scholarly journals Hyperspectral Sea Ice Image Classification Based on the Spectral-Spatial-Joint Feature with the PCA Network

2021 ◽  
Vol 13 (12) ◽  
pp. 2253
Author(s):  
Yanling Han ◽  
Xi Shi ◽  
Shuhu Yang ◽  
Yun Zhang ◽  
Zhonghua Hong ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Sea Ice ◽  
Image Classification ◽  
Spatial Information ◽  
Gabor Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Small Sample ◽  
Bohai Bay ◽  
Hybrid Strategy

Sea ice is one of the most prominent causes of marine disasters occurring at high latitudes. The detection of sea ice is particularly important, and the classification of sea ice images is an important part of sea ice detection. Traditional sea ice classification based on optical remote sensing mostly uses spectral information only and does not fully extract rich spectral and spatial information from sea ice images. At the same time, it is difficult to obtain samples and the resulting small sample sizes used in sea ice classification has limited the improvement of classification accuracy to a certain extent. In response to the above problems, this paper proposes a hyperspectral sea ice image classification method involving spectral-spatial-joint features based on the principal component analysis (PCA) network. First, the method uses the gray-level co-occurrence matrix (GLCM) and Gabor filter to extract textural and spatial information about sea ice. Then, the optimal band combination is extracted with a band selection algorithm based on a hybrid strategy, and the information hidden in the sea ice image is deeply extracted through a fusion of spectral and spatial features. Then, the PCA network is designed based on principal component analysis filters in order to extract the depth features of sea ice more effectively, and hash binarization maps and block histograms are used to enhance the separation and reduce the dimensions of features. Finally, the low-level features in the data form more abstract and invariant high-level features for sea ice classification. In order to verify the effectiveness of the proposed method, we conducted experiments on two different data collection points in Bohai Bay and Baffin Bay. The experimental results show that, compared with other single feature and spectral-spatial-joint feature algorithms, the proposed method achieves better sea ice classification results (94.15% and 96.86%) by using fewer training samples and a shorter training time.

scholarly journals Hyperspectral image classification based on spectral-spatial kernel principal component analysis network

2020 ◽  
Vol 165 ◽  
pp. 03001
Author(s):  
Yanguo Fan ◽  
Shizhe Hou ◽  
Dingfeng Yu
Keyword(s):  
Principal Component Analysis ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Spectral Information ◽  
Original Structure ◽  
Hyperspectral Image Classification

Hyperspectral imagery contains both spectral information and spatial relationships among pixels. How to combine spatial information with spectral information effectively has always been a research hotspot of hyperspectral image classification. In this paper, a Spatial-Spectral Kernel Principal Component Analysis Network (SS-KPCANet) was proposed. The network is developed from the original structure of Principal Component Analysis Network. In which PCA is replaced by KPCA to extract more nonlinear features. In addition, the combination of spatial and spectral features also improves the performance of the network. At the end of the network, neighbourhood correction is added to further improve the classification accuracy. Experiments on three datasets show the effectiveness of the proposed method. Comparison with state-of-the-art deep learning-based methods indicate that the proposed method needs less training samples and has better performance.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

Efficient Feature Descriptor using Gabor Filter and Principal Component Analysis for Glaucoma Diagnosis

2021 ◽  
pp. 6787-6794
Author(s):  
Anisha Rebinth, Dr. S. Mohan Kumar
Keyword(s):  
Principal Component Analysis ◽  
Gabor Filter ◽  
Principal Component ◽  
Component Analysis ◽  
Disease Diagnosis ◽  
Image Features ◽  
Support Vector ◽  
Svm Classifier ◽  
Feature Descriptor ◽  
Glaucoma Diagnosis

An automated Computer Aided Diagnosis (CAD) system for glaucoma diagnosis using fundus images is developed. The various glaucoma image classification schemes using the supervised and unsupervised learning approaches are reviewed. The research paper involves three stages of glaucoma disease diagnosis. First, the pre-processing stage the texture features of the fundus image is recorded with a two-dimensional Gabor filter at various sizes and orientations. The image features are generated using higher order statistical characteristics, and then Principal Component Analysis (PCA) is used to select and reduce the dimension of the image features. For the performance study, the Gabor filter based features are extracted from the RIM-ONE and HRF database images, and then Support Vector Machine (SVM) classifier is used for classification. Final stage utilizes the SVM classifier with the Radial Basis Function (RBF) kernel learning technique for the efficient classification of glaucoma disease with accuracy 90%.

Video Copy Detection Based on Principal Component Analysis

2014 ◽  
Vol 548-549 ◽  
pp. 693-697
Author(s):  
Yan Yan Hou
Keyword(s):  
Principal Component Analysis ◽  
Spatial Information ◽  
False Positive Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Video Copy Detection ◽  
Copy Detection ◽  
Key Frame ◽  
Detection Algorithms ◽  
Positive Rate

Content-based video hashing was proposed for the purpose of video copy detection. Conventional video copy detection algorithms apply image hashing algorithm to either every frame or key frame which is sensitive to video variation. In our proposed algorithm, key frames including temporal and spatial information are used to video copy detection, Discrete cosine transform (DCT) is done for video key frame and feature vector is extracted by principal component analysis ( PCA ). An average true positive rate of 99.31% and false positive rate of 0.37% demonstrate the robustness and uniqueness of the proposed algorithm. Experiments indicate that it is easy to implement and more efficient than other video copy detection algorithms.

JPEG Steganalysis Based on Feature Fusion by Principal Component Analysis

2012 ◽  
Vol 263-266 ◽  
pp. 2933-2938 ◽  
Author(s):  
Feng Ying He ◽  
Shang Ping Zhong ◽  
Kai Zhi Chen
Keyword(s):  
Principal Component Analysis ◽  
Image Classification ◽  
Detection Rate ◽  
Feature Fusion ◽  
Principal Component ◽  
Component Analysis ◽  
Experimental Results ◽  
Jpeg Images

Aiming to the problems in the existing JPEG steganalysis schemes, such as high redundancy in features and failure to make good use of the complementarities among them, this study proposed a JPEG steganalysis approach based on feature fusion by the principal component analysis (PCA) and analysis of the complementarities among features. The study fused complementary features and isolated redundant components by PCA, and finally used RBaggSVM classifier for classification. Experimental results show that this scheme effectively improves the detection rate of steganalysis in JPEG images and achieves faster speed of image classification.

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