3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Hu, Jie; He, Zhi; Li, Jun; He, Lin; Wang, Yiwen

doi:10.3390/rs10071070

Cited by 21 publications

(6 citation statements)

References 53 publications

(68 reference statements)

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“…Spatial-spectral multiview 3D Gabor inspired active learning for hyperspectral image classification method was proposed in [65]. Trivial multiview active learning methods can make a comprehensive analysis of both sample selection and object characterization in active learning by using several features of multiple views.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…However, multiview cannot effectively exploit spatial-spectral information by respecting the 3D nature of hyperspectral imaging, therefore, the sample selection method in multiview is only based on the disagreement of multiple views. To overcome such problems, J. Hu, et al [65] proposed a two-step 3D Gabor inspired multiview method for hyperspectral image classification. The first step consists of the view generation step, in which a 3D Gabor filter was used to generate multiple cubes with limited bands and utilize the features assessment strategies to select cubes for constructing views.…”

Section: Comparison and Discussionmentioning

confidence: 99%

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Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Ahmad

Khan

et al. 2019

Remote Sensing

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Acquisition of labeled data for supervised Hyperspectral Image (HSI) classification is expensive in terms of both time and costs. Moreover, manual selection and labeling are often subjective and tend to induce redundancy into the classifier. Active learning (AL) can be a suitable approach for HSI classification as it integrates data acquisition to the classifier design by ranking the unlabeled data to provide advice for the next query that has the highest training utility. However, multiclass AL techniques tend to include redundant samples into the classifier to some extent. This paper addresses such a problem by introducing an AL pipeline which preserves the most representative and spatially heterogeneous samples. The adopted strategy for sample selection utilizes fuzziness to assess the mapping between actual output and the approximated a-posteriori probabilities, computed by a marginal probability distribution based on discriminative random fields. The samples selected in each iteration are then provided to the spectral angle mapper-based objective function to reduce the inter-class redundancy. Experiments on five HSI benchmark datasets confirmed that the proposed Fuzziness and Spectral Angle Mapper (FSAM)-AL pipeline presents competitive results compared to the state-of-the-art sample selection techniques, leading to lower computational requirements.

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Comparison and Discussionmentioning

confidence: 99%

Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Ahmad

Khan

et al. 2019

Remote Sensing

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“…ic (11) with Equations (6)- (8), the second and third term in Equation 11are zero. So there are two terms in Equation (11). The result can be calculated by utilizing same methods in biasb…”

Section: Cv-cae Trainingmentioning

confidence: 99%

“…In addition, Gabor wavelet filtering is used to extract texture and edge information in different directions [10]. 3D-Gabor filter is employed to generate multiple cubes for active learning [11]. The second part designs classifier via the obtained features to achieve classification tasks, including hierarchical classifier [12], The rest of this paper is strctured as follows.…”

Section: Introductionmentioning

confidence: 99%

Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification

et al. 2019

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Recently, deep learning models, such as autoencoder, deep belief network and convolutional autoencoder (CAE), have been widely applied on polarimetric synthetic aperture radar (PolSAR) image classification task. These algorithms, however, only consider the amplitude information of the pixels in PolSAR images failing to obtain adequate discriminative features. In this work, a complex-valued convolutional autoencoder network (CV-CAE) is proposed. CV-CAE extends the encoding and decoding of CAE to complex domain so that the phase information can be adopted. Benefiting from the advantages of the CAE, CV-CAE extract features from a tiny number of training datasets. To further boost the performance, we propose a novel post processing method called spatial pixel-squares refinement (SPF) for preliminary classification map. Specifically, the majority voting and difference-value methods are utilized to determine whether the pixel-squares (PixS) needs to be refined or not. Based on the blocky structure of land cover of PolSAR images, SPF refines the PixS simultaneously. Therefore, it is more productive than current methods worked on pixel level. The proposed algorithm is measured on three typical PolSAR datasets, and better or comparable accuracy is obtained compared with other state-of-the-art methods.

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“…Recently, as an important branch of machine learning [19][20][21][22][23], deep learning has attracted much interest due to its strong capabilities in analysis and feature extraction [24,25]. By extracting features of the input data from the bottom to the top of the network, deep-learning models can form the high-level abstract features suitable for pattern classification [26].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Parameter-Optimized 3D-CNNs Combined with Transfer Learning and Virtual Samples

Sun

Yue

et al. 2018

Remote Sensing

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Recent research has shown that spatial-spectral information can help to improve the classification of hyperspectral images (HSIs). Therefore, three-dimensional convolutional neural networks (3D-CNNs) have been applied to HSI classification. However, a lack of HSI training samples restricts the performance of 3D-CNNs. To solve this problem and improve the classification, an improved method based on 3D-CNNs combined with parameter optimization, transfer learning, and virtual samples is proposed in this paper. Firstly, to optimize the network performance, the parameters of the 3D-CNN of the HSI to be classified (target data) are adjusted according to the single variable principle. Secondly, in order to relieve the problem caused by insufficient samples, the weights in the bottom layers of the parameter-optimized 3D-CNN of the target data can be transferred from another well trained 3D-CNN by a HSI (source data) with enough samples and the same feature space as the target data. Then, some virtual samples can be generated from the original samples of the target data to further alleviate the lack of HSI training samples. Finally, the parameter-optimized 3D-CNN with transfer learning can be trained by the training samples consisting of the virtual and the original samples. Experimental results on real-world hyperspectral satellite images have shown that the proposed method has great potential prospects in HSI classification.

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3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Cited by 21 publications

References 53 publications

Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images

Complex-Valued Convolutional Autoencoder and Spatial Pixel-Squares Refinement for Polarimetric SAR Image Classification

Hyperspectral Image Classification Based on Parameter-Optimized 3D-CNNs Combined with Transfer Learning and Virtual Samples

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