Tensor-Based Classification Models for Hyperspectral Data Analysis

Makantasis, Konstantinos; Doulamis, Anastasios; Nikitakis, Antonis

doi:10.1109/tgrs.2018.2845450

Cited by 112 publications

(75 citation statements)

References 33 publications

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“…Meanwhile, the analysis technics of hyperspectral imaging combined with new intelligent algorithms can be also applied to classification [8][9][10]. The spectra acquired from NIR sensor have the potential to extract corresponding feature information of samples.…”

Section: Introductionmentioning

confidence: 99%

Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors

Wang

Tian

Yang

et al. 2020

Sensors

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Near-infrared (NIR) spectral sensors can deliver the spectral response of light absorbed by materials. Data analysis technology based on NIR sensors has been a useful tool for quality identification. In this paper, an improved deep convolutional neural network (CNN) with batch normalization and MSRA (Microsoft Research Asia) initialization is proposed to discriminate the tobacco cultivation regions using data collected from NIR sensors. The network structure is created with six convolutional layers and three full connection layers, and the learning rate is controlled by exponential attenuation method. One-dimensional kernel is applied as the convolution kernel to extract features. Meanwhile, the methods of L2 regularization and dropout are used to avoid the overfitting problem, which improve the generalization ability of the network. Experimental results show that the proposed deep network structure can effectively extract the complex characteristics inside the spectrum, which proves that it has excellent recognition performance on tobacco cultivation region discrimination, and it also demonstrates that the deep CNN is more suitable for information mining and analysis of big data.

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Section: Introductionmentioning

confidence: 99%

Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors

Wang

Tian

Yang

et al. 2020

Sensors

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“…An improvement over conventional SVMs was observed, especially if the number of training samples is small. The problems related to small numbers of training samples can also be effectively addressed by tensor-based linear and non-linear models as proposed by Makantasis et al in [21].…”

Section: Introductionmentioning

confidence: 99%

Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

et al. 2019

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In this paper, we evaluate different popular voting strategies for fusion of classifier results. A convolutional neural network (CNN) and different variants of random forest (RF) classifiers were trained to discriminate between 15 tree species based on airborne hyperspectral imaging data. The spectral data was preprocessed with a multi-class linear discriminant analysis (MCLDA) as a means to reduce dimensionality and to obtain spatial-spectral features. The best individual classifier was a CNN with a classification accuracy of 0.73 +/− 0.086. The classification performance increased to an accuracy of 0.78 +/− 0.053 by using precision weighted voting for a hybrid ensemble of the CNN and two RF classifiers. This voting strategy clearly outperformed majority voting (0.74), accuracy weighted voting (0.75), and presidential voting (0.75).

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“…In addition, the total variation (TV) method [26] and extend morphological features (EMPs) [27] approach based on morphological analysis [28] are used to generate spatial information by describing the texture characteristics of the image, and to effectively improve the classification accuracy. In recent years, tensor learning methods [29] are developed in the area of hyperspectral image processing. In [30], Zhang et al proposed tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction to improve HSI classification accuracy.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation

Zhan

Sun

et al. 2018

Remote Sensing

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High dimensional image classification is a fundamental technique for information retrieval from hyperspectral remote sensing data. However, data quality is readily affected by the atmosphere and noise in the imaging process, which makes it difficult to achieve good classification performance. In this paper, multiple kernel learning-based low rank representation at superpixel level (Sp_MKL_LRR) is proposed to improve the classification accuracy for hyperspectral images. Superpixels are generated first from the hyperspectral image to reduce noise effect and form homogeneous regions. An optimal superpixel kernel parameter is then selected by the kernel matrix using a multiple kernel learning framework. Finally, a kernel low rank representation is applied to classify the hyperspectral image. The proposed method offers two advantages. (1) The global correlation constraint is exploited by the low rank representation, while the local neighborhood information is extracted as the superpixel kernel adaptively learns the high-dimensional manifold features of the samples in each class; (2) It can meet the challenges of multiscale feature learning and adaptive parameter determination in the conventional kernel methods. Experimental results on several hyperspectral image datasets demonstrate that the proposed method outperforms several state-of-the-art classifiers tested in terms of overall accuracy, average accuracy, and kappa statistic.

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Tensor-Based Classification Models for Hyperspectral Data Analysis

Cited by 112 publications

References 33 publications

Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors

Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors

Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers

Hyperspectral Classification via Superpixel Kernel Learning-Based Low Rank Representation

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