The Earthquake Damage Remote Sensing Automatic Recgnition Based on Texture of Roof Tiles

2014 ◽  
Vol 651-653 ◽  
pp. 1315-1319 ◽  
Author(s):  
Dong Ping Li ◽  
Jun Gong ◽  
Jing Yi Li ◽  
Shan Shan Guo
Keyword(s):  
Remote Sensing ◽  
Evaluation Method ◽  
Rapid Assessment ◽  
Texture Features ◽  
Technology Support ◽  
Remote Sensing Images ◽  
Gis Technology ◽  
Disaster Assessment ◽  
High Resolution Images ◽  
Earthquake Damages

To meet the technical demands of rapid assessment on small and medium earthquake damages, this paper presents the comprehensive disaster evaluation method of on-spot human-computer interaction survey and remote sensing image analysis based on the GIS technology support in the small and medium earthquakes. By making full use of the advantages of existing data, emphasizing on the automatical identification of the unique texture features of small earthquakes with a combination analysis on high resolution images gained from unmanned aerial vehicles (uav) and the seismic damages, the new method results in the rank distribution of earthquakes by gaining the experienced parameter of local small-medium earthquakes based on the analysis of regional characteristics of texture features of remote sensing images. It is concluded that the evaluation method is more accurate and efficient for small and medium earthquake rapid disaster assessment.

scholarly journals CHANGE DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES USING LEVENE-TEST AND FUZZY EVALUATION

2018 ◽  
Vol XLII-3 ◽  
pp. 1695-1701
Author(s):  
G. H. Wang ◽  
H. B. Wang ◽  
W. F. Fan ◽  
Y. Liu ◽  
H. J. Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Evaluation Method ◽  
Fault Tolerant ◽  
Texture Features ◽  
Fuzzy Evaluation ◽  
Remote Sensing Images ◽  
Texture Information ◽  
Levene Test

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

scholarly journals MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 891-898 ◽  
Author(s):  
Ye Lv ◽  
Guofeng Wang ◽  
Xiangyun Hu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Learning Algorithm ◽  
Mean Shift ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
City Development ◽  
Remote Sensing Images ◽  
High Resolution Images

At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.

scholarly journals MACHINE LEARNING BASED ROAD DETECTION FROM HIGH RESOLUTION IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 891-898
Author(s):  
Ye Lv ◽  
Guofeng Wang ◽  
Xiangyun Hu
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Learning Algorithm ◽  
Mean Shift ◽  
Texture Features ◽  
Supervised Machine Learning ◽  
City Development ◽  
Remote Sensing Images ◽  
High Resolution Images

At present, remote sensing technology is the best weapon to get information from the earth surface, and it is very useful in geo- information updating and related applications. Extracting road from remote sensing images is one of the biggest demand of rapid city development, therefore, it becomes a hot issue. Roads in high-resolution images are more complex, patterns of roads vary a lot, which becomes obstacles for road extraction. In this paper, a machine learning based strategy is presented. The strategy overall uses the geometry features, radiation features, topology features and texture features. In high resolution remote sensing images, the images cover a great scale of landscape, thus, the speed of extracting roads is slow. So, roads’ ROIs are firstly detected by using Houghline detection and buffering method to narrow down the detecting area. As roads in high resolution images are normally in ribbon shape, mean-shift and watershed segmentation methods are used to extract road segments. Then, Real Adaboost supervised machine learning algorithm is used to pick out segments that contain roads’ pattern. At last, geometric shape analysis and morphology methods are used to prune and restore the whole roads’ area and to detect the centerline of roads.

scholarly journals Remote Sensing Single-Image Resolution Improvement Using A Deep Gradient-Aware Network with Image-Specific Enhancement

2020 ◽  
Vol 12 (5) ◽  
pp. 758 ◽  
Author(s):  
Mengjiao Qin ◽  
Sébastien Mavromatis ◽  
Linshu Hu ◽  
Feng Zhang ◽  
Renyi Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Image Resolution ◽  
Single Image ◽  
Remote Sensing Images ◽  
Practical Applications ◽  
Specific Enhancement ◽  
Urban Monitoring ◽  
High Resolution Images

Super-resolution (SR) is able to improve the spatial resolution of remote sensing images, which is critical for many practical applications such as fine urban monitoring. In this paper, a new single-image SR method, deep gradient-aware network with image-specific enhancement (DGANet-ISE) was proposed to improve the spatial resolution of remote sensing images. First, DGANet was proposed to model the complex relationship between low- and high-resolution images. A new gradient-aware loss was designed in the training phase to preserve more gradient details in super-resolved remote sensing images. Then, the ISE approach was proposed in the testing phase to further improve the SR performance. By using the specific features of each test image, ISE can further boost the generalization capability and adaptability of our method on inexperienced datasets. Finally, three datasets were used to verify the effectiveness of our method. The results indicate that DGANet-ISE outperforms the other 14 methods in the remote sensing image SR, and the cross-database test results demonstrate that our method exhibits satisfactory generalization performance in adapting to new data.

scholarly journals Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

2019 ◽  
Vol 11 (2) ◽  
pp. 108 ◽  
Author(s):  
Lu Xu ◽  
Dongping Ming ◽  
Wen Zhou ◽  
Hanqing Bao ◽  
Yangyang Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Color Space ◽  
Texture Features ◽  
Rate Of Change ◽  
Parameters Estimation ◽  
Remote Sensing Images ◽  
Image Region ◽  
Local Variance

Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction.

Research on Quick Monitoring and Evaluating of Flood Disaster in Poyang Lake Area Based on Remote Sensing

2013 ◽  
Vol 303-306 ◽  
pp. 740-743
Author(s):  
Qun Sun
Keyword(s):  
Remote Sensing ◽  
Poyang Lake ◽  
Rapid Assessment ◽  
Economic Loss ◽  
Flood Disaster ◽  
Lake Area ◽  
Gis Technology ◽  
Remote Sensing Monitoring ◽  
Rs And Gis ◽  
Poyang Lake Area

Flood disaster happens frequently in Poyang Lake area, which causes a huge economic loss each year. In order to prevent and reduce the loss caused by flood disaster, and to promote the economic development in Poyang Lake area, the author has researched methods of quick monitoring and evaluating of flood disaster based on RS and GIS. Firstly, the author discusses the technical means of monitoring and assessment of flood disaster, which includes remote monitoring technology and GIS technology. Secondly, taking Poyang Lake area for example, the author introduces the methods and processes of remote sensing monitoring of flood disaster. Finally, various data of damage has been computed rapidly to achieve the purpose of rapid assessment of the loss by using the function of spatial analysis of GIS and withdrawing flooded areas from the remote sensing monitoring image combined with background data.

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