Hyperspectral Image Analysis for Writer Identification using Deep Learning

Islam, Ammad Ul; Khan, Muhammad Jaleed; Khurshid, Khurram

doi:10.1109/dicta47822.2019.8945886

Cited by 17 publications

(11 citation statements)

References 31 publications

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“…Deep learning technologies are the ideal solutions for better classification of different types of images (satellite [4,5], drones, medical [6], facial [7], etc.). The classification of hyperspectral images by convolutional neural networks (CNN) has attracted the attention of researchers, by the perfect results obtained in recent years [8][9][10]. Thus, several factors can change the CNN-HSI results, either by the choice of parameters, the extraction method and the concatenation, or in the applied architecture [11,12].…”

Section: Introductionmentioning

confidence: 99%

Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks)

Hamouda

Bouhlel

2021

IET Image Processing

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Classification of Hyperspectral Satellite Images (HSI) is a very important technology for object detection and cartography. Several problems can be detected, which make classification difficult (large size of the images, fusion between the classes, small amount of samples, etc.). Recently, several Convolutional Neural Networks (CNN-HSI) have been proposed for the classification of hyperspectral images. In this article, an improvement to CNN-HSI is proposed, aiming to reduce the number of erroneous pixels during classification (due to the limited number of samples). Thus, an extra-convolution technique (ExCNN) is proposed, where we add layers of global convolutions on the classified images, outgoing from classical CNN. The addition of 1 to 10 layers, on three real hyperspectral images, is tested. The results obtained are compared with other similar methods of the state of art, and show the effectiveness of the proposed method. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks)

Hamouda

Bouhlel

2021

IET Image Processing

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“…Deep learning was used by researchers to identify writer using HSI. Spectral responses were extracted and input to the convolutional neural network (CNN).Different test train ratios were evaluated [6].…”

Section: Figure 2 Hsi Cube Representing Different Bandsmentioning

confidence: 99%

“…In [6], PCA is used for dimensionality reduction of HSDI and k-means clustering is applied. These clusters are eventually trained by multi-class SVM.…”

Section: Figure 2 Hsi Cube Representing Different Bandsmentioning

confidence: 99%

Ink Classification in Hyperspectral Images

Bilal¹,

Ahmad²,

Ali³

2020

Preprint

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Hyperspectral imaging provides vital information about the objects and elements present inside the image. That’s why they are very useful in satellite imagery as well as image forensics. Hyperspectral document analysis (HSDI) can be used for document authentication using ink analysis which can provide sufficient information about the composition and type of ink. In this project, we have implemented HSDI based ink classification technique using Principle Component Analysis for dimensionality reduction and K-means clustering for ink classification. This is unsupervised learning approach and it is very simple and efficient in order to classify limited number of bands. We have used this technique to classify 33 different bands of ink.

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“…In an image context, a 3D geographical information system (GIS) data plan for the WiMax network was integrated to optimize both the network performance and the investment costs, both of which are relevant to the required number of base stations and sectors [3]. In addition, soft computing plays an important role in GIS research [4][5][6][7]. One important aspect of implementing soft computing is the quality of the dataset.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition

Kamsing

Torteeka

Boonpook

et al. 2020

Applied Computational Intelligence and Soft Computing

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To enhance the performance of image classification and speech recognition, the optimizer is considered an important factor for achieving high accuracy. The state-of-the-art optimizer can perform to serve in applications that may not require very high accuracy, yet the demand for high-precision image classification and speech recognition is increasing. This study implements an adaptive method for applying the particle filter technique with a gradient descent optimizer to improve model learning performance. Using a pretrained model helps reduce the computational time to deploy an image classification model and uses a simple deep convolutional neural network for speech recognition. The applied method results in a higher speech recognition accuracy score—89.693% for the test dataset—than the conventional method, which reaches 89.325%. The applied method also performs well on the image classification task, reaching an accuracy of 89.860% on the test dataset, better than the conventional method, which has an accuracy of 89.644%. Despite a slight difference in accuracy, the applied optimizer performs well in this dataset overall.

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Hyperspectral Image Analysis for Writer Identification using Deep Learning

Cited by 17 publications

References 31 publications

Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks)

Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks)

Ink Classification in Hyperspectral Images

Deep Neural Learning Adaptive Sequential Monte Carlo for Automatic Image and Speech Recognition

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