A Dual-Path Small Convolution Network for Hyperspectral Image Classification

Convolutional neural network (CNN) has shown excellent performance in hyperspectral image (HSI) classification. However, the structure of the CNN models is complex, requiring many training parameters and floating-point operations (FLOPs). This is often inefficient and results in longer training and testing time. In addition, the label samples of hyperspectral data are limited, and a deep network often causes the over-fitting phenomenon. Hence, a dual-path small convolution (DPSC) module is proposed. It is composed of two 1 × 1 small convolutions with a residual path and a density path. It can effectively extract abstract features from HSI. A dual-path small convolution network (DPSCN) is constructed by stacking DPSC modules. Specifically, the proposed model uses a DPSC module to complete the extraction of spectral and spectral–spatial features successively. It then uses a global average pooling layer at the end of the model to replace the conventional fully connected layer to complete the final classification. In the implemented study, all convolutional layers of the proposed network, except the middle layer, use 1 × 1 small convolution, effectively reduced model parameters and increased the speed of feature extraction processes. DPSCN was compared with several current state-of-the-art models. The results on three benchmark HSI data sets demonstrated that the proposed model is of lower complexity, has stronger generalization ability, and has higher classification efficiency.

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Active Semi-Supervised Random Forest for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs11242974 ◽

2019 ◽

Vol 11 (24) ◽

pp. 2974 ◽

Cited By ~ 3

Author(s):

Youqiang Zhang ◽

Guo Cao ◽

Xuesong Li ◽

Bisheng Wang ◽

Peng Fu

Keyword(s):

Random Forest ◽

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Data Sets ◽

Great Success ◽

Full Potential ◽

Hyperspectral Image Classification ◽

Unified Framework ◽

Proposed Model

Random forest (RF) has obtained great success in hyperspectral image (HSI) classification. However, RF cannot leverage its full potential in the case of limited labeled samples. To address this issue, we propose a unified framework that embeds active learning (AL) and semi-supervised learning (SSL) into RF (ASSRF). Our aim is to utilize AL and SSL simultaneously to improve the performance of RF. The objective of the proposed method is to use a small number of manually labeled samples to train classifiers with relative high classification accuracy. To achieve this goal, a new query function is designed to query the most informative samples for manual labeling, and a new pseudolabeling strategy is introduced to select some samples for pseudolabeling. Compared with other AL- and SSL-based methods, the proposed method has several advantages. First, ASSRF utilizes the spatial information to construct a query function for AL, which can select more informative samples. Second, in addition to providing more labeled samples for SSL, the proposed pseudolabeling method avoids bias caused by AL-labeled samples. Finally, the proposed model retains the advantages of RF. To demonstrate the effectiveness of ASSRF, we conducted experiments on three real hyperspectral data sets. The experimental results have shown that our proposed method outperforms other state-of-the-art methods.

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The Weibull Birnbaum-Saunders Distribution And Its Applications

Statistics Optimization & Information Computing ◽

10.19139/soic-2310-5070-887 ◽

2020 ◽

Vol 9 (1) ◽

pp. 61-81

Author(s):

Lazhar BENKHELIFA

Keyword(s):

Maximum Likelihood ◽

Estimation Method ◽

Likelihood Estimation ◽

Real Data ◽

Reliability Estimation ◽

Maximum Likelihood Estimates ◽

Model Parameters ◽

Data Sets ◽

Proposed Model ◽

Modeling Data

A new lifetime model, with four positive parameters, called the Weibull Birnbaum-Saunders distribution is proposed. The proposed model extends the Birnbaum-Saunders distribution and provides great flexibility in modeling data in practice. Some mathematical properties of the new distribution are obtained including expansions for the cumulative and density functions, moments, generating function, mean deviations, order statistics and reliability. Estimation of the model parameters is carried out by the maximum likelihood estimation method. A simulation study is presented to show the performance of the maximum likelihood estimates of the model parameters. The flexibility of the new model is examined by applying it to two real data sets.

