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  • 1
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2017-10, Vol.55 (10), p.5653-5665
    Description: Scene classification from remote sensing images provides new possibilities for potential application of high spatial resolution imagery. How to efficiently implement scene recognition from high spatial resolution imagery remains a significant challenge in the remote sensing domain. Recently, convolutional neural networks (CNN) have attracted tremendous attention because of their excellent performance in different fields. However, most works focus on fully training a new deep CNN model for the target problems without considering the limited data and time-consuming issues. To alleviate the aforementioned drawbacks, some works have attempted to use the pretrained CNN models as feature extractors to build a feature representation of scene images for classification and achieved successful applications including remote sensing scene classification. However, existing works pay little attention to exploring the benefits of multilayer features for improving the scene classification in different aspects. As a matter of fact, the information hidden in different layers has great potential for improving feature discrimination capacity. Therefore, this paper presents a fusion strategy for integrating multilayer features of a pretrained CNN model for scene classification. Specifically, the pretrained CNN model is used as a feature extractor to extract deep features of different convolutional and fully connected layers; then, a multiscale improved Fisher kernel coding method is proposed to build a mid-level feature representation of convolutional deep features. Finally, the mid-level features extracted from convolutional layers and the features of fully connected layers are fused by a principal component analysis/spectral regression kernel discriminant analysis method for classification. For validation and comparison purposes, the proposed approach is evaluated via experiments with two challenging high-resolution remote sensing data sets, and shows the competitive performance compared with fully trained CNN models, fine-tuning CNN models, and other related works.
    Subject(s): Training ; Convolutional codes ; Visualization ; feature fusion ; Semantics ; spectral regression kernel discriminant analysis (SRKDA) ; Feature extraction ; Convolutional neural networks (CNN) ; Data mining ; scene classification ; Remote sensing ; improved Fisher kernel
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 2
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2015-05, Vol.53 (5), p.2532-2546
    Description: In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification.
    Subject(s): Training ; Accuracy ; Training data ; hyperspectral image classification ; Markov random fields (MRFs) ; Feature extraction ; rotation forests ; Hyperspectral imaging ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 3
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2018-01, Vol.56 (1), p.202-216
    Description: Classification techniques for hyperspectral images based on random forest (RF) ensembles and extended multiextinction profiles (EMEPs) are proposed as a means of improving performance. To this end, five strategies - bagging, boosting, random subspace, rotation-based, and boosted rotation-based - are used to construct the RF ensembles. EPs, which are based on an extrema-oriented connected filtering technique, are applied to the images associated with the first informative components extracted by independent component analysis, leading to a set of EMEPs. The effectiveness of the proposed method is investigated on two benchmark hyperspectral images: the University of Pavia and Indian Pines. Comparative experimental evaluations reveal the superior performance of the proposed methods, especially those employing rotation-based and boosted rotation-based approaches. An additional advantage is that the CPU processing time is acceptable.
    Subject(s): Radio frequency ; extended multiextinction profiles (EMEPs) ; hyperspectral image classification ; Vegetation ; Feature extraction ; Boosting ; Sensors ; Ensemble learning ; Hyperspectral imaging ; random forest (RF) ; Principal components analysis ; Usage ; Image processing ; Image segmentation
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 4
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2016-03, Vol.54 (3), p.1519-1531
    Description: With different principles, support vector machines (SVMs) and multiple classifier systems (MCSs) have shown excellent performances for classifying hyperspectral remote sensing images. In order to further improve the performance, we propose a novel ensemble approach, namely, rotation-based SVM (RoSVM), which combines SVMs and MCSs together. The basic idea of RoSVM is to generate diverse SVM classification results using random feature selection and data transformation, which can enhance both individual accuracy and diversity within the ensemble simultaneously. Two simple data transformation methods, i.e., principal component analysis and random projection, are introduced into RoSVM. An empirical study on three hyperspectral data sets demonstrates that the proposed RoSVM ensemble method outperforms the single SVM and random subspace SVM. The impacts of the parameters on the overall accuracy of RoSVM (different training sets, ensemble sizes, and numbers of features in the subset) are also investigated in this paper.
