Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder

Ma, Xiaorui; Wang, Hongyu; Geng, Jie

doi:10.1109/jstars.2016.2517204

Cited by 235 publications

(96 citation statements)

References 49 publications

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“…To extract joint spectral-spatial features, a common idea is to flatten the spatial neighboring region into a 1-D vector, and then, the obtained spatial vector and the original spectral vector are stacked and fed into fully connected networks. For those works, readers can refer to [35], [37], [73], [74]. In [75]- [77], a new spectral vector was first computed by averaging all spectral pixels within a spatial neighboring.…”

Section: Spectral-spatial-feature Networkmentioning

confidence: 99%

Deep Learning for Hyperspectral Image Classification: An Overview

Li¹,

Song²,

Fang³

et al. 2019

IEEE Trans. Geosci. Remote Sensing

1,350

452

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Hyperspectral image (HSI) classification has become a hot topic in the field of remote sensing. In general, the complex characteristics of hyperspectral data make the accurate classification of such data challenging for traditional machine learning methods. In addition, hyperspectral imaging often deals with an inherently nonlinear relation between the captured spectral information and the corresponding materials. In recent years, deep learning has been recognized as a powerful feature-extraction tool to effectively address nonlinear problems and widely used in a number of image processing tasks. Motivated by those successful applications, deep learning has also been introduced to classify HSIs and demonstrated good performance. This survey paper presents a systematic review of deep learning-based HSI classification literatures and compares several strategies for this topic. Specifically, we first summarize the main challenges of HSI classification which cannot be effectively overcome by traditional machine learning methods, and also introduce the advantages of deep learning to handle these problems. Then, we build a framework which divides the corresponding works into spectralfeature networks, spatial-feature networks, and spectral-spatialfeature networks to systematically review the recent achievements in deep learning-based HSI classification. In addition, considering the fact that available training samples in the remote sensing field are usually very limited and training deep networks require a large number of samples, we include some strategies to improve classification performance, which can provide some guidelines for future studies on this topic. Finally, several representative deep learning-based classification methods are conducted on real HSIs in our experiments.

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Section: Spectral-spatial-feature Networkmentioning

confidence: 99%

Deep Learning for Hyperspectral Image Classification: An Overview

Li¹,

Song²,

Fang³

et al. 2019

IEEE Trans. Geosci. Remote Sensing

1,350

452

View full text Add to dashboard Cite

show abstract

“…In this paper, it was determined experimentally. Third, we compare the SLN with the recently-proposed deep learning-based methods [49,71]. The experimental results are reported in Table 11, where SSDCNN is the Spectral-Spatial Deep Convolutional Neural Network, Deep o is deep features with orthogonal matching pursuit and SSDL is Low Spatial Sampling Distance.…”

Section: Discussionmentioning

confidence: 99%

Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

Wei

Zhou

2017

Remote Sensing

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This paper presents a hierarchical deep framework called Spectral-Spatial Response (SSR) to jointly learn spectral and spatial features of Hyperspectral Images (HSIs) by iteratively abstracting neighboring regions. SSR forms a deep architecture and is able to learn discriminative spectral-spatial features of the input HSI at different scales. It includes several existing spectral-spatial-based methods as special scenarios within a single unified framework. Based on SSR, we further propose the Subspace Learning-based Networks (SLN) as an example of SSR for HSI classification. In SLN, the joint spectral and spatial features are learned using templates simply learned by Marginal Fisher Analysis (MFA) and Principal Component Analysis (PCA). An important contribution to the success of SLN is the exploitation of label information of training samples and the local spatial structure of HSI. Extensive experimental results on four challenging HSI datasets taken from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS) airborne sensors show the implementational simplicity of SLN and verify the superiority of SSR for HSI classification.

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“…The work [4] uses a standard stacked autoencoder for classification using logistic regression. In [5], some extra penalties are added to the basic stacked autoencoder formulation in order to incorporate contextual information.…”

Section: A Deep Learning In Hyperspectral Classificationmentioning

confidence: 99%

Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification

Singhal

Majumdar

2018

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent studies in hyperspectral imaging, biometrics and energy analytics, the framework of deep dictionary learning has shown promise. Deep dictionary learning outperforms other traditional deep learning tools when training data is limited; therefore hyperspectral imaging is one such example that benefits from this framework. Most of the prior studies were based on the unsupervised formulation; and in all cases, the training algorithm was greedy and hence sub-optimal. This is the first work that shows how to learn the deep dictionary learning problem in a joint fashion. Moreover, we propose a new discriminative penalty to the said framework. The third contribution of this work is showing how to incorporate stochastic regularization techniques into the deep dictionary learning framework. Experimental results on hyperspectral image classification shows that the proposed technique excels over all state-of-the-art deep and shallow (traditional) learning based methods published in recent times.

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Spectral–Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder

Cited by 235 publications

References 49 publications

Deep Learning for Hyperspectral Image Classification: An Overview

Deep Learning for Hyperspectral Image Classification: An Overview

Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

Row-Sparse Discriminative Deep Dictionary Learning for Hyperspectral Image Classification

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