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An Incremental Isomap Method for Hyperspectral Dimensionality Reduction and Classification

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.87.7.445 ◽

2021 ◽

Vol 87 (6) ◽

pp. 445-455

Author(s):

Yi Ma ◽

Zezhong Zheng ◽

Yutang Ma ◽

Mingcang Zhu ◽

Ran Huang ◽

...

Keyword(s):

Manifold Learning ◽

Nearest Neighbor ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Training Data ◽

Support Vector ◽

Data Sets ◽

K Nearest Neighbor ◽

Data Set ◽

Data Points

Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We pres- ent in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature varia- tion algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine.

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Kernel Joint Sparse Representation Based on Self-Paced Learning for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs11091114 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1114

Author(s):

Sixiu Hu ◽

Jiangtao Peng ◽

Yingxiong Fu ◽

Luoqing Li

Keyword(s):

Sparse Representation ◽

Hyperspectral Image ◽

Feature Space ◽

Classification Performance ◽

Hyperspectral Data ◽

Data Sets ◽

Neighborhood Structure ◽

Joint Sparse Representation ◽

Negative Effect ◽

Sparse Coefficient

By means of joint sparse representation (JSR) and kernel representation, kernel joint sparse representation (KJSR) models can effectively model the intrinsic nonlinear relations of hyperspectral data and better exploit spatial neighborhood structure to improve the classification performance of hyperspectral images. However, due to the presence of noisy or inhomogeneous pixels around the central testing pixel in the spatial domain, the performance of KJSR is greatly affected. Motivated by the idea of self-paced learning (SPL), this paper proposes a self-paced KJSR (SPKJSR) model to adaptively learn weights and sparse coefficient vectors for different neighboring pixels in the kernel-based feature space. SPL strateges can learn a weight to indicate the difficulty of feature pixels within a spatial neighborhood. By assigning small weights for unimportant or complex pixels, the negative effect of inhomogeneous or noisy neighboring pixels can be suppressed. Hence, SPKJSR is usually much more robust. Experimental results on Indian Pines and Salinas hyperspectral data sets demonstrate that SPKJSR is much more effective than traditional JSR and KJSR models.

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A Robust Dynamic Classifier Selection Approach for Hyperspectral Images with Imprecise Label Information

Sensors ◽

10.3390/s20185262 ◽

2020 ◽

Vol 20 (18) ◽

pp. 5262

Author(s):

Meizhu Li ◽

Shaoguang Huang ◽

Jasper De Bock ◽

Gert de Cooman ◽

Aleksandra Pižurica

Keyword(s):

Hyperspectral Image ◽

Probability Distributions ◽

Imprecise Probabilities ◽

Data Sets ◽

Classifier Selection ◽

Training Samples ◽

Proposed Model ◽

Label Information ◽

Dynamic Classifier Selection ◽

The Individual

Supervised hyperspectral image (HSI) classification relies on accurate label information. However, it is not always possible to collect perfectly accurate labels for training samples. This motivates the development of classifiers that are sufficiently robust to some reasonable amounts of errors in data labels. Despite the growing importance of this aspect, it has not been sufficiently studied in the literature yet. In this paper, we analyze the effect of erroneous sample labels on probability distributions of the principal components of HSIs, and provide in this way a statistical analysis of the resulting uncertainty in classifiers. Building on the theory of imprecise probabilities, we develop a novel robust dynamic classifier selection (R-DCS) model for data classification with erroneous labels. Particularly, spectral and spatial features are extracted from HSIs to construct two individual classifiers for the dynamic selection, respectively. The proposed R-DCS model is based on the robustness of the classifiers’ predictions: the extent to which a classifier can be altered without changing its prediction. We provide three possible selection strategies for the proposed model with different computational complexities and apply them on three benchmark data sets. Experimental results demonstrate that the proposed model outperforms the individual classifiers it selects from and is more robust to errors in labels compared to widely adopted approaches.

