Object-oriented information extraction and application in high-resolution remote sensing image

2005 ◽  
Author(s):  
Wenxia Wei ◽  
Xiuwan Chen ◽  
Ainai Ma
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Object Oriented ◽  
Remote Sensing Image

scholarly journals U-net Network for Building Information Extraction of Remote-Sensing Imagery

2018 ◽  
Vol 14 (12) ◽  
pp. 179
Author(s):  
Jingtan Li ◽  
Maolin Xu ◽  
Hongling Xiu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Information Extraction ◽  
Image Data ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Training Set ◽  
Building Information ◽  
The Face

With the resolution of remote sensing images is getting higher and higher, high-resolution remote sensing images are widely used in many areas. Among them, image information extraction is one of the basic applications of remote sensing images. In the face of massive high-resolution remote sensing image data, the traditional method of target recognition is difficult to cope with. Therefore, this paper proposes a remote sensing image extraction based on U-net network. Firstly, the U-net semantic segmentation network is used to train the training set, and the validation set is used to verify the training set at the same time, and finally the test set is used for testing. The experimental results show that U-net can be applied to the extraction of buildings.

scholarly journals Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule

2015 ◽  
Vol XL-7/W4 ◽  
pp. 75-80 ◽  
Author(s):  
C. K. Li ◽  
W. Fang ◽  
X. J. Dong
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Object Oriented ◽  
Remote Sensing Data ◽  
Image Data ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Multi Level ◽  
Image Object

With the development of remote sensing technology, the spatial resolution, spectral resolution and time resolution of remote sensing data is greatly improved. How to efficiently process and interpret the massive high resolution remote sensing image data for ground objects, which with spatial geometry and texture information, has become the focus and difficulty in the field of remote sensing research. An object oriented and rule of the classification method of remote sensing data has presents in this paper. Through the discovery and mining the rich knowledge of spectrum and spatial characteristics of high-resolution remote sensing image, establish a multi-level network image object segmentation and classification structure of remote sensing image to achieve accurate and fast ground targets classification and accuracy assessment. Based on worldview-2 image data in the Zangnan area as a study object, using the object-oriented image classification method and rules to verify the experiment which is combination of the mean variance method, the maximum area method and the accuracy comparison to analysis, selected three kinds of optimal segmentation scale and established a multi-level image object network hierarchy for image classification experiments. The results show that the objectoriented rules classification method to classify the high resolution images, enabling the high resolution image classification results similar to the visual interpretation of the results and has higher classification accuracy. The overall accuracy and Kappa coefficient of the object-oriented rules classification method were 97.38%, 0.9673; compared with object-oriented SVM method, respectively higher than 6.23%, 0.078; compared with object-oriented KNN method, respectively more than 7.96%, 0.0996. The extraction precision and user accuracy of the building compared with object-oriented SVM method, respectively higher than 18.39%, 3.98%, respectively better than the object-oriented KNN method 21.27%, 14.97%.

Study on Remote Sensing Image Auto-Identify Classification by Used of Object-Oriented Technology

2013 ◽  
Vol 444-445 ◽  
pp. 1244-1249
Author(s):  
Si Wen Xia ◽  
Shu Gan ◽  
Peng Fei Ren ◽  
Xiao Lun Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Statistical Characteristics ◽  
Object Oriented Technology ◽  
Image Characteristics ◽  
To Come ◽  
Set Up

In dealing with high-resolution remote sensing image auto-identify classification, the traditional pixel-based and spectral statistical characteristics classification technology or method has some insurmountable difficulties. In this paper, object-oriented image analysis method is by application, the auto-identify classification rules are set up based the different remote sensing image characteristics that included such as spectral, texture, scale and so on. As a case study, a petroleum reserve base auto-identify classification is selected as an example and the target is identified, in a better effective result by applications of the object-oriented method. The result appraising analysis indicates that object-oriented classification method to identify automatically high-resolution remote sensing images pattern object can get a high precision. The method of object-oriented has a widely potential application for remote sensing image automatic-identify classification in times to come.

Object-Oriented Information Extraction of Farmland Shelterbelts from Remote Sensing Image

2012 ◽  
Vol 500 ◽  
pp. 500-505
Author(s):  
Xiao Liang Shi ◽  
Ying Li ◽  
Rong Xin Deng
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
High Resolution ◽  
Object Oriented ◽  
Remote Sensing Image ◽  
Structure Characteristics ◽  
Lower Accuracy ◽  
Important Means ◽  
Spectrum Characteristics ◽  
Single Pixel

It has become an important means of shelterbelts surveying using high resolution remote sensing image to access the distribution of farmland shelterbelts. However, traditional classifications of remote sensing image based on spectrum characteristics of single pixel, and didn’t consider the factors including relativity and structure characteristics of the neighboring pixels, which will lead to lower accuracy of feature extraction for high resolution remote sensing image. On the basis of object-oriented classification method and the module of ENVI Feature Extraction, the paper extracted the shelterbelts distribution through image segmentation and rules establishment for the Spot5 high resolution remote sensing image in the Midwest of Jilin Province, and the extraction accuracy is 91.3%.The result shows that the method can accurately extract farmland shelterbelts from high resolution remote sensing image.

Object-oriented recognition of high-resolution remote sensing image

2016 ◽  
Author(s):  
Yongyan Wang ◽  
Haitao Li ◽  
Hong Chen ◽  
Yuannan Xu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Object Oriented ◽  
Remote Sensing Image
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