scholarly journals URBAN SHANTY TOWN RECOGNITION BASED ON HIGH-RESOLUTION REMOTE SENSING IMAGES AND NATIONAL GEOGRAPHICAL MONITORING FEATURES – A CASE STUDY OF NANNING CITY

2018 ◽  
Vol XLII-3 ◽  
pp. 517-522
Author(s):  
Y. He ◽  
Y. He
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Texture Feature ◽  
Unsupervised Classification ◽  
Residential Areas ◽  
Remote Sensing Images ◽  
Maximum Probability ◽  
Different Types ◽  
Shanty Town ◽  
Nanning City

Urban shanty towns are communities that has contiguous old and dilapidated houses with more than 2000 square meters built-up area or more than 50 households. This study makes attempts to extract shanty towns in Nanning City using the product of Census and TripleSat satellite images. With 0.8-meter high-resolution remote sensing images, five texture characteristics (energy, contrast, maximum probability, and inverse difference moment) of shanty towns are trained and analyzed through GLCM. In this study, samples of shanty town are well classified with 98.2 % producer accuracy of unsupervised classification and 73.2 % supervised classification correctness. Low-rise and mid-rise residential blocks in Nanning City are classified into 4 different types by using k-means clustering and nearest neighbour classification respectively. This study initially establish texture feature descriptions of different types of residential areas, especially low-rise and mid-rise buildings, which would help city administrator evaluate residential blocks and reconstruction shanty towns.

scholarly journals AUTOMATIC BUILDING DAMAGE DETECTION METHOD USING HIGH-RESOLUTION REMOTE SENSING IMAGES AND 3D GIS MODEL

2016 ◽  
Vol III-8 ◽  
pp. 43-50 ◽  
Author(s):  
Jihui Tu ◽  
Haigang Sui ◽  
Wenqing Feng ◽  
Zhina Song
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Damage Detection ◽  
Detection Method ◽  
Texture Feature ◽  
Correction Method ◽  
Building Damage ◽  
3D Gis ◽  
Remote Sensing Images ◽  
Gis Model

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

scholarly journals AUTOMATIC BUILDING DAMAGE DETECTION METHOD USING HIGH-RESOLUTION REMOTE SENSING IMAGES AND 3D GIS MODEL

2016 ◽  
Vol III-8 ◽  
pp. 43-50 ◽  
Author(s):  
Jihui Tu ◽  
Haigang Sui ◽  
Wenqing Feng ◽  
Zhina Song
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Damage Detection ◽  
Detection Method ◽  
Texture Feature ◽  
Correction Method ◽  
Building Damage ◽  
3D Gis ◽  
Remote Sensing Images ◽  
Gis Model

In this paper, a novel approach of building damaged detection is proposed using high resolution remote sensing images and 3D GIS-Model data. Traditional building damage detection method considers to detect damaged building due to earthquake, but little attention has been paid to analyze various building damaged types(e.g., trivial damaged, severely damaged and totally collapsed.) Therefore, we want to detect the different building damaged type using 2D and 3D feature of scenes because the real world we live in is a 3D space. The proposed method generalizes that the image geometric correction method firstly corrects the post-disasters remote sensing image using the 3D GIS model or RPC parameters, then detects the different building damaged types using the change of the height and area between the pre- and post-disasters and the texture feature of post-disasters. The results, evaluated on a selected study site of the Beichuan earthquake ruins, Sichuan, show that this method is feasible and effective in building damage detection. It has also shown that the proposed method is easily applicable and well suited for rapid damage assessment after natural disasters.

scholarly journals Remote estimation of rice LAI based on Fourier spectrum texture from UAV image

Plant Methods ◽  
2019 ◽  
Vol 15 (1) ◽  
Author(s):  
Bo Duan ◽  
Yating Liu ◽  
Yan Gong ◽  
Yi Peng ◽  
Xianting Wu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Fourier Spectrum ◽  
Growth Parameters ◽  
Spectral Feature ◽  
Texture Feature ◽  
Accurate Estimation ◽  
Remote Sensing Images ◽  
Vegetation Growth ◽  
Uav Image

Abstract Background The accurate estimation of rice LAI is particularly important to monitor rice growth status. Remote sensing, as a non-destructive measurement technology, has been proved to be useful for estimating vegetation growth parameters, especially at large scale. With the development of unmanned aerial vehicles (UAVs), this novel remote sensing platform has been widely used to provide remote sensing images which have much higher spatial resolution. Previous reports have shown that the spectral feature of remote sensing images could be an effective indicator to estimate vegetation growth parameters. However, the texture feature of high-resolution remote sensing images is rarely employed for this purpose. Besides, the physical mechanism between the texture feature and vegetation growth parameters is still unclear. Results In this study, a Fourier spectrum texture based on the UAV Image was developed to estimate rice LAI. And the relationship between Fourier spectrum texture and rice LAI was also analyzed. The results showed that Fourier spectrum texture could improve the accuracy of rice LAI estimation. Conclusions In conclusion, the texture feature of high-resolution remote sensing images may be more effective in rice LAI estimation than the spectral feature.

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