Multi-Feature Manifold Discriminant Analysis for Hyperspectral Image Classification

Hong, Huixiao; Li, Zhengying; Pan, Yu

doi:10.3390/rs11060651

Cited by 20 publications

(12 citation statements)

References 57 publications

(57 reference statements)

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“…As a typical texture descriptor, LBP [62] is widely employed in many tasks, such as face recognition [63], image classification [64], and object detection [65]. In HSRRS scene classification, texture-coded mapped images can be explored as the inputs of deep networks to provide useful supplementary information.…”

Section: Local Binary Pattern (Lbp) Descriptormentioning

confidence: 99%

Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing

Hong

2019

Remote Sensing

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High spatial resolution remote sensing (HSRRS) images contain complex geometrical structures and spatial patterns, and thus HSRRS scene classification has become a significant challenge in the remote sensing community. In recent years, convolutional neural network (CNN)-based methods have attracted tremendous attention and obtained excellent performance in scene classification. However, traditional CNN-based methods focus on processing original red-green-blue (RGB) image-based features or CNN-based single-layer features to achieve the scene representation, and ignore that texture images or each layer of CNNs contain discriminating information. To address the above-mentioned drawbacks, a CaffeNet-based method termed CTFCNN is proposed to effectively explore the discriminating ability of a pre-trained CNN in this paper. At first, the pretrained CNN model is employed as a feature extractor to obtain convolutional features from multiple layers, fully connected (FC) features, and local binary pattern (LBP)-based FC features. Then, a new improved bag-of-view-word (iBoVW) coding method is developed to represent the discriminating information from each convolutional layer. Finally, weighted concatenation is employed to combine different features for classification. Experiments on the UC-Merced dataset and Aerial Image Dataset (AID) demonstrate that the proposed CTFCNN method performs significantly better than some state-of-the-art methods, and the overall accuracy can reach 98.44% and 94.91%, respectively. This indicates that the proposed framework can provide a discriminating description for HSRRS images. 2 of 23 color features, spectral features, and multi-feature fusion [23,24]. However, these hand-crafted features are limited in describing complex scenes of HSRRS, which will affect the classification performance. Compared with the low-level methods, the mid-level methods aim to obtain a global representation of a scene by encoding local descriptors, e.g., scale-invariant feature transform, histogram of oriented gradient and color histogram. The bag-of-view-word (BoVW) model is one of the most popular feature encoding approaches [25]. Due to its simplicity and efficiency, the BoVW model is widely applied for mid-level scene description [26][27][28][29]. However, the quantization error of the BoVW method is large, and some important information may be lost. Therefore, many feature coding methods were developed to reduce the reconstruction error, including improved Fisher kernel (IFK) [30], vectors of locally aggregated descriptors (VLAD) [31], spatial pyramid matching kernel (SPM) [32], locality-constrained linear coding (LLC) [33], latent semantic analysis (LSA), probabilistic latent semantic analysis (pLSA) [34,35], and latent dirichlet allocation (LDA) [35]. However, both low-level and mid-level methods are mainly based on hand-crafted features, which are difficult to effectively describe in HSRRS scene images with complex land-cover/land-use (LULC) situations.In recent years, deep-learning-based methods have made a...

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Section: Local Binary Pattern (Lbp) Descriptormentioning

confidence: 99%

Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing

Hong

2019

Remote Sensing

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“…Unfortunately, these two GTs have been widely used to assess the performance of classification methods. [31][32][33][34][35][36] As an indication, as of the end of 2018, we identified almost 300 scientific published papers using one or the other of these two GTs and more than 40 using both.…”

Section: Impacts Of Biased Ground Truth In Classificationmentioning

confidence: 99%

Learning or assessment of classification algorithms relying on biased ground truth data: what interest?

Chehdi

Cariou

2019

J. Appl. Rem. Sens.

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The use of ground truth (GT) data in the learning and/or assessment of classification algorithms is essential. Using a biased or simplified GT attached to a remote sensing image to partition does not allow a rigorous explanation of the physical phenomena reflected by such images. Unfortunately, this scientific problem is not always treated carefully and is generally neglected in the relevant literature. Furthermore, the impacts of obtained classification results for decision-making are negative. This is inconsistent when considering investments in both the development of sophisticated sensors and the design of objective classification algorithms. Any GT must be validated according to a rigorous protocol before utilization, which is unfortunately not always the case. The evidence of this problem is provided, using two popular hyperspectral images (Indian Pine and Pavia University) that misleadingly are frequently used without care by the remote sensing community since the associated GTs are not accurate. The heterogeneity of the spectral signatures of some GT classes was proven using a semisupervised and an unsupervised classification method. Through this critical analysis, we propose a general framework for a minimum objective assessment and validation of the GT accuracy, before exploiting them in a classification method. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…in which λ r is the eigenvalue of Equation (21). After obtaining the eigenvectors v r1 , v r2 , v r3 , .…”

Section: Spatial-spectral Multi-manifold Analysis Modelmentioning

confidence: 99%

“…ISOMAP characterizes the data distribution by geodesic distances instead of Euclidean distances, it seeks a lower-dimensional embedding which maintains geodesic distances between all points [19]. However, due to their nonlinear characteristic, they suffer from the problem of out-of-sample and cannot process the unknown samples [20][21][22]. To address this issue, many linear manifold learning methods were designed to obtain explicit feature mappings, which can map unknown samples into low-dimensional space [23,24].…”

Section: Introductionmentioning

confidence: 99%

Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

et al. 2019

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Hyperspectral images (HSI) possess abundant spectral bands and rich spatial information, which can be utilized to discriminate different types of land cover. However, the high dimensional characteristics of spatial-spectral information commonly cause the Hughes phenomena. Traditional feature learning methods can reduce the dimensionality of HSI data and preserve the useful intrinsic information but they ignore the multi-manifold structure in hyperspectral image. In this paper, a novel dimensionality reduction (DR) method called spatial-spectral multiple manifold discriminant analysis (SSMMDA) was proposed for HSI classification. At first, several subsets are obtained from HSI data according to the prior label information. Then, a spectral-domain intramanifold graph is constructed for each submanifold to preserve the local neighborhood structure, a spatial-domain intramanifold scatter matrix and a spatial-domain intermanifold scatter matrix are constructed for each sub-manifold to characterize the within-manifold compactness and the between-manifold separability, respectively. Finally, a spatial-spectral combined objective function is designed for each submanifold to obtain an optimal projection and the discriminative features on different submanifolds are fused to improve the classification performance of HSI data. SSMMDA can explore spatial-spectral combined information and reveal the intrinsic multi-manifold structure in HSI. Experiments on three public HSI data sets demonstrate that the proposed SSMMDA method can achieve better classification accuracies in comparison with many state-of-the-art methods.

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Multi-Feature Manifold Discriminant Analysis for Hyperspectral Image Classification

Cited by 20 publications

References 57 publications

Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing

Combing Triple-Part Features of Convolutional Neural Networks for Scene Classification in Remote Sensing

Learning or assessment of classification algorithms relying on biased ground truth data: what interest?

Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

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