A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification

• URL: http://arxiv.org/abs/2210.14621v1
• Date: Wed, 26 Oct 2022 10:54:23 GMT
• Title: A new band selection approach based on information theory and support vector machine for hyperspectral images reduction and classification
• Authors: A. Elmaizi, E. Sarhrouni, A. Hammouch, C. Nacir
• Abstract summary: spectral band selection is an essential step for removing the irrelevant, noisy and redundant bands. We propose a new strategy based on joint mutual information to measure the statistical dependence and correlation between the selected bands. The proposed filter approach is compared to an effective reproduced filters based on mutual information.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The high dimensionality of hyperspectral images consisting of several bands often imposes a big computational challenge for image processing. Therefore, spectral band selection is an essential step for removing the irrelevant, noisy and redundant bands. Consequently increasing the classification accuracy. However, identification of useful bands from hundreds or even thousands of related bands is a nontrivial task. This paper aims at identifying a small set of highly discriminative bands, for improving computational speed and prediction accuracy. Hence, we proposed a new strategy based on joint mutual information to measure the statistical dependence and correlation between the selected bands and evaluate the relative utility of each one to classification. The proposed filter approach is compared to an effective reproduced filters based on mutual information. Simulations results on the hyperpectral image HSI AVIRIS 92AV3C using the SVM classifier have shown that the effective proposed algorithm outperforms the reproduced filters strategy performance. Keywords-Hyperspectral images, Classification, band Selection, Joint Mutual Information, dimensionality reduction ,correlation, SVM.

Related papers

• MB-RACS: Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network [65.1004435124796]
  We propose a Measurement-Bounds-based Rate-Adaptive Image Compressed Sensing Network (MB-RACS) framework. Our experiments demonstrate that the proposed MB-RACS method surpasses current leading methods.
  arXiv  Detail & Related papers  (2024-01-19T04:40:20Z)
• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• Hybridization of filter and wrapper approaches for the dimensionality reduction and classification of hyperspectral images [0.0]
  We have proposed a hybrid algorithm through band selection for dimensionality reduction of hyperspectral images. The proposed approach is compared to an effective reproduced filters approach based on mutual information.
  arXiv  Detail & Related papers  (2022-10-29T05:25:10Z)
• A Novel Filter Approach for Band Selection and Classification of Hyperspectral Remotely Sensed Images Using Normalized Mutual Information and Support Vector Machines [0.0]
  This paper introduces a new filter approach for dimension reduction and classification of hyperspectral images using information theoretic (normalized mutual information) and support vector machines SVM. We applied our proposed algorithm on two well-known benchmark datasets gathered by the NASA's AVIRIS sensor over Indiana and Salinas valley in USA.
  arXiv  Detail & Related papers  (2022-10-27T14:23:06Z)
• A novel information gain-based approach for classification and dimensionality reduction of hyperspectral images [0.0]
  We propose a new filter approach based on information gain for dimensionality reduction and classification of hyperspectral images. A special strategy based on hyperspectral bands selection is adopted to pick the most informative bands and discard the irrelevant and noisy ones. The proposed method is compared using two benchmark hyperspectral datasets (Indiana, Pavia) with three competing methods.
  arXiv  Detail & Related papers  (2022-10-26T20:59:57Z)
• A novel filter based on three variables mutual information for dimensionality reduction and classification of hyperspectral images [0.0]
  Band selection filter based on "Mutual Information" is a common technique for dimensionality reduction. A new filter approach based on three variables mutual information is developed in order to measure band correlation for classification. The proposed approach is very competitive, effective and outperforms the reproduced filter strategy performance.
  arXiv  Detail & Related papers  (2022-10-26T10:29:00Z)
• New wrapper method based on normalized mutual information for dimension reduction and classification of hyperspectral images [0.0]
  We propose a new wrapper method based on normalized mutual information (NMI) and error probability (PE) Experiments have been performed on two challenging hyperspectral benchmarks datasets captured by the NASA's Airborne Visible/Infrared Imaging Spectrometer Sensor (AVIRIS)
  arXiv  Detail & Related papers  (2022-10-25T21:17:11Z)
• Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification [74.84221280249876]
  An efficient analysis of large amounts of chest radiographs can aid physicians and radiologists. We propose a novel Discrete Wavelet Transform (DWT)-based method for the efficient identification and encoding of visual information.
  arXiv  Detail & Related papers  (2022-05-08T15:29:54Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• ADRN: Attention-based Deep Residual Network for Hyperspectral Image Denoising [52.01041506447195]
  We propose an attention-based deep residual network to learn a mapping from noisy HSI to the clean one. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
  arXiv  Detail & Related papers  (2020-03-04T08:36:27Z)
• Residual-Sparse Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet frames [146.63177174491082]
  Fuzzy $C$-Means (FCM) algorithm incorporates a morphological reconstruction operation and a tight wavelet frame transform. We present an improved FCM algorithm by imposing an $ell_0$ regularization term on the residual between the feature set and its ideal value. Experimental results reported for synthetic, medical, and color images show that the proposed algorithm is effective and efficient, and outperforms other algorithms.
  arXiv  Detail & Related papers  (2020-02-14T10:00:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.