KNN-Based Representation of Superpixels for Hyperspectral Image Classification

Tu, Bing; Wang, Jinping; Kang, Xudong; Zhang, Guoyun; Ou, Xianfeng; Guo, Lei

doi:10.1109/jstars.2018.2872969

Cited by 77 publications

(46 citation statements)

References 70 publications

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“…In Figures 7 and 8, the NPV and MCC the proposed random forest classifier values are near to 1 and showed higher performance compared to ANN [29], KNN [30] and decision tree [32]. In FPR, FNR, FRR and error rate, the proposed random In Figure 9(a), the performance measurements like specificity, sensitivity and accuracy is compared to random forest classifier, KNN, ANN and decision tree.…”

Section: Performance Analysismentioning

confidence: 99%

An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

Narasimhulu¹

2021

IET Image Processing

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In medical imaging, the automatic diagnosis of kidney carcinoma has become more difficult because it is not easy to detect by physicians. Pre-processing is the first identification method to enhance image quality, remove noise and unwanted components from the backdrop of the kidneys image. The pre-processing method is essential and significant for the proposed algorithm. The objective of this analysis is to recognize and classify kidney disturbances with an ultrasound scan by providing a number of substantial content description parameters. The ultrasound pictures are prepared to protect the interest pixels before extracting the feature. A series of quantitative features were synthesized of each images, the principal component analysis was conducted for minimizing the number of features to produce set of wavelet-based multi-scale features. Dragonfly algorithm (DFA) was executed in this method. In the proposed work, the design and training of a random decision forest classifier and selected features are implemented. The classification of e-health information using ideal characteristics is used by the RF classifier. The proposed technique is activated in MATLAB/simulink work site and the experimental results show that the peak accuracy of the proposed technique is 95.6% using GWO-FFBN techniques compared to other existing techniques. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Performance Analysismentioning

confidence: 99%

An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

Narasimhulu¹

2021

IET Image Processing

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“…Sellars et al used a combination of Gaussian kernel technique, Log-Euclidean distance of a covariance matrix and Euclidean spectral distance to construct a weight between two connected superpixels [41]. By using a domain transform recursive filtering and k nearest neighbor rule (KNN), Tu et al [47] gave a representation of the distance between superpixels. These work make an effective attempt for HSI classification at superpixel level.…”

Section: Superpixel-to-superpixel Similaritymentioning

confidence: 99%

“…Compared with the similarity between a pair of superpixels introduced in [40] (an affine hull model and the singular value decomposition), the similarity suggested above is easy to understand since only the sorting rule is used. Contrasted to similarity defined in [41,47], our method is simple to calculate, and has no use of parameters. These advantages of the proposal make it easier to be applied in the field of remote sensing.…”

Section: Superpixel-to-superpixel Similaritymentioning

confidence: 99%

A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

et al. 2020

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Although superpixel segmentation provides a powerful tool for hyperspectral image (HSI) classification, it is still a challenging problem to classify an HSI at superpixel level because of the characteristics of adaptive size and shape of superpixels. Furthermore, these characteristics of superpixels along with the appearance of noisy pixels makes it difficult to appropriately measure the similarity between two superpixels. Under the assumption that pixels within a superpixel belong to the same class with a high probability, this paper proposes a novel spectral–spatial HSI classification method at superpixel level (SSC-SL). Firstly, a simple linear iterative clustering (SLIC) algorithm is improved by introducing a new similarity and a ranking technique. The improved SLIC, specifically designed for HSI, can straightly segment HSI with arbitrary dimensionality into superpixels, without consulting principal component analysis beforehand. In addition, a superpixel-to-superpixel similarity is newly introduced. The defined similarity is independent of the shape of superpixel, and the influence of noisy pixels on the similarity is weakened. Finally, the classification task is accomplished by labeling each unlabeled superpixel according to the nearest labeled superpixel. In the proposed superpixel-level classification scheme, each superpixel is regarded as a sample. This obviously greatly reduces the data volume to be classified. The experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed spectral–spatial classification method over several comparative state-of-the-art classification approaches, in terms of classification accuracy.

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“…One is based on spectral information, while the other is based on spectral-spatial information jointly. Traditional machine learning classification algorithms for hyperspectral images, such as k-nearest neighbor [7], spectral angle mapping [8], and multinomial logistic regression [9] are basically the former one. In order to improve classification performance, later researches used support vector machine [10], principal component analysis (PCA) [11], and independent component analysis [12] to decrease the redundant informative features.…”

Section: Introductionmentioning

confidence: 99%

An Effective Classification Method for Hyperspectral Image With Very High Resolution Based on Encoder–Decoder Architecture

Zhang

Jiang

Liu

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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KNN-Based Representation of Superpixels for Hyperspectral Image Classification

Cited by 77 publications

References 70 publications

An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

An automatic feature selection and classification framework for analyzing ultrasound kidney images using dragonfly algorithm and random forest classifier

A Novel Spectral–Spatial Classification Method for Hyperspectral Image at Superpixel Level

An Effective Classification Method for Hyperspectral Image With Very High Resolution Based on Encoder–Decoder Architecture

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