Going Deeper With Contextual CNN for Hyperspectral Image Classification.

Going Deeper With Contextual CNN for Hyperspectral Image Classification

Hyungtae Lee, Heesung Kwon

<span title="">2017</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhlhr4jqkbcmdbua2ca45o7kru" style="color: black;">IEEE Transactions on Image Processing</a> </i>  

In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. ... Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly ... CONTEXTUAL DEEP CNN The Proposed CNN Network We propose a fully convolutional network (FCN) [6] with 9 convolutional layers for hyperspectral image classification, as shown in Fig. 1 . ...

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Hyperspectral imaging (HSI) contains abundant spectrums as well as spatial information, providing a great basis for classification in the field of remote sensing. ... To attain higher classification accuracy with deeper layers, residual blocks are also applied to the network. ... Abbreviations The following abbreviations are used in this manuscript: HSI Hyperspectral Image PCA Principle Component Analysis CNN Convolutional Neural Network ...

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First few layers of a CNN pretrained on a large-scale RGB dataset are capable of acquiring general image characteristics which are remarkably effective in tasks targeted for different RGB datasets. ... A pretrain-finetune strategy is widely used to reduce the overfitting that can occur when data is insufficient for CNN training. ... Wonkook Kim at Pusan National University for his help with the experiments. ...

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In this paper, we propose a method for remotely-sensed image classification by using sparse representation of deep learning features. ... In recent years, deep learning has been widely studied for remote sensing image analysis. ... Landgrebe for making the AVIRIS Indian Pines hyperspectral dataset available to the community and Paolo Gamba for providing the ROSIS data over Pavia, Italy, along with the training and test sets. ...

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Recently, deep learning methods, for example, convolutional neural networks (CNNs), have achieved high performance in hyperspectral image (HSI) classification. ... In this paper, we present a wide sliding window and subsampling network (WSWS Net) for HSI classification. It is based on layers of transform kernels with sliding windows and subsampling (WSWS). ... Going Deeper with a Fully Connected Layer In order to learn higher level spatial and spectral features, the WSWS Net is extended deeper with more WSWS layers. ...

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Index Terms-Convolutional network, deconvolutional network, hyperspectral image classification, residual learning, unsupervised spectral-spatial feature learning. ... In this paper, we propose a novel network architecture, fully Conv-Deconv network, for unsupervised spectral-spatial feature learning of hyperspectral images, which is able to be trained in an end-to-end ... Landgrebe from Purdue University for providing the Indian Pines data set. ...

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This paper presents principles and key features for airborne hyperspectral imaging, lidar, and the fusion of those, as well as applications of these for urban land cover classification. ... features critical for classification of urban surfaces, transferability, dimensionality, and computational expense. ... In [130, 131] , the authors also added the contextual features into hyperspectral image classification, including the information in the classification map generation step. ...

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In recent years, deep convolutional neural networks (CNNs) have shown impressive ability to represent hyperspectral images (HSIs) and achieved encouraging results in HSI classification. ... In addition, the conventional CNN models operate convolutions with local receptive fields, which cause failures in modeling contextual spatial information. ... In order to extract the spectral spatial features in hyperspectral data more effectively, Chen et al. combined the regularization on the basis of 3D-CNN for HSI classification (3D-CNN-LR) in [21] . ...

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Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art ... To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. ... Acknowledgments: We gratefully thank the Associate Editor and the five Anonymous Reviewers for their outstanding comments and suggestions, which greatly helped us to improve the technical quality and presentation ...

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Hyperspectral Imaging (HSI) has been extensively utilized in many real-life applications because it benefits from the detailed spectral information contained in each pixel. ... This prompts the deployment of DL for HSI classification (HSIC) which revealed good performance. ... ACKNOWLEDGMENT The authors thanks to Ganesan Narayanasamy who is leading IBM OpenPOWER/POWER enablement and ecosystem worldwide for his support to get the IBM AC922 system's access. ...

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coverage and weather independent images, which in turn provides faster turnaround times for creation of large area geospatial data. ... have started to capture many interest among surveyors, professionals and practitioners abroad, Malaysia however is still lacking behind in term of the knowledge and the usage of this technology together with ... ., (2014) adopt the AE for hyperspectral data classification methods and SAEs deep architecture. It is shown that AE extracted features are useful for classification. ...

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Mou et al. [16] fused a ResNet with an end-to-end conv-deconvnet to deal with hyperspectral image classification. ... Xu et al. [28] utilized a CNN to extract spatial and spectral information from hyperspectral remote sensing images and features from LIDAR or VIS data using cascade. ... Acknowledgments: The authors would like to thank the editors and anonymous reviewers for their valuable comments, which helped us improve this work. ...

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the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. ... Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for ... CNNs, being very successful in classifying complex contextual images, have been widely used to classify remote sensing data too. ...

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During recent years, convolutional neural network (CNN)-based methods have been widely applied to hyperspectral image (HSI) classification by mostly mining the spectral variabilities. ... On top of the new SPPF, we further propose a flexible multi-stream CNN-based classification framework that is compatible with multiple in-stream sub-network designs. ... Introduction Hyperspectral image (HSI) classification deals with the problem of pixel-wise labeling of the hyperspectral spectrum, which has historically been a heavily studied, but not yet perfectly solved ...

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For example, the use of deep CNNs for RS image classification was introduced in Romero, Gatta, and Camps-Valls (2016) with the network being trained by an unsupervised method that seeks sparse feature ... Figure 4 shows a typical DBN for deep feature learning from hyperspectral images. In DBN, the output of the preceding RBM is used as input data for the next RBM. ... CONFLICT OF INTEREST The authors have declared no conflicts of interest for this article. ...

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