Novel detection method for infrared small targets using weighted information entropy

Qu, Xiujie; Chen, He; Peng, Guihua

doi:10.1109/jsee.2012.00102

Cited by 43 publications

(20 citation statements)

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“…It can be classified by randomly selecting the number of training samples with 10% or 5% labeled samples. A classifier model [50][51][52] is constructed that uses a random forest based on a CART decision tree with probabilistic output; it is a supervised classifier [53] that integrates multiple weak classifiers. The classifier predicts ground objects according to the number of votes cast.…”

Section: Advances In Multimediamentioning

confidence: 99%

“…The objects with larger weighted entropy values are sorted, and objects that account for 5% or 10% of the total sample increase are added to the training samples to form new training samples; then, the prediction and classification steps are carried out again. The above steps are repeated until the conditions for iteration stop are satisfied or the labeled samples are used up; the remaining samples are used for accuracy evaluation, which uses classifier performance detection [50,54].…”

Section: Advances In Multimediamentioning

confidence: 99%

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A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest

Sun

Wang

et al. 2018

Advances in Multimedia

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The purposes of the algorithm presented in this paper are to select features with the highest average separability by using the random forest method to distinguish categories that are easy to distinguish and to select the most divisible features from the most difficult categories using the weighted entropy algorithm. The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm; (4) precision evaluation; and (5) a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. In order to verify the universality of the proposed algorithm, two different data sources are tested, which are AVIRIS and Hyperion data. The results show that the overall classification accuracy of AVIRIS data is up to 87.36%, the kappa coefficient is up to 0.8591, and the classification time is 22.72s. Hyperion data is up to 99.17%, the kappa coefficient is up to 0.9904, and the classification time is 8.16s. Classification accuracy is obviously improved and efficiency is greatly improved, compared with the minimum distance and the SVM classifier and the CART classifier.

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Section: Advances In Multimediamentioning

confidence: 99%

Section: Advances In Multimediamentioning

confidence: 99%

A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest

Sun

Wang

et al. 2018

Advances in Multimedia

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show abstract

“…4a5-d5 are bright, and the parameter pairs used in Figs. 4a5-d5 is (3, 2), (15,2), (3,20) and (15,2), respectively, (refer to Lemmas 1 and 2 for the reason why the dark target becomes bright).…”

Section: Qualitative Analysesmentioning

confidence: 99%

“…In recent years, the local entropy operator has been applied to suppress backgrounds of small target images [10,15], since the emergence of the target damages the characteristics of image texture in the local regions. For an image, the entropy should be identified because its texture characteristic is definitive, whereas the appearance of a small target arouses the change of the value of local entropy operator.…”

Section: Introductionmentioning

confidence: 99%

Background suppression of small target image based on fast local reverse entropy operator

Wei

Tong

2013

IET Computer Vision

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Background suppression is vitally important for the small target detection, which aims to enhance targets and improve the signal-to-noise ratio of small target images. Consequently, the study proposes a background suppression approach based on the fast local reverse entropy operator, which is designed according to the fact that the appearance of a small target could result in the great change of the value of local reverse entropy in the local region. The operator is adopted to suppress complex backgrounds of small target images in order to enhance small targets, and then bring about high probabilities of detection and low probabilities of false alarm in the small target detection. Both quantitative and qualitative analyses contribute to confirm the validity and efficiency of the proposed approach.

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“…Detection algorithm based on entropy [14,15]: Entropy is an expression of capacity that signal contains information. The acuteness degree for area gray is reflected by area information entropy in an infrared image.…”

Section: Introductionmentioning

confidence: 99%

A new infrared small and dim target detection algorithm based on multi-directional composite window

Yang

Zhou

et al. 2015

Infrared Physics & Technology

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Novel detection method for infrared small targets using weighted information entropy

Cited by 43 publications

References 0 publications

A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest

A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest

Background suppression of small target image based on fast local reverse entropy operator

A new infrared small and dim target detection algorithm based on multi-directional composite window

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