3-D Deep Learning Approach for Remote Sensing Image Classification

Hamida, Ahmed Ben; Benoît, Alexandre; Lambert, Patrick; Amar, Chokri Ben

doi:10.1109/tgrs.2018.2818945

Cited by 617 publications

(302 citation statements)

References 61 publications

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“…The proposed method has been trained and evaluated over PaviaC and PaviaU datasets. The performance is compared with recent state-of-the-art approaches [18,19] in terms of classification accuracy. We trained our model with different percentage of training data (4.4%, 5%, 9%, 15%).…”

Section: Resultsmentioning

confidence: 99%

“…It involves removal of unwanted, irrelevant bands from each pixel of the image. The existing approaches for hyperspectral image classification (HSIC) can be grouped into traditional [3-7, 16, 17] and deep learning-based methods [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Deep learning models have been much more effective in HSIC as compared to the traditional approaches.…”

Section: Introductionmentioning

confidence: 99%

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3D CNN with Localized Residual Connections for Hyperspectral Image Classification

Dwivedi

Mandal

Yadav

et al. 2020

Communications in Computer and Information Science

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In this paper we propose a novel 3D CNN network with localized residual connections for hyperspectral image classification. Our work chalks a comparative study with the existing methods employed for abstracting deeper features and propose a model which incorporates residual features from multiple stages in the network. The proposed architecture processes individual spatiospectral feature rich cubes from hyperspectral images through 3D convolutional layers. The residual connections result in improved performance due to assimilation of both low-level and high-level features. We conduct experiments over Pavia University and Pavia Center dataset for performance analysis. We compare our method with two recent state-of-the-art methods for hyperspectral image classification method. The proposed network outperforms the existing approaches by a good margin.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

3D CNN with Localized Residual Connections for Hyperspectral Image Classification

Dwivedi

Mandal

Yadav

et al. 2020

Communications in Computer and Information Science

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“…Chen et al [154] proposed to apply Gabor filtering as an unsupervised pretraining technique before CNN to help extract features, thus indirectly mitigating the CNN overfitting problem caused by the lack of labeled data. Hamida et al [155] recently developed 3D CNN architectures for the HSI classification. They showed that their 3D CNN models were able to achieve a better classification rate than the standard 2D CNN models.…”

Section: Earth Data Classificationmentioning

confidence: 99%

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Sun

Scanlon

2019

Environ. Res. Lett.

338

129

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Big Data and machine learning (ML) technologies have the potential to impact many facets of environment and water management (EWM). Big Data are information assets characterized by high volume, velocity, variety, and veracity. Fast advances in high-resolution remote sensing techniques, smart information and communication technologies, and social media have contributed to the proliferation of Big Data in many EWM fields, such as weather forecasting, disaster management, smart water and energy management systems, and remote sensing. Big Data brings about new opportunities for data-driven discovery in EWM, but it also requires new forms of information processing, storage, retrieval, as well as analytics. ML, a subdomain of artificial intelligence (AI), refers broadly to computer algorithms that can automatically learn from data. ML may help unlock the power of Big Data if properly integrated with data analytics. Recent breakthroughs in AI and computing infrastructure have led to the fast development of powerful deep learning (DL) algorithms that can extract hierarchical features from data, with better predictive performance and less human intervention. Collectively Big Data and ML techniques have shown great potential for data-driven decision making, scientific discovery, and process optimization. These technological advances may greatly benefit EWM, especially because (1) many EWM applications (e.g. early flood warning) require the capability to extract useful information from a large amount of data in autonomous manner and in real time, (2) EWM researches have become highly multidisciplinary, and handling the ever increasing data volume/types using the traditional workflow is simply not an option, and last but not least, (3) the current theoretical knowledge about many EWM processes is still incomplete, but which may now be complemented through data-driven discovery. A large number of applications on Big Data and ML have already appeared in the EWM literature in recent years. The purposes of this survey are to (1) examine the potential and benefits of data-driven research in EWM, (2) give a synopsis of key concepts and approaches in Big Data and ML, (3) provide a systematic review of current applications, and finally (4) discuss major issues and challenges, and recommend future research directions. EWM includes a broad range of research topics. Instead of attempting to survey each individual area, this review focuses on areas of nexus in EWM, with an emphasis on elucidating the potential benefits of increased data availability and predictive analytics to improving the EWM research.

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“…In remote sensing field, the use of deep learning is rapidly growing. A considerable number of works propose deep strategies for spatial and spectral feature learning [3,[19][20][21]. Vakalopoulou et al [3] propose an automated building detection framework from very high resolution remote sensing data based on deep convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, Makantasis et al [20] propose a deep learning based method that exploits a CNN to encode pixels' spectral and spatial information and constructs high-level features of hyperspectral data in an automated way. Furthermore, Hamida et al [21] design a lightweight CNN architecture to process spectral and spatial information of hyperspectral data, and provide a less costing solution while ensuring an accurate classification of the hyperspectral data. In theory, considering the subtle differences among categories in remote scene classification, we may attempt to form high-level representations for remote sensing images from CNN activations.…”

Section: Introductionmentioning

confidence: 99%

Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

et al. 2017

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Abstract:Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet) to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial "distance" of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral "distance" between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pre-trained deep CNNs to other remote sensing tasks.

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3-D Deep Learning Approach for Remote Sensing Image Classification

Cited by 617 publications

References 61 publications

3D CNN with Localized Residual Connections for Hyperspectral Image Classification

3D CNN with Localized Residual Connections for Hyperspectral Image Classification

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

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