xiaopo Zheng, Xin Ye, Yuejun Sun. Spatial Up-Scaling Correction for Leaf Area Index Based on the Fractal Theory. Remote Sensing. 2016; 8 (3):197.
Chicago/Turabian StyleLing Wu; Qiming Qin; Xiangnan Liu; Huazhong Ren; Jianhua Wang; Xiaopo Zheng; Xin Ye; Yuejun Sun. 2016. "Spatial Up-Scaling Correction for Leaf Area Index Based on the Fractal Theory." Remote Sensing 8, no. 3: 197.
Road damage detection and assessment from high-resolution remote sensing image is critical for natural disaster investigation and disaster relief. In a disaster context, the pairing of pre-disaster and post-disaster road data for change detection and assessment is difficult to achieve due to the mismatch of different data sources, especially for rural areas where the pre-disaster data (i.e., remote sensing imagery or vector map) are hard to obtain. In this study, a knowledge-based method for road damage detection and assessment solely from post-disaster high-resolution remote sensing image is proposed. The road centerline is firstly extracted based on the preset road seed points. Then, features such as road brightness, standard deviation, rectangularity, and aspect ratio are selected to form a knowledge model. Finally, under the guidance of the road centerline, the post-disaster roads are extracted and the damaged roads are detected by applying the knowledge model. In order to quantitatively assess the damage degree, damage assessment indicators with their corresponding standard of damage grade are also proposed. The newly developed method is evaluated using a WorldView-1 image over Wenchuan, China acquired three days after the earthquake on 15 May 2008. The results show that the producer’s accuracy (PA) and user’s accuracy (UA) reached about 90% and 85%, respectively, indicating that the proposed method is effective for road damage detection and assessment. This approach also significantly reduces the need for pre-disaster remote sensing data.
Jianhua Wang; Qiming Qin; Jianghua Zhao; Xin Ye; Xiao Feng; Xuebin Qin; Xiucheng Yang. Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image. Remote Sensing 2015, 7, 4948 -4967.
AMA StyleJianhua Wang, Qiming Qin, Jianghua Zhao, Xin Ye, Xiao Feng, Xuebin Qin, Xiucheng Yang. Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image. Remote Sensing. 2015; 7 (4):4948-4967.
Chicago/Turabian StyleJianhua Wang; Qiming Qin; Jianghua Zhao; Xin Ye; Xiao Feng; Xuebin Qin; Xiucheng Yang. 2015. "Knowledge-Based Detection and Assessment of Damaged Roads Using Post-Disaster High-Resolution Remote Sensing Image." Remote Sensing 7, no. 4: 4948-4967.