Hyperspectral image classification method combining fast guided filtering and spatial neighborhood information

AbstractTo solve the problem that the traditional hyperspectral image classification method cannot effectively distinguish the boundary of objects with a single scale feature, which leads to low classification accuracy, this paper introduces the idea of guided filtering into hyperspectral image classification, and then proposes a multi-scale guided feature extraction and classification (MGFEC) algorithm for hyperspectral images. Firstly, the principal component analysis theory is used to reduce the dimension of hyperspectral image data. Then, guided filtering algorithm is used to achieve multi-scale spatial structure extraction of hyperspectral image by setting different sizes of filtering windows, so as to retain more edge details. Finally, the extracted multi-scale features are input into the support vector machine classifier for classification. Several practical hyperspectral image datasets were used to verify the experiment, and compared with other spectral feature extraction algorithms. The experimental results show that the multi-scale features extracted by the MGFEC algorithm proposed in this paper are more accurate than those extracted by only using spectral information, which leads to the improvement of the final classification accuracy. This fully shows that the proposed method is not only effective, but also suitable for processing different hyperspectral image data.

Download Full-text

Hyperspectral Image Classification Based on Improved Rotation Forest Algorithm

Sensors ◽

10.3390/s18113601 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3601 ◽

Cited By ~ 3

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.

Download Full-text

R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2017.2655516 ◽

2017 ◽

Vol 10 (5) ◽

pp. 1975-1986 ◽

Cited By ~ 83

Author(s):

Bin Pan ◽

Zhenwei Shi ◽

Xia Xu

Keyword(s):

Deep Learning ◽

Image Classification ◽

Hyperspectral Image ◽

Classification Method ◽

Hyperspectral Image Classification

Download Full-text

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

Journal of Spectroscopy ◽

10.1155/2015/969185 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 5

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.

Download Full-text