Improvement of Anomaly Detection Algorithms in Hyperspectral Images Using Discrete Wavelet Transform

The use of hyperspectral sensors has gained relevance in agriculture due to its potential in the phytosanitary management of crops. However, these sensors are sensitive to spectral noise, which makes their real application difficult. Therefore, this work focused on the analysis of the spectral noise present in a bank of 180 hyperspectral images of mango leaves acquired in the laboratory, and the implementation of a denoising technique based on the discrete wavelet transform. The noise analysis consisted in the identification of the highest noisy bands, while the performance of the technique was based on the PSNR and SNR metrics. As a result, it was determined that the spectral noise was present at the ends of the spectrum (417-421nm and 969-994nm) and that the Neigh-Shrink method achieved a SNR of the order of 1011 with respect to the order of 102 of the original spectrum.

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Anomaly detection of network traffic based on Analytical Discrete Wavelet Transform

2010 8th International Conference on Communications ◽

10.1109/iccomm.2010.5509071 ◽

2010 ◽

Cited By ~ 8

Author(s):

Marius Salagean ◽

Ioana Firoiu

Keyword(s):

Wavelet Transform ◽

Anomaly Detection ◽

Discrete Wavelet Transform ◽

Network Traffic ◽

Discrete Wavelet

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Energy Detection Based on Undecimated Discrete Wavelet Transform and Its Application in Magnetic Anomaly Detection

PLoS ONE ◽

10.1371/journal.pone.0110829 ◽

2014 ◽

Vol 9 (10) ◽

pp. e110829 ◽

Cited By ~ 4

Author(s):

Xinhua Nie ◽

Zhongming Pan ◽

Dasha Zhang ◽

Han Zhou ◽

Min Chen ◽

...

Keyword(s):

Wavelet Transform ◽

Anomaly Detection ◽

Discrete Wavelet Transform ◽

Magnetic Anomaly ◽

Energy Detection ◽

Discrete Wavelet

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Discrete Wavelet Transform and One-Class Support Vector Machines for anomaly detection in wireless sensor networks

2011 International Symposium on Intelligent Signal Processing and Communications Systems (ISPACS) ◽

10.1109/ispacs.2011.6146181 ◽

2011 ◽

Cited By ~ 4

Author(s):

Saowaluk Takianngam ◽

Wipawee Usaha

Keyword(s):

Wireless Sensor Networks ◽

Wavelet Transform ◽

Sensor Networks ◽

Support Vector Machines ◽

Anomaly Detection ◽

Discrete Wavelet Transform ◽

Wireless Sensor ◽

Support Vector ◽

Discrete Wavelet ◽

Vector Machines

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Real network traffic anomaly detection based on Analytical Discrete Wavelet Transform

2010 12th International Conference on Optimization of Electrical and Electronic Equipment ◽

10.1109/optim.2010.5510445 ◽

2010 ◽

Cited By ~ 6

Author(s):

Marius Salagean

Keyword(s):

Wavelet Transform ◽

Anomaly Detection ◽

Discrete Wavelet Transform ◽

Network Traffic ◽

Discrete Wavelet ◽

Real Network ◽

Traffic Anomaly ◽

Traffic Anomaly Detection

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Robust Classification Technique for Hyperspectral Images Based on 3D-Discrete Wavelet Transform

Remote Sensing ◽

10.3390/rs13071255 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1255

Author(s):

R Anand ◽

S Veni ◽

J Aravinth

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Performance Metrics ◽

Hyperspectral Image ◽

Research Work ◽

Three Dimensional ◽

Hyperspectral Images ◽

Support Vector ◽

Discrete Wavelet ◽

3D Dwt

Hyperspectral image classification is an emerging and interesting research area that has attracted several researchers to contribute to this field. Hyperspectral images have multiple narrow bands for a single image that enable the development of algorithms to extract diverse features. Three-dimensional discrete wavelet transform (3D-DWT) has the advantage of extracting the spatial and spectral information simultaneously. Decomposing an image into a set of spatial–spectral components is an important characteristic of 3D-DWT. It has motivated us to perform the proposed research work. The novelty of this work is to bring out the features of 3D-DWT applicable to hyperspectral images classification using Haar, Fejér-Korovkin and Coiflet filters. Three-dimensional-DWT is implemented with the help of three stages of 1D-DWT. The first two stages of 3D-DWT are extracting spatial resolution, and the third stage is extracting the spectral content. In this work, the 3D-DWT features are extracted and fed to the following classifiers (i) random forest (ii) K-nearest neighbor (KNN) and (iii) support vector machine (SVM). Exploiting both spectral and spatial features help the classifiers to provide a better classification accuracy. A comparison of results was performed with the same classifiers without DWT features. The experiments were performed using Salinas Scene and Indian Pines hyperspectral datasets. From the experiments, it has been observed that the SVM with 3D-DWT features performs better in terms of the performance metrics such as overall accuracy, average accuracy and kappa coefficient. It has shown significant improvement compared to the state of art techniques. The overall accuracy of 3D-DWT+SVM is 88.3%, which is 14.5% larger than that of traditional SVM (77.1%) for the Indian Pines dataset. The classification map of 3D-DWT + SVM is more closely related to the ground truth map.

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