Hyperspectral image classification via contextual deep learning

Ma, Xiaorui; Geng, Jie; Wang, Hongyu

doi:10.1186/s13640-015-0071-8

Cited by 117 publications

(63 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As detailed in 5, the 3D architecture is capable of reaching a 99% accuracy rate while only using 9% of the image pixel to train the network. The state of the art method detailed in [38] also deploys 9% of the dataset for training. However, this approach under-performs the proposed 3D architecture with a 98% accuracy level for about 20,000 trained parameters against our 99% accuracy level for less than 7,000 trained parameters.…”

Section: Computational Cost and Training Data Requirementsmentioning

confidence: 99%

3-D Deep Learning Approach for Remote Sensing Image Classification

Hamida

Benoît

Lambert

et al. 2018

IEEE Trans. Geosci. Remote Sensing

618

240

View full text Add to dashboard Cite

Recently, a variety of approaches has been enriching the field of Remote Sensing (RS) image processing and analysis. Unfortunately, existing methods remain limited faced to the rich spatio-spectral content of today's large datasets. It would seem intriguing to resort to Deep Learning (DL) based approaches at this stage with regards to their ability to offer accurate semantic interpretation of the data. However, the specificity introduced by the coexistence of spectral and spatial content in the RS datasets widens the scope of the challenges presented to adapt DL methods to these contexts. Therefore, the aim of this paper is firstly to explore the performance of DL architectures for the RS hyperspectral dataset classification and secondly to introduce a new three-dimensional DL approach that enables a joint spectral and spatial information process. A set of three-dimensional schemes is proposed and evaluated. Experimental results based on well known hyperspectral datasets demonstrate that the proposed method is able to achieve a better classification rate than state of the art methods with lower computational costs.

show abstract

Section: Computational Cost and Training Data Requirementsmentioning

confidence: 99%

3-D Deep Learning Approach for Remote Sensing Image Classification

Hamida

Benoît

Lambert

et al. 2018

IEEE Trans. Geosci. Remote Sensing

618

240

View full text Add to dashboard Cite

show abstract

“…The aforementioned methods simply employ various manually-extracted spectral-spatial features to represent the pixels, which highly depends on experts' experience and is not general. In contrast, deep learning-based methods [21], [44], which can generate features automatically, have recently attracted increasing attention in hyperspectral image classification. The first attempt can be found in [20], where stacked autoencoder was utilized for high-level feature extraction.…”

Section: A Hyperspectral Image Classificationmentioning

confidence: 99%

“…Subsequently, Mou et al [18] first employed Recurrent Neural Network (RNN) for hyperspectral image classification. Besides, Ma et al [21] attempted to learn the spectral-spatial features via a deep learning architecture by fine-tuning the network via a supervised strategy. Recently, Convolutional Neural Network (CNN) has emerged as a powerful tool for hyperspectral image classification [22], [23], arXiv:1905.06133v1 [eess.IV] 14 May 2019 [24].…”

mentioning

confidence: 99%

Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Wan

Gong

Zhong

et al. 2020

IEEE Trans. Geosci. Remote Sensing

384

141

View full text Add to dashboard Cite

Convolutional Neural Network (CNN) has demonstrated impressive ability to represent hyperspectral images and to achieve promising results in hyperspectral image classification. However, traditional CNN models can only operate convolution on regular square image regions with fixed size and weights, so they cannot universally adapt to the distinct local regions with various object distributions and geometric appearances. Therefore, their classification performances are still to be improved, especially in class boundaries. To alleviate this shortcoming, we consider employing the recently proposed Graph Convolutional Network (GCN) for hyperspectral image classification, as it can conduct the convolution on arbitrarily structured non-Euclidean data and is applicable to the irregular image regions represented by graph topological information. Different from the commonly used GCN models which work on a fixed graph, we enable the graph to be dynamically updated along with the graph convolution process, so that these two steps can be benefited from each other to gradually produce the discriminative embedded features as well as a refined graph. Moreover, to comprehensively deploy the multi-scale information inherited by hyperspectral images, we establish multiple input graphs with different neighborhood scales to extensively exploit the diversified spectral-spatial correlations at multiple scales. Therefore, our method is termed 'Multi-scale Dynamic Graph Convolutional Network' (MDGCN). The experimental results on three typical benchmark datasets firmly demonstrate the superiority of the proposed MDGCN to other state-of-the-art methods in both qualitative and quantitative aspects.

show abstract

“…The learned features are expected to be more discriminative for classification. In fact, spatial adjacent pixels usually share similar spectral characteristics and have the same label, and using spatial information can reduce the uncertainty of samples and suppress the salt-and-pepper noise of classification results [57]. Furthermore, recent studies have found that a higher layer of the deep hierarchical model produces increasingly abstract representations and is increasingly invariant to some transformations [58,59].…”

Section: Spectral-spatial Responsementioning

confidence: 99%

Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

Wei

Zhou

2017

Remote Sensing

View full text Add to dashboard Cite

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.

show abstract

Hyperspectral image classification via contextual deep learning

Cited by 117 publications

References 33 publications

3-D Deep Learning Approach for Remote Sensing Image Classification

3-D Deep Learning Approach for Remote Sensing Image Classification

Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification

Spectral-Spatial Response for Hyperspectral Image Classiﬁcation

Contact Info

Product

Resources

About