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.

Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

2018 ◽  
Vol 46 (11) ◽  
pp. 1805-1814
Author(s):  
Tianjun Wu ◽  
Liegang Xia ◽  
Jiancheng Luo ◽  
Xiaocheng Zhou ◽  
Xiaodong Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Computationally Efficient ◽  
Remote Sensing Images

Study on Remote Sensing Images of De-Blurring Based on Dictionary Learning

2015 ◽  
Vol 738-739 ◽  
pp. 217-222
Author(s):  
Yan Jia ◽  
Zhen Tao Qin ◽  
Bang Xin Yang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Dictionary Learning ◽  
High Spatial Resolution ◽  
Learning Algorithm ◽  
Research Field ◽  
Projection Algorithm ◽  
Remote Sensing Images ◽  
Novel Algorithm

De-blurring the high resolution remote sensing images is an important issue in the relative research field of remote sensing. In this paper a novel algorithm of de-blurring the high resolution remote sensing images is proposed based on sparse representation. The high spatial resolution remote sensing images can be de-blurred by gradient projection algorithm, and keep the useful information of the image. The experimental results of the remote sensing images obtained by “the first satellite of high resolution” show that the algorithm can de-blur the image more effectively and improve the PSNR, this method has better performance than other dictionary learning algorithm.

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 STUDY ON BUILDING EXTRACTION FROM HIGH-RESOLUTION IMAGES USING MBI

2018 ◽  
Vol XLII-3 ◽  
pp. 283-287 ◽  
Author(s):  
Z. Ding ◽  
X. Q. Wang ◽  
Y. L. Li ◽  
S. S. Zhang
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Extraction Methods ◽  
Research Topic ◽  
False Alarms ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Shape Constraint ◽  
High Resolution Images

Building extraction from high resolution remote sensing images is a hot research topic in the field of photogrammetry and remote sensing. However, the diversity and complexity of buildings make building extraction methods still face challenges in terms of accuracy, efficiency, and so on. In this study, a new building extraction framework based on MBI and combined with image segmentation techniques, spectral constraint, shadow constraint, and shape constraint is proposed. In order to verify the proposed method, worldview-2, GF-2, GF-1 remote sensing images covered Xiamen Software Park were used for building extraction experiments. Experimental results indicate that the proposed method improve the original MBI significantly, and the correct rate is over 86 %. Furthermore, the proposed framework reduces the false alarms by 42 % on average compared to the performance of the original MBI.

scholarly journals RELATIONSHIP MODEL BETWEEN NIGHTLIGHT DATA AND FLOOR AREA RATIO FROM HIGH RESOLUTION IMAGES

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1419-1422
Author(s):  
M. Yan ◽  
L. Xu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Development Trend ◽  
Area Ratio ◽  
Remote Sensing Images ◽  
Floor Area ◽  
Floor Area Ratio ◽  
Economic Information ◽  
Relationship Model ◽  
High Resolution Images

It is a hotpot that extraction the floor area ratio from high resolution remote sensing images. It is a development trend of using nightlight data to survey the urban social and economic information. This document aims to provide a conference relationship model for VIIRS/NPP nightlight data and floor Area Ratio from High Resolution ZY-3 Images. It shows that there is a lineal relationship between the shadow and the floor area ratio, and the R<sup>2</sup> is 0.98. It shows that there is a quadratic polynomial relationship between the floor area ratio and the nightlight, and the R<sup>2</sup> is 0.611. We can get a conclusion that, VIIRS/NPP nightlights data may show the floor area ratio in an extent at level of administrative street.

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.

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