Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking

Wang, Qi; Lin, Jianzhe; Yuan, Yuan

doi:10.1109/tnnls.2015.2477537

Cited by 447 publications

(150 citation statements)

References 44 publications

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“…Preliminary work on the impact of errors in the training data, and alternative methods to train a CNN to estimate sea ice concentration, can be found in [53]. Learning a sparse representation of the data could improve the ice concentration estimates when training sample quality and quantity are not sufficient [54].…”

Section: Discussionmentioning

confidence: 99%

Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network

2017

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Abstract:In this study, a convolutional neural network (CNN) is used to estimate sea ice concentration using synthetic aperture radar (SAR) scenes acquired during freeze-up in the Gulf of St. Lawrence on the east coast of Canada. The ice concentration estimates from the CNN are compared to those from a neural network (multi-layer perceptron or MLP) that uses hand-crafted features as input and a single layer of hidden nodes. The CNN is found to be less sensitive to pixel level details than the MLP and produces ice concentration that is less noisy and in closer agreement with that from image analysis charts. This is due to the multi-layer (deep) structure of the CNN, which enables abstract image features to be learned. The CNN ice concentration is also compared with ice concentration estimated from passive microwave brightness temperature data using the ARTIST sea ice (ASI) algorithm. The bias and RMS of the difference between the ice concentration from the CNN and that from image analysis charts is reduced as compared to that from either the MLP or ASI algorithm. Additional results demonstrate the impact of varying the input patch size, varying the number of CNN layers, and including the incidence angle as an additional input.

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Section: Discussionmentioning

confidence: 99%

Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network

2017

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“…Therein, the miniature VGG-M and full-sized VGG-16 are abbreviated as Orig.M and Orig. 16 More specifically, the network structures Orig.M, Orig.16, New Ext., Select Ext., and Select S-Ext. with the three penalization modes are compared and studied in Section 5.2 for a holistic comparison.…”

Section: The Baseline Network Structure and Extension Styles For Analmentioning

confidence: 99%

Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Zhu

et al. 2017

Remote Sensing

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Joint vehicle localization and categorization in high resolution aerial images can provide useful information for applications such as traffic flow structure analysis. To maintain sufficient features to recognize small-scaled vehicles, a regions with convolutional neural network features (R-CNN) -like detection structure is employed. In this setting, cascaded localization error can be averted by equally treating the negatives and differently typed positives as a multi-class classification task, but the problem of class-imbalance remains. To address this issue, a cost-effective network extension scheme is proposed. In it, the correlated convolution and connection costs during extension are reduced by feature map selection and bi-partite main-side network construction, which are realized with the assistance of a novel feature map class-importance measurement and a new class-imbalance sensitive main-side loss function. By using an image classification dataset established from a set of traditional real-colored aerial images with 0.13 m ground sampling distance which are taken from the height of 1000 m by an imaging system composed of non-metric cameras, the effectiveness of the proposed network extension is verified by comparing with its similarly shaped strong counter-parts. Experiments show an equivalent or better performance, while requiring the least parameter and memory overheads are required.

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“…Each vertex v i is associated with the spatial and spectral joint feature defined in Equation (6). The hypergraph G is constructed by the K-nearest neighbor method.…”

Section: Hypergraph Embeddingmentioning

confidence: 99%

“…[1][2][3]. Most of these applications depend on the key problem of classifying the image pixels within hyperspectral imagery (HSI) into multiple categories, i.e., HSI classification, and extensive research efforts have been focused on this problem [4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

et al. 2017

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Abstract:The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for improving the classification accuracy. However, this approach usually results in features of higher dimension and the curse of the dimensionality problem may arise resulting from the small ratio between the number of training samples and the dimensionality of features. To ease this problem, we propose a novel algorithm for spatial-spectral feature extraction based on hypergraph embedding. Firstly, each HSI pixel is regarded as a vertex and the joint of extended morphological profiles (EMP) and spectral features is adopted as the feature associated with the vertex. A hypergraph is then constructed by the K-Nearest-Neighbor method, in which each pixel and its most K relevant pixels are linked as one hyperedge to represent the complex relationships between HSI pixels. Secondly, the hypergraph embedding model is designed to learn a low dimensional feature with the reservation of geometric structure of HSI. An adaptive hyperedge weight estimation scheme is also introduced to preserve the prominent hyperedges by the regularization constraint on the weight. Finally, the learned low-dimensional features are fed to the support vector machine (SVM) for classification. The experimental results on three benchmark hyperspectral databases are presented. They highlight the importance of spatial-spectral joint features embedding for the accurate classification of HSI data. The weight estimation is better for further improving the classification accuracy. These experimental results verify the proposed method.

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Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking

Cited by 447 publications

References 44 publications

Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network

Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network

Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images

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