scholarly journals A minimum spanning forest based hyperspectral image classification method for cancerous tissue detection

2014 ◽  
Author(s):  
Robert Pike ◽  
Samuel K. Patton ◽  
Guolan Lu ◽  
Luma V. Halig ◽  
Dongsheng Wang ◽  
...  
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Classification Method ◽  
Hyperspectral Image Classification ◽  
Cancerous Tissue ◽  
Spanning Forest ◽  
Minimum Spanning Forest

scholarly journals Spectral-Spatial Hyperspectral Image Classification Based on Homogeneous Minimum Spanning Forest

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
F. Poorahangaryan ◽  
H. Ghassemian
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Space ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Spatial Classification ◽  
Spanning Forest ◽  
Minimum Spanning Forest ◽  
Initial Classification

The combination of spectral and spatial information is known as a suitable way to improve the accuracy of hyperspectral image classification. In this paper, we propose a spectral-spatial hyperspectral image classification approach composed of the following stages. Initially, the support vector machine (SVM) is applied to obtain the initial classification map. Then, we present a new index called the homogeneity order and, using that with K-nearest neighbors, we select some pixels in feature space. The extracted pixels are considered as markers for Minimum Spanning Forest (MSF) construction. The class assignment to the markers is done using the initial classification map results. In the final stage, MSF is applied to these markers, and a spectral-spatial classification map is obtained. Experiments performed on several real hyperspectral images demonstrate that the classification accuracies obtained by the proposed scheme are improved when compared to MSF-based spectral-spatial classification approaches.

Hyperspectral Image Classification Method Based on Adaptive Manifold Filtering

2018 ◽  
Vol 55 (4) ◽  
pp. 041010
Author(s):  
廖建尚 Liao Jianshang ◽  
王立国 Wang Liguo ◽  
郝思媛 Hao Siyuan
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Classification Method ◽  
Hyperspectral Image Classification

scholarly journals Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3601 ◽  
Author(s):  
Fei Lv ◽  
Min Han
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Image Data ◽  
Classification Method ◽  
Data Sets ◽  
Hyperspectral Image Classification ◽  
Rotation Forest ◽  
Public Data ◽  
Learning Machine ◽  
Q Statistic

Hyperspectral image classification is a hot issue in the field of remote sensing. It is possible to achieve high accuracy and strong generalization through a good classification method that is used to process image data. In this paper, an efficient hyperspectral image classification method based on improved Rotation Forest (ROF) is proposed. It is named ROF-KELM. Firstly, Non-negative matrix factorization( NMF) is used to do feature segmentation in order to get more effective data. Secondly, kernel extreme learning machine (KELM) is chosen as base classifier to improve the classification efficiency. The proposed method inherits the advantages of KELM and has an analytic solution to directly implement the multiclass classification. Then, Q-statistic is used to select base classifiers. Finally, the results are obtained by using the voting method. Three simulation examples, classification of AVIRIS image, ROSIS image and the UCI public data sets respectively, are conducted to demonstrate the effectiveness of the proposed method.

scholarly journals Improving Hyperspectral Image Classification Method for Fine Land Use Assessment Application Using Semisupervised Machine Learning

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Chunyang Wang ◽  
Zengzhang Guo ◽  
Shuangting Wang ◽  
Liping Wang ◽  
Chao Ma
Keyword(s):  
Land Use ◽  
Image Classification ◽  
Hyperspectral Image ◽  
Objective Assessment ◽  
Research Direction ◽  
Semisupervised Learning ◽  
Classification Method ◽  
Hyperspectral Image Classification ◽  
Test Analysis ◽  
Adjustment Policy

Study on land use/cover can reflect changing rules of population, economy, agricultural structure adjustment, policy, and traffic and provide better service for the regional economic development and urban evolution. The study on fine land use/cover assessment using hyperspectral image classification is a focal growing area in many fields. Semisupervised learning method which takes a large number of unlabeled samples and minority labeled samples, improving classification and predicting the accuracy effectively, has been a new research direction. In this paper, we proposed improving fine land use/cover assessment based on semisupervised hyperspectral classification method. The test analysis of study area showed that the advantages of semisupervised classification method could improve the high precision overall classification and objective assessment of land use/cover results.

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