Supervised classification methods applied to airborne hyperspectral images: comparative study using mutual information

Nhaila, Hasna; Elmaizi, Asma; Sarhrouni, Elkebir; Hammouch, Ahmed

doi:10.1016/j.procs.2019.01.013

Cited by 6 publications

(6 citation statements)

References 16 publications

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“…In all cases, classification accuracies decreased when using the KNN instead of the SVM for the classification stage. This result confirms the fact that the classifier SVMs are less affected by the Hughes phenomenon especially when trained with mixed spectral-spatial data confirming the results obtained on our previous work [28]. Fig.…”

Section: Classification Results and Discussion 1) Classification Resu...supporting

confidence: 91%

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A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy

Elmaizi¹,

Nhaila²,

Sarhrouni³

et al. 2019

IJACSA

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During the last decade, hyperspectral images have attracted increasing interest from researchers worldwide. They provide more detailed information about an observed area and allow an accurate target detection and precise discrimination of objects compared to classical RGB and multispectral images. Despite the great potentialities of hyperspectral technology, the analysis and exploitation of the large volume data remain a challenging task. The existence of irrelevant redundant and noisy images decreases the classification accuracy. As a result, dimensionality reduction is a mandatory step in order to select a minimal and effective images subset. In this paper, a new filter approach normalized mutual synergy (NMS) is proposed in order to detect relevant bands that are complementary in the class prediction better than the original hyperspectral cube data. The algorithm consists of two steps: images selection through normalized synergy information and pixel classification. The proposed approach measures the discriminative power of the selected bands based on a combination of their maximal normalized synergic information, minimum redundancy and maximal mutual information with the ground truth. A comparative study using the support vector machine (SVM) and k-nearest neighbor (KNN) classifiers is conducted to evaluate the proposed approach compared to the state of art band selection methods. Experimental results on three benchmark hyperspectral images proposed by the NASA "Aviris Indiana Pine", "Salinas" and "Pavia University" demonstrated the robustness, effectiveness and the discriminative power of the proposed approach over the literature approaches.

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Section: Classification Results and Discussion 1) Classification Resu...supporting

confidence: 91%

“…The SVM and KNN algorithms choice is based on our previous comparison study [28] where both classifiers results showed their great performance for HSI classification compared to other classifiers.…”

Section: B Classifiers and Evaluation Metricsmentioning

confidence: 99%

A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy

Elmaizi¹,

Nhaila²,

Sarhrouni³

et al. 2019

IJACSA

Self Cite

View full text Add to dashboard Cite

show abstract

“…After observing the classification results, it was concluded that the SVM has the highest accuracy results followed by the RF classification which is considered slightly better than the K-NN. The achieved results were close to what was mentioned in [21] as the SVM, RF, and K-NN classifiers were achieving the best accuracies respectively, which is the same conclusion that was reached in this dataset. 2) Second dataset results: The second dataset classification results are completely different from the first.…”

Section: Resultssupporting

confidence: 88%

“…In [21], comparison research of four supervised classifiers has been offered using HSI of different datasets obtained using AVIRIS sensor. They used the following classification methods which are SVM with different kernel types, Random Forest RF, Discriminant Analysis (DA) with two different kernels, and K-NN.…”

Section: E Classification Using Svm Rf Da and K-nnmentioning

confidence: 99%

Automated Labeling of Hyperspectral Images for Oil Spills Classification

Said¹,

Hany²,

Magdy³

et al. 2021

IJACSA

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The constant increase in oil demand caused a huge loss in the form of oil spills during the process of exporting the product, which leads to an increase in pollution, especially in the marine environment. This research assists in providing a solution for this problem through modern technology by detecting oil spills using satellite imagery, more specifically hyperspectral images (HSI). The obtained dataset from the AVIRIS satellite is considered raw data, which leads to the availability of a vast amount of unlabeled data. This was one of the main reasons to propose a method to classify the HSI by automatically labeling the raw data first through unsupervised K-means clustering. The automatically labeled HSI is used to train various classifiers, that are Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (K-NN), to accomplish the optimal accuracy to be comparable with another research accuracy. In addition, the results of the first region of interest (ROI) indicate that the SVM with RBF kernel obtains 99.89% with principle component analysis (PCA) and 99.86% without the PCA, which revealed better accuracy than RF and the K-NN, while in the second ROI the RF obtained 99.9% with PCA and 99.91% without the PCA, better than K-NN and SVM. The region of interests selected lies within the Gulf of Mexico area. This area was selected based on the frequency of usage in previous research in detecting oil spills.

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“…Interest in supervised learning has been rekindled by the availability of big datasets and better computer capabilities. A variety of techniques, including support vector machines (SVM), random forests (RF), and DL algorithms, have been created and used by researchers in a variety of domains, including remote sensing [ 132 ], computer vision [ 133 ], image processing ([ [134] , [135] ]) natural language processing [ 136 ], health [ 137 ], andWQM. Bagging, k-nearest Neighbors (k-NN), Logistic Regression (LR), Naive Bayes, and Bayesian Networks are more examples of supervised learning algorithms.…”

Section: Resultsmentioning

confidence: 99%

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Essamlali,

Nhaila,

El Khaili

2024

Heliyon

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Supervised classification methods applied to airborne hyperspectral images: comparative study using mutual information

Cited by 6 publications

References 16 publications

A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy

A Novel Approach for Dimensionality Reduction and Classification of Hyperspectral Images based on Normalized Synergy

Automated Labeling of Hyperspectral Images for Oil Spills Classification

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

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