Hyperspectral image classification with SVM and guided filter

Guo, Yanhui; Yin, Xijie; Zhao, Xuan; Yang, Disheng; Bai, Yu

doi:10.1186/s13638-019-1346-z

Cited by 62 publications

(28 citation statements)

References 27 publications

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“…In the case of hyperspectral image classification, in [73], a guided filter is incorporated into the SVM classifier. This originates from the fusion of spectral and spatial features with the help of the PCA method.…”

Section: Other Applications Of Svm-based Classifiersmentioning

confidence: 99%

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

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This paper gives an overview of the different research works related to electromyographic signals (EMG) classification based on Support Vector Machines (SVM). The article summarizes the techniques used to make the classification in each reference. Furthermore, it includes the obtained accuracy, the number of signals or channels used, the way the authors made the feature vector, and the type of kernels used. Hence, this article also includes a compilation about the bands used to filter signals, the number of signals recommended, the most commonly used sampling frequencies, and certain features that can create the characteristics of the vector. This research gathers articles related to different kinds of SVM-based classification and other tools for signal processing in the field.

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Section: Other Applications Of Svm-based Classifiersmentioning

confidence: 99%

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

et al. 2019

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“…Owing to the limited training samples, SVM is applicable for the classication of high-dimensional data. 31 Thus, we applied SVM classi¯er for the leukocyte classi¯cation. The objective function of SVM is described as where w is the weight vector, x i is the feature sample, b is the bias, y i is the label of the sample.…”

Section: Classi¯cationmentioning

confidence: 99%

Spatial-spectral identification of abnormal leukocytes based on microscopic hyperspectral imaging technology

Zhou

et al. 2020

J. Innov. Opt. Health Sci.

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Screening and diagnosing of abnormal Leukocytes are crucial for the diagnosis of immune diseases and Acute Lymphoblastic Leukemia (ALL). As the deterioration of abnormal leukocytes is mainly due to the changes in the chromatin distribution, which signi¯cantly a®ects the absorption and re°ection of light, the spectral feature is proved to be important for leukocytes classi¯cation and identi¯cation. This paper proposes an accurate identi¯cation method for healthy and abnormal leukocytes based on microscopic hyperspectral imaging (HSI) technology which combines the spectral information. The segmentation of nucleus and cytoplasm is obtained by the morphological watershed algorithm. Then, the spectral features are extracted and combined with the spatial features. Based on this, the support vector machine (SVM) is applied for classi¯cation of¯ve types of leukocytes and abnormal leukocytes. Compared with di®erent classi¯cation methods, the proposed method utilizes spectral features which highlight the differences between healthy leukocytes and abnormal leukocytes, improving the accuracy in the classi¯cation and identi¯cation of leukocytes. This paper only selects one subtype of ALL for test, and the proposed method can be applied for detection of other leukemia in the future.

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“…The kappa coefficient (which is a measure of an algorithm's effectiveness) is also evaluated, and results indicate that the proposed DCNN is atleast 10% better than the others in terms of kappa. While DCNN is found to be superior to SVM, the work done by Yanhui Guo, Xijie Yin, Xuechen Zhao , Dongxin Yang and Yu Bai in [2] uses SVM with a guided filter to improve the classification performance. The guided filter acts as a feature improvement algorithm, and helps in describing the images with better accuracy.…”

Section: Table 1 Datasets Used For Evaluationmentioning

confidence: 99%

“…Thereby improving the effectiveness of the algorithm. In their work [2], the researchers have compared the results with SVM, SVM-EPF, Co-SVM, Co-SVM-EPF, GF-SVM & the proposed GF-SVM-EPF. They found that the proposed GF-SVM-EPF outperforms the other algorithms by atleast 6%.…”

Section: Table 1 Datasets Used For Evaluationmentioning

confidence: 99%

“…Using the unsupervised manner classification accuracy is one of the most critical parameters that has been analyzed in various articles using training samples as these are aware that training samples are some of the most limited in the number of numbers for HSI classification. There is still some controversy due to the unsupervised classification, which is the main challenge that leads to a challenging and complicated high dimensional data observation that is suggested in a more magnificent combination of spectral information [2]. Image classification has been a topic of research for more than 2 decades now, it includes multiple levels of processing for the input image.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Statistical Perspective on Hyper Spectral Classification Systems for Accuracy Improvement

Gadhave,

Sedamkar

2020

IJEAT

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Classification on a hyperspectral imagery data is a multi-domain problem, it involves segmentation, followed by feature extraction (FE) & selection and finally classification. The vast majority of work in processing of hyperspectral imagery data is done in the field of image classification itself, due to the fact that most of the hyperspectral images are captured in order to evaluate the areas where a particular type of event is occurring, these events range from crop growth, forest covers and military applications. These systems use an algorithm for each of the given steps individually in order to evaluate the accuracy of the system under test. Thus, various algorithms have been proposed in order to evaluate the classification performance of hyperspectral systems. Due to so many algorithms in the field of research, there is a lot of confusion as to which approach should be selected for an effective system. Thus, we need to find approaches which have good accuracy. In order to find the best approaches for classification, researchers have to generally study a plethora of papers, so in this paper, we compare a set of algorithms used for hyperspectral image classification and compare their performance so that the researchers reading this text can analyses these algorithms and select the ones which are best suited for their particular application. Moreover, recommendations are also made in order to further improve the performance of these systems.

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Hyperspectral image classification with SVM and guided filter

Cited by 62 publications

References 27 publications

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Support Vector Machine-Based EMG Signal Classification Techniques: A Review

Spatial-spectral identification of abnormal leukocytes based on microscopic hyperspectral imaging technology

Statistical Perspective on Hyper Spectral Classification Systems for Accuracy Improvement

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