High Performance Computing for Hyperspectral Remote Sensing

Du, Qian; Chang, Yang-Lang; King, Roger L.

doi:10.1109/jstars.2010.2095495

Cited by 201 publications

(97 citation statements)

References 50 publications

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“…The rich spectral information of HSI is powerful, and has been widely employed in a range of successful applications in agriculture [1], environmental sciences [2], wild-land fire tracking, and biological threat detection [3]. Classification of each pixel in HSI plays a crucial role in these applications.…”

Section: Introductionmentioning

confidence: 99%

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

2017

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Abstract:Recent research has shown that using spectral-spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral-spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral-spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods-namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods-on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.

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Section: Introductionmentioning

confidence: 99%

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

2017

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“…Among the many typical applications of HSIs are civil and biological threat detection [1], atmospheric environmental research [2], and ocean research [3], among others. The most commonly used technology in these applications is the classification of pixels in the HSI, referred to as HSI classification.…”

Section: Introductionmentioning

confidence: 99%

A Fast Dense Spectral–Spatial Convolution Network Framework for Hyperspectral Images Classification

et al. 2018

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Recent research shows that deep-learning-derived methods based on a deep convolutional neural network have high accuracy when applied to hyperspectral image (HSI) classification, but long training times. To reduce the training time and improve accuracy, in this paper we propose an end-to-end fast dense spectral-spatial convolution (FDSSC) framework for HSI classification. The FDSSC framework uses different convolutional kernel sizes to extract spectral and spatial features separately, and the "valid" convolution method to reduce the high dimensions. Densely-connected structures-the input of each convolution consisting of the output of all previous convolution layers-was used for deep learning of features, leading to extremely accurate classification. To increase speed and prevent overfitting, the FDSSC framework uses a dynamic learning rate, parametric rectified linear units, batch normalization, and dropout layers. These attributes enable the FDSSC framework to achieve accuracy within as few as 80 epochs. The experimental results show that with the Indian Pines, Kennedy Space Center, and University of Pavia datasets, the proposed FDSSC framework achieved state-of-the-art performance compared with existing deep-learning-based methods while significantly reducing the training time.

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“…There is a need to utilize the high speed numerical methods in order to approximate the inverse of large sparse matrices accurately. Conjugate Gradient (CG) method is the most distinguished iterative algorithm for solving sparse systems with too large size to be implemented by direct methods (Plaza and Chang 2007). To apply CG method on the linear equation = , A must be a symmetric, positive definite and sparse matrix.…”

Section: Speed-up Inversion Methodsmentioning

confidence: 99%

Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

Jamshidpour

Homayouni

Safari

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Hyperspectral image classification has been one of the most popular research areas in the remote sensing community in the past decades. However, there are still some problems that need specific attentions. For example, the lack of enough labeled samples and the high dimensionality problem are two most important issues which degrade the performance of supervised classification dramatically. The main idea of semi-supervised learning is to overcome these issues by the contribution of unlabeled samples, which are available in an enormous amount. In this paper, we propose a graph-based semi-supervised classification method, which uses both spectral and spatial information for hyperspectral image classification. More specifically, two graphs were designed and constructed in order to exploit the relationship among pixels in spectral and spatial spaces respectively. Then, the Laplacians of both graphs were merged to form a weighted joint graph. The experiments were carried out on two different benchmark hyperspectral data sets. The proposed method performed significantly better than the well-known supervised classification methods, such as SVM. The assessments consisted of both accuracy and homogeneity analyses of the produced classification maps. The proposed spectral-spatial SSL method considerably increased the classification accuracy when the labeled training data set is too scarce. When there were only five labeled samples for each class, the performance improved 5.92% and 10.76% compared to spatial graph-based SSL, for AVIRIS Indian Pine and Pavia University data sets respectively.

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High Performance Computing for Hyperspectral Remote Sensing

Cited by 201 publications

References 50 publications

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network

A Fast Dense Spectral–Spatial Convolution Network Framework for Hyperspectral Images Classification

Graph-Based Semi-Supervised Hyperspectral Image Classification Using Spatial Information

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