Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification

Salimi, Amir; Ziaii, Mansour; Amiri, Ali; Zadeh, Mahdieh Hosseinjani; Karimpouli, Sadegh; Moradkhani, Mostafa

doi:10.1016/j.ejrs.2017.02.003

Cited by 41 publications

(25 citation statements)

References 40 publications

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“…On the one hand, we suffer from limited ground-truth training samples—to deal with this, we can exploit various data augmentation techniques to increase the size of training sets (if they exist) [ 19 ]. On the other hand, the high dimensionality of such imagery, especially HSI, may easily lead to the curse of dimensionality and high levels of data redundancy [ 20 ]. To this end, the research community has been developing both band selection and feature extraction algorithms that are designed to effectively deal with HSIs [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Recent Advances in Multi- and Hyperspectral Image Analysis

Nalepa

2021

Sensors

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Current advancements in sensor technology bring new possibilities in multi- and hyperspectral imaging. Real-life use cases which can benefit from such imagery span across various domains, including precision agriculture, chemistry, biology, medicine, land cover applications, management of natural resources, detecting natural disasters, and more. To extract value from such highly dimensional data capturing up to hundreds of spectral bands in the electromagnetic spectrum, researchers have been developing a range of image processing and machine learning analysis pipelines to process these kind of data as efficiently as possible. To this end, multi- or hyperspectral analysis has bloomed and has become an exciting research area which can enable the faster adoption of this technology in practice, also when such algorithms are deployed in hardware-constrained and extreme execution environments; e.g., on-board imaging satellites.

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Section: Introductionmentioning

confidence: 99%

Recent Advances in Multi- and Hyperspectral Image Analysis

Nalepa

2021

Sensors

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“…A high-dimensional feature set poses a curse of dimensionality problem. This phenomenon deteriorates the model's performance through irrelevant features [20]. 3.…”

Section: Introductionmentioning

confidence: 99%

Automatic Personality Traits Perception Using Asymmetric Auto-Encoder

Zaferani¹,

Teshnehlab²,

Vali³

2021

IEEE Access

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On account of an increase in the human-computer interface applications, the study of automatic personality perception has become more and more prevalent than speech signal processing in recent years. These studies have shown that personality traits derived from psychology theories mainly affect acoustic features. However, some obstacles remain in the automatic personality perception classification, and the most important one is to extract the features related to each personality trait. Previous studies have shown that the personality effect differs from one acoustic feature to the others. Additionally, there are many features one can extract from speech signals. Curse of dimensionality in features also makes the classification difficult. This paper aimed to introduce and examine a novel and efficient automatic feature extraction method to classify the well-known big five personality traits. In this regard, three data augmentation methods for increasing data samples were examined. Afterwards, 6,373 statistical features were extracted from the nonverbal features of the SSPNet Speaker Personality Corpus. Finally, an innovative stacked asymmetric auto-encoder was utilized to extract useful features automatically to improve classification results. Compared with the conventional stacked auto-encoder and convolutional neural network, the proposed method exhibited an average improvement of 12.40%(10.14%) and 14.36%(1.42%) in terms of the unweighted average recall (accuracy), respectively. In comparison with other published works, classification results also revealed a notable average enhancement (11.78%) for unweighted average recall for all five traits and an average improvement of 5.1% for accuracy in two out of five personality traits.

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“…Currently, the methods for reducing the dimension of extracted spectral data from hyperspectral images mainly include feature extraction based on transformation (Du et al, 2018) [e.g., principal component analysis (PCA)] and feature selection based on non-transformation (Salimi et al, 2018;e.g., algorithms for selecting local feature bands). Peerbhay et al (2015) used hyperspectral remote sensing for the detection and mapping of Solanum mauritianum located within commercial forestry ecosystems.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth

Huang

Yang

et al. 2021

Front. Plant Sci.

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Mile-a-minute weed (Mikania micrantha Kunth) is considered as one of top 100 most dangerous invasive species in the world. A fast and accurate detection technology will be needed to identify M. micrantha. It will help to mitigate the extensive ecologic and economic damage on our ecosystems caused by this alien plant. Hyperspectral technology fulfills the above requirement. However, when working with hyperspectral images, preprocessing, dimension reduction, and classifier are fundamental to achieving reliable recognition accuracy and efficiency. The spectral data of M. micrantha were collected using hyperspectral imaging in the spectral range of 450–998 nm. A different combination of preprocessing methods, principal component analysis (for dimension reduction), and three classifiers were used to analyze the collected hyperspectral images. The results showed that a combination of Savitzky-Golay (SG) smoothing, principal component analysis (PCA), and random forest (RF) achieved an accuracy (A) of 88.71%, an average accuracy (AA) of 88.68%, and a Kappa of 0.7740 with an execution time of 9.647 ms. In contrast, the combination of SG, PCA and a support vector machine (SVM) resulted in a weaker performance in terms of A (84.68%), AA(84.66%), and Kappa (0.6934), but with less execution time (1.318 ms). According to the requirements for specific identification accuracy and time cost, SG-PCA-RF and SG-PCA-SVM might represent two promising methods for recognizing M. micrantha in the wild.

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Using a Feature Subset Selection method and Support Vector Machine to address curse of dimensionality and redundancy in Hyperion hyperspectral data classification

Cited by 41 publications

References 40 publications

Recent Advances in Multi- and Hyperspectral Image Analysis

Recent Advances in Multi- and Hyperspectral Image Analysis

Automatic Personality Traits Perception Using Asymmetric Auto-Encoder

Hyperspectral Imaging for Identification of an Invasive Plant Mikania micrantha Kunth

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