scholarly journals Remotely Sensed Image Classification by Complex Network Eigenvalue and Connected Degree

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Mengxi Xu ◽  
Chenglin Wei
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Complex Network ◽  
Clustering Algorithm ◽  
Remote Sensing Image ◽  
Remotely Sensed ◽  
Clustering Coefficient ◽  
Classification Method ◽  
Remotely Sensed Image ◽  
Remotely Sensed Images

It is a well-known problem of remotely sensed images classification due to its complexity. This paper proposes a remotely sensed image classification method based on weighted complex network clustering using the traditionalK-means clustering algorithm. First, the degree of complex network and clustering coefficient of weighted feature are used to extract the features of the remote sensing image. Then, the integrated features of remote sensing image are combined to be used as the basis of classification. Finally,K-means algorithm is used to classify the remotely sensed images. The advantage of the proposed classification method lies in obtaining better clustering centers. The experimental results show that the proposed method gives an increase of 8% in accuracy compared with the traditionalK-means algorithm and the Iterative Self-Organizing Data Analysis Technique (ISODATA) algorithm.

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%.

A Boundary Parallel-Like Index for High-Resolution Remotely Sensed Imagery Classification

2019 ◽  
Vol 33 (04) ◽  
pp. 1954014 ◽  
Author(s):  
Weiwei Jiang ◽  
Henglin Xiao ◽  
Zhan Zhao ◽  
Jianguo Zhou
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Classification ◽  
Remote Sensing Image ◽  
Remotely Sensed ◽  
Shape Features ◽  
Remote Sensing Imagery ◽  
Remotely Sensed Imagery ◽  
Remote Sensing Image Classification ◽  
Discriminating Ability

This paper proposes boundary parallel-like index (BPI) to describe shape features for high-resolution remote sensing image classification. Parallel-like boundary is found to be a discriminating clue which can reveal the shape regularity of segmented objects. Therefore, multi-orientation distance projections were constructed to measure and quantify parallel-like information. The discriminating ability was tested using original and segmented ground objects, respectively. The proposed BPI showed better discrimination for both original and segmented data than for other shape features, especially for buildings. This was also confirmed by the considerably higher accuracy of BPI in building classification experiments of high-resolution remote sensing imagery. It suggests the proposed BPI is useful for building related applications.

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