    Subject(s): Support vector machines ; Training ; hyperspectral remote sensing image ; Accuracy ; multiple classifier systems (MCSs) ; Classification ; Hyperspectral imaging ; Principal component analysis ; rotation-based ensemble ; support vector machines (SVMs) ; Learning ; Usage ; Analysis ; Principal components analysis ; Regression analysis ; Research ; Remote sensing ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 5
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2017-01, Vol.55 (1), p.421-431
    Description: Multiple classifier systems or ensemble learning is an effective tool for providing accurate classification results of hyperspectral remote sensing images. Two well-known ensemble learning classifiers for hyperspectral data are random forest (RF) and rotation forest (RoF). In this paper, we proposed to use a novel decision tree (DT) ensemble method, namely, canonical correlation forest (CCF). More specifically, several individual canonical correlation trees (CCTs) that are binary DTs, which use canonical correlation components for the hyperplane splitting, are used to construct the CCF. Additionally, we adopt the projection bootstrap technique in CCF, in which the full spectral bands are retained for split selection in the projected space. The techniques aforementioned allow the CCF to improve the accuracy of member classifiers and diversity within the ensemble. Furthermore, the CCF is extended to the spectral-spatial frameworks that incorporate Markov random fields, extended multiattribute profiles (EMAPs), and the ensemble of independent component analysis and rolling guidance filter (E-ICA-RGF). Experimental results on six hyperspectral data sets are used to indicate the comparative effectiveness of the proposed method, in terms of accuracy and computational complexity, compared with RF and RoF, and it turns out that CCF is a promising approach for hyperspectral image classification not only with spectral information but also in the spectral-spatial frameworks.
    Subject(s): Training ; Radio frequency ; ensemble learning ; Correlation ; Canonical correlation forests (CCF) ; hyperspectral image ; classification ; Spatial resolution ; Hyperspectral imaging ; Markov processes ; Forests and forestry ; Research ; Image processing ; Geophysical research
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 6
    Language: English
    In: IEEE geoscience and remote sensing letters, 2014-01, Vol.11 (1), p.239-243
    Description: In this letter, an ensemble learning approach, Rotation Forest, has been applied to hyperspectral remote sensing image classification for the first time. The framework of Rotation Forest is to project the original data into a new feature space using transformation methods for each base classifier (decision tree), then the base classifier can train in different new spaces for the purpose of encouraging both individual accuracy and diversity within the ensemble simultaneously. Principal component analysis (PCA), maximum noise fraction, independent component analysis, and local Fisher discriminant analysis are introduced as feature transformation algorithms in the original Rotation Forest. The performance of Rotation Forest was evaluated based on several criteria: different data sets, sensitivity to the number of training samples, ensemble size and the number of features in a subset. Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, and Random Forest. They indicate that Rotation Forests are promising approaches for generating classifier ensemble of hyperspectral remote sensing.
    Subject(s): Training ; ensemble learning ; hyperspectral remote sensing image ; decision tree ; Rotation Forest ; Forestry ; Classification ; Bagging ; Principal component analysis ; Hyperspectral imaging ; Engineering Sciences ; Signal and Image processing
    ISSN: 1545-598X
    E-ISSN: 1558-0571
    Source: IEEE Electronic Library (IEL)
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  • 7
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2018-09, Vol.56 (9), p.5343-5356
    Description: Multiple types of features, e.g., spectral, filtering, texture, and shape features, are helpful for hyperspectral image (HSI) classification tasks. Combining multiple features can describe the characteristics of pixels from different perspectives, and always results in better classification performance. Recently, multifeature combination learning has been widely employed to the multitask-learning-based representation-based model to obtain a multifeature representation vector. However, the linear sparse representation-based classifier (SRC) cannot handle the HSI with highly nonlinear distribution, and kernel sparse representation-based classifier (KSRC) can remedy the drawback of linear SRC. By adopting nonlinear mapping, the samples in kernel space are often of high or even infinite dimensionality. In this paper, we integrate kernel principal component analysis into multifeature-based KSRC and propose a novel multiple feature kernel sparse representation-based classifier (namely, MFKSRC) for hyperspectral imagery. More specifically, spatial features, Gabor textures, local binary patterns, and difference morphological profiles are adopted and then each kind of feature is transformed nonlinearly into a new low-dimensional kernel space. The proposed framework can handle data with nonlinear distribution and add a dimensionality reduction stage in kernel space before optimizing the corresponding cost function. Experimental results on different HSIs demonstrate that the proposed MFKSRC algorithm outperforms the state-of-the-art classifiers.
