Hyperspectral image classification by combining local binary pattern and joint sparse representation

Tu, Bing; Kuang, Wenlan; Zhao, Guangzhe; He, Danbing; Liao, Zhuolang; Ma, W.

doi:10.1080/01431161.2019.1633699

Cited by 16 publications

(7 citation statements)

References 38 publications

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“…To compensate make up for this deficiency, and to meet the requirements of a constant grey level and rotation, this paper uses a circular neighbourhood to replace the traditional square neighbourhood LBP algorithm [32]. The LBP algorithm has the advantages of simple calculation, efficient recognition, good texture feature display and low computational complexity [33,34].…”

Section: Handcrafted Texture Extraction Methodsmentioning

confidence: 99%

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Chen

et al. 2021

IEEE Access

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Lung cancer (LC) is one of the most serious cancers threatening human health. Histopathological examination is the gold standard for qualitative and clinical staging of lung tumors. However, the process for doctors to examine thousands of histopathological images is very cumbersome, especially for doctors with less experience. Therefore, objective pathological diagnosis results can effectively help doctors choose the most appropriate treatment mode, thereby improving the survival rate of patients. For the current problem of incomplete experimental subjects in the computer-aided diagnosis of lung cancer subtypes, this study included relatively rare lung adenosquamous carcinoma (ASC) samples for the first time, and proposed a computer-aided diagnosis method based on histopathological images of ASC, lung squamous cell carcinoma (LUSC) and small cell lung carcinoma (SCLC). Firstly, the multidimensional features of 121 LC histopathological images were extracted, and then the relevant features (Relief) algorithm was used for feature selection. The support vector machines (SVMs) classifier was used to classify LC subtypes, and the receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to make it more intuitive evaluate the generalization ability of the classifier. Finally, through a horizontal comparison with a variety of mainstream classification models, experiments show that the classification effect achieved by the Relief-SVM model is the best. The LUSC-ASC classification accuracy was 73.91%, the LUSC-SCLC classification accuracy was 83.91% and the ASC-SCLC classification accuracy was 73.67%. Our experimental results verify the potential of the auxiliary diagnosis model constructed by machine learning (ML) in the diagnosis of LC.

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Section: Handcrafted Texture Extraction Methodsmentioning

confidence: 99%

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Chen

et al. 2021

IEEE Access

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“…LBP is a parameterless texture descriptor. LBP has the advantages of being simple and effective and having a strong recognition ability and low computational complexity [16]- [18]. The gray value extracted by LBP is used to draw the gray statistics histogram, and the specific method is as shown in formula (1).…”

Section: ) Gray Statisticsmentioning

confidence: 99%

Classification of Cervical Biopsy Images Based on LASSO and EL-SVM

Huang

Zhang

et al. 2020

IEEE Access

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Cervical biopsy (biopsy) is an important part of the diagnosis of cervical cancer. The artificial classification of biopsy images in diagnosis is difficult and depends on the clinical experience of pathologists. However, the classification accuracy of computerized biopsy tissue images with similar lesions is low, and the problem of incomplete experimental objects needs to be addressed. This paper proposes a method of cervical biopsy tissue image classification based on least absolute shrinkage and selection operator (LASSO) and ensemble learning-support vector machine (EL-SVM). Using the LASSO algorithm for feature selection, the average optimization time was reduced by 35.87 seconds while ensuring the accuracy of the classification, and then serial fusion was performed. The EL-SVM classifier was used to identify and classify 468 biopsy tissue images, and the receiver operating characteristic (ROC) curve and error curve were used to evaluate the generalization ability of the classifier. Experiments show that the normalcervical cancer classification accuracy reached 99.64%, the normal-low-grade squamous intraepithelial lesion (LSIL) classification accuracy was 84.25%, the normal-high-grade squamous intraepithelial lesion (HSIL) classification accuracy was 87.40%, the LSIL-HSIL classification accuracy was 76.34%, the LSILcervical cancer classification accuracy was 91.88%, and the HSIL-cervical cancer classification accuracy was 81.54%.

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“…e resulting binary number is called LBP code. e results show that LBP algorithm has significant rotation invariance and gray invariance [46], and it can be effectively applied to HSI classification [47].…”

Section: Introductionmentioning

confidence: 98%

Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Chen

Chang

Shi

et al. 2021

Discrete Dynamics in Nature and Society

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Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.

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Hyperspectral image classification by combining local binary pattern and joint sparse representation

Cited by 16 publications

References 38 publications

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Classification of Cervical Biopsy Images Based on LASSO and EL-SVM

Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

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