New framework for hyperspectral band selection using modified wind-driven optimization algorithm

Sawant, Shrutika S.; Manoharan, Prabukumar

doi:10.1080/01431161.2019.1607609

Cited by 30 publications

(16 citation statements)

References 33 publications

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“…The first layer is the convolutional layer in which the convolution process carried out according to (16). Here ⊗ denotes convolution operator, a filter is Ғ and i, j denotes the respective spatial location…”

Section: Proposed Methodsmentioning

confidence: 99%

“…denotes activation function. In the present work, the ReLU is used as it is better in training convergence [16]

σ ()(, x) = truemax ()(, 0, x)

The activation function in (17) gives the output as same as input or zero. Thus, calculation becomes easy.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The other popular and successful methods include BS as an optimisation problem inspired by nature [15], evolutionary methods, BS based on swarm optimisation [16], and methods like optimal neighbouring reconstruction (ONR) [17]. The overview of BS methods in HSI classification is described in [18].…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral remote sensing image classification using combinatorial optimisation based un‐supervised band selection and CNN

Vaddi

Manoharan

2020

IET Image Processing

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Hyperspectral image (HSI) consists of hundreds of contiguous spectral bands, which can be used in the classification of different objects on the earth. The inclusion of both spectral as well as spatial features stands essential in order that high classification accuracy is achieved. However, incorporation of the spectral and spatial information without preserving the intrinsic structure of the data leads on to downscaling the classification accuracy. To address the issue aforementioned, the proposed method which involves using unsupervised spectral band selection based on three major constrains: (i) low reconstruction error with neighbourhood bands, (ii) low noise, (iii) high information entropy, is put forward. In addition, the structure-preserving recursive filter is used to extract spatial features. Finally, the classification is performed using convolutional neural networks (CNNs) with different sets of convolutional, pooling, and fully connected layers. To test the performance of the proposed method, experiments have been carried out with three benchmark HSI datasets Indian pines, University of Pavia, and Salinas. These experiments reveal that the proposed method offers better classification accuracy over the purportedly state-of-the-art methods in terms of standard metrics like overall accuracy, average accuracy, and kappa coefficient (K). The proposed method has attained OAs of 99.9, 98.9, and 99.93% for the three datasets, respectively.

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Section: Proposed Methodsmentioning

confidence: 99%

“…denotes activation function. In the present work, the ReLU is used as it is better in training convergence [16]

σ ()(, x) = truemax ()(, 0, x)

The activation function in (17) gives the output as same as input or zero. Thus, calculation becomes easy.…”

Section: Proposed Methodsmentioning

confidence: 99%

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Hyperspectral remote sensing image classification using combinatorial optimisation based un‐supervised band selection and CNN

Vaddi

Manoharan

2020

IET Image Processing

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“…Unfortunately, these two GTs have been widely used to assess the performance of classification methods. [31][32][33][34][35][36] As an indication, as of the end of 2018, we identified almost 300 scientific published papers using one or the other of these two GTs and more than 40 using both.…”

Section: Impacts Of Biased Ground Truth In Classificationmentioning

confidence: 99%

Learning or assessment of classification algorithms relying on biased ground truth data: what interest?

Chehdi

Cariou

2019

J. Appl. Rem. Sens.

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The use of ground truth (GT) data in the learning and/or assessment of classification algorithms is essential. Using a biased or simplified GT attached to a remote sensing image to partition does not allow a rigorous explanation of the physical phenomena reflected by such images. Unfortunately, this scientific problem is not always treated carefully and is generally neglected in the relevant literature. Furthermore, the impacts of obtained classification results for decision-making are negative. This is inconsistent when considering investments in both the development of sophisticated sensors and the design of objective classification algorithms. Any GT must be validated according to a rigorous protocol before utilization, which is unfortunately not always the case. The evidence of this problem is provided, using two popular hyperspectral images (Indian Pine and Pavia University) that misleadingly are frequently used without care by the remote sensing community since the associated GTs are not accurate. The heterogeneity of the spectral signatures of some GT classes was proven using a semisupervised and an unsupervised classification method. Through this critical analysis, we propose a general framework for a minimum objective assessment and validation of the GT accuracy, before exploiting them in a classification method. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…A band selection algorithm with information theory can remove redundant bands while selecting information-rich bands through evolutionary algorithms [21]. Sawant et al [22,23] proposed a modified wind-driven optimization algorithm and cuckoo search algorithm to find the best band, avoiding premature convergence. Similarly, Yu et al [24] proposed a quantum evolutionary algorithm for band selection.…”

Section: Introductionmentioning

confidence: 99%

Three-Stages Hyperspectral Image Compression Sensing with Band Selection

Zhang¹,

Zhang²,

Cai³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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Compressed sensing (CS), as an efficient data transmission method, has achieved great success in the field of data transmission such as image, video and text. It can robustly recover signals from fewer Measurements, effectively alleviating the bandwidth pressure during data transmission. However, CS has many shortcomings in the transmission of hyperspectral image (HSI) data. This work aims to consider the application of CS in the transmission of hyperspectral image (HSI) data, and provides a feasible research scheme for CS of HSI data. HSI has rich spectral information and spatial information in bands, which can reflect the physical properties of the target. Most of the hyperspectral image compressed sensing (HSICS) algorithms cannot effectively use the inter-band information of HSI, resulting in poor reconstruction effects. In this paper, A three-stage hyperspectral image compression sensing algorithm (Three-stages HSICS) is proposed to obtain intra-band and inter-band characteristics of HSI, which can improve the reconstruction accuracy of HSI. Here, we establish a multi-objective band selection (Mop-BS) model, a multi-hypothesis prediction (MHP) model and a residual sparse (ReWSR) model for HSI, and use a staged reconstruction method to restore the compressed HSI. The simulation results show that the three-stage HSICS successfully improves the reconstruction accuracy of HSICS, and it performs best among all comparison algorithms.

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New framework for hyperspectral band selection using modified wind-driven optimization algorithm

Cited by 30 publications

References 33 publications

Hyperspectral remote sensing image classification using combinatorial optimisation based un‐supervised band selection and CNN

Hyperspectral remote sensing image classification using combinatorial optimisation based un‐supervised band selection and CNN

Learning or assessment of classification algorithms relying on biased ground truth data: what interest?

Three-Stages Hyperspectral Image Compression Sensing with Band Selection

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