    Subject(s): Hyperspectral image (HSI) classification ; Dictionaries ; Shape ; multiple feature learning ; Feature extraction ; kernel principal component analysis (KPCA) ; multitask learning ; Kernel ; Task analysis ; Hyperspectral imaging ; sparse representation ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 8
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2015-09, Vol.53 (9), p.4768-4786
    Description: Classification is one of the most important techniques to the analysis of hyperspectral remote sensing images. Nonetheless, there are many challenging problems arising in this task. Two common issues are the curse of dimensionality and the spatial information modeling. In this paper, we present a new general framework to train series of effective classifiers with spatial information for classifying hyperspectral data. The proposed framework is based on the two key observations: 1) the curse of dimensionality and the high feature-to-instance ratio can be alleviated by using random subspace (RS) ensembles; and 2) the spatial-contextual information is modeled by the extended multiattribute profiles (EMAPs). Two fast learning algorithms, i.e., decision tree (DT) and extreme learning machine (ELM), are selected as the base classifiers. Six RS ensemble methods, namely, RS with DT, random forest (RF), rotation forest, rotation RF (RoRF), RS with ELM (RSELM), and rotation subspace with ELM (RoELM), are constructed by the multiple base learners. Experimental results on both simulated and real hyperspectral data verify the effectiveness of the RS ensemble methods for the classification of both spectral and spatial information (EMAPs). On the University of Pavia Reflective Optics Spectrographic Imaging System image, our proposed approaches, i.e., both RSELM and RoELM with EMAPs, achieve the state-of-the-art performances, which demonstrates the advantage of the proposed methods. The key parameters in RS ensembles and the computational complexity are also investigated in this paper.
    Subject(s): Training ; Radio frequency ; hyperspectral data ; Classification ; Feature extraction ; Prediction algorithms ; random subspace (RS) ; extended multiattribute profiles (EMAPs) ; Hyperspectral imaging ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 9
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2018-08, Vol.56 (8), p.4664-4677
    Description: To adequately represent the nonlinearities in the high-dimensional feature space for hyperspectral images (HSIs), we propose a multiple kernel collaborative representation-based classifier (CRC) in this paper. Extended morphological profiles are first extracted from the original HSIs, because they can efficiently capture the spatial and spectral information. In the proposed method, a novel multiple kernel learning (MKL) model is embedded into CRC. Multiple kernel patterns, e.g., Naive, Multimetric, and Multiscale are adopted for the optimal set of basic kernels, which are helpful to capture the useful information from different pixel distributions, kernel metric spaces, and kernel scales. To learn an optimal linear combination of the predefined basic kernels, we add an extra training stage to the typical CRC where kernel weights are jointly learned with the representation coefficients from the training samples by minimizing the representation error. Moreover, by considering different contributions of dictionary atoms, the adaptive representation strategy is applied to the MKL framework via a dissimilarity-weighted regularizer to obtain a more robust representation of test pixels in the fused kernel space. Experimental results on three real HSIs confirm that the proposed classifiers outperform the other state-of-the-art representation-based classifiers.
    Subject(s): Training ; Dictionaries ; Collaboration ; extended morphological profiles (EMPs) ; Collaborative representation (CR) ; Kernel ; Gallium nitride ; Hyperspectral imaging ; hyperspectral image (HSI) classification ; multiple kernel learning (MKL)
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 10
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2016-08, Vol.54 (8), p.4971-4982
    Description: To obtain accurate classification results of hyperspectral images, both spectral and spatial information should be fully exploited in the classification process. In this paper, we propose a novel method using independent component analysis (ICA) and edge-preserving filtering (EPF) via an ensemble strategy for the classification of hyperspectral data. First, several subsets are randomly selected from the original feature space. Second, ICA is used to extract spectrally independent components followed by an effective EPF method, to produce spatial features. Two strategies (i.e., parallel and concatenated) are presented to include the spatial features in the analysis. The spectral-spatial features are then classified with a random forest or a rotation forest classifier. Experimental results on two real hyperspectral data sets demonstrate the effectiveness of the proposed methods. A sensitivity analysis of the new classifiers is also performed.
    Subject(s): hyperspectral data ; edge-preserving filter (EPF) ; Image edge detection ; independent component analysis (ICA) ; Classification ; Feature extraction ; Entropy ; Hyperspectral imaging ; Principal component analysis ; Signal and Image Processing ; Computer Science
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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