A Study on the Recognition and Classification Method of High Resolution Remote Sensing Image Based on Deep Belief Network

2016 ◽  
pp. 362-370
Author(s):  
Guanyu Chen ◽  
Xiang Li ◽  
Ling Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Deep Belief Network ◽  
Classification Method ◽  
Belief Network

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%.

Analysis of the Remote Sensing Image Extraction of Building Height’s Method

2012 ◽  
Vol 256-259 ◽  
pp. 2293-2297 ◽  
Author(s):  
Jia Liang Li ◽  
You Po Su
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Classification Method ◽  
Topographic Map ◽  
Simple Process

In this paper, based on high resolution remote sensing image and the corresponding civil areas of digital topographic map, by using modules and effective classification method of ENVI software and ARCGIS software, by calculating the shadow of one building to obtain the height of it in city. Practice has proved that this method brings small workload, owns simple process, and can basically meet the needs of a city’s reference requirements of relevant disaster preventions and mitigations planning. It has practical value.

Road Centreline Extraction of High-Resolution Remote Sensing Image with Improved Beamlet Transform and K-Means Clustering

2021 ◽  
Vol 46 (4) ◽  
pp. 4153-4162
Author(s):  
Hancong Fu ◽  
Fan Deng ◽  
Yanlin Shao ◽  
Yuangang Liu ◽  
Jiahua Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Beamlet Transform

Cascaded panoptic segmentation method for high resolution remote sensing image

2021 ◽  
pp. 107515
Author(s):  
Xia Hua ◽  
Xinqing Wang ◽  
Ting Rui ◽  
Faming Shao ◽  
Dong Wang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Image ◽  
Segmentation Method
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