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A GA-Based Multi-View, Multi-Learner Active Learning Framework for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs12020297 ◽

2020 ◽

Vol 12 (2) ◽

pp. 297 ◽

Cited By ~ 3

Author(s):

Nasehe Jamshidpour ◽

Abdolreza Safari ◽

Saeid Homayouni

Keyword(s):

Active Learning ◽

Supervised Learning ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Accurate Estimation ◽

Data Sets ◽

Learning Framework ◽

Spatial Graph ◽

Active Learning Method ◽

Significant Superiority

This paper introduces a novel multi-view multi-learner (MVML) active learning method, in which the different views are generated by a genetic algorithm (GA). The GA-based view generation method attempts to construct diverse, sufficient, and independent views by considering both inter- and intra-view confidences. Hyperspectral data inherently owns high dimensionality, which makes it suitable for multi-view learning algorithms. Furthermore, by employing multiple learners at each view, a more accurate estimation of the underlying data distribution can be obtained. We also implemented a spectral-spatial graph-based semi-supervised learning (SSL) method as the classifier, which improved the performance of the classification task in comparison with supervised learning. The evaluation of the proposed method was based on three different benchmark hyperspectral data sets. The results were also compared with other state-of-the-art AL-SSL methods. The experimental results demonstrated the efficiency and statistically significant superiority of the proposed method. The GA-MVML AL method improved the classification performances by 16.68%, 18.37%, and 15.1% for different data sets after 40 iterations.

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Refining Coarse-Grained Spatial Data Using Auxiliary Spatial Data Sets with Various Granularities

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33015091 ◽

2019 ◽

Vol 33 ◽

pp. 5091-5099 ◽

Cited By ~ 2

Author(s):

Yusuke Tanaka ◽

Tomoharu Iwata ◽

Toshiyuki Tanaka ◽

Takeshi Kurashima ◽

Maya Okawa ◽

...

Keyword(s):

Gaussian Process ◽

Spatial Data ◽

Coarse Grained ◽

Model Parameters ◽

Data Sets ◽

Auxiliary Data ◽

Fine Grained ◽

Proposed Model ◽

Spatial Data Sets ◽

Target Data

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The finegrained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of finegrained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.

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A New Generalized Weighted Weibull Distribution

Pakistan Journal of Statistics and Operation Research ◽

10.18187/pjsor.v15i1.2782 ◽

2019 ◽

pp. 161-178 ◽

Cited By ~ 1

Author(s):

Salman Abbas ◽

Gamze Ozal ◽

Saman Hanif Shahbaz ◽

Muhammad Qaiser Shahbaz

Keyword(s):

Weibull Distribution ◽

Generating Function ◽

Probability Generating Function ◽

Real Data ◽

Quantile Function ◽

Moment Generating Function ◽

Likelihood Method ◽

Model Parameters ◽

Data Sets ◽

Proposed Model

In this article, we present a new generalization of weighted Weibull distribution using Topp Leone family of distributions. We have studied some statistical properties of the proposed distribution including quantile function, moment generating function, probability generating function, raw moments, incomplete moments, probability, weighted moments, Rayeni and q th entropy. The have obtained numerical values of the various measures to see the eect of model parameters. Distribution of of order statistics for the proposed model has also been obtained. The estimation of the model parameters has been done by using maximum likelihood method. The eectiveness of proposed model is analyzed by means of a real data sets. Finally, some concluding remarks are given.

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The Beta Exponential Fréchet Distribution with Applications

Austrian Journal of Statistics ◽

10.17713/ajs.v46i1.144 ◽

2017 ◽

Vol 46 (1) ◽

pp. 41-63 ◽

Cited By ~ 6

Author(s):

M.E. Mead ◽

Ahmed Z. Afify ◽

G.G. Hamedani ◽

Indranil Ghosh

Keyword(s):

Residual Life ◽

Real Data ◽

Mean Residual Life ◽

Model Parameters ◽

Data Sets ◽

Nested Models ◽

Fréchet Distribution ◽

Proposed Model ◽

Mean Inactivity Time ◽

Frechet Distribution

We define and study a new generalization of the Fréchet distribution called the beta exponential Fréchet distribution. The new model includes thirty two special models. Some of its mathematical properties, including explicit expressions for the ordinary and incomplete moments, quantile and generating functions, mean residual life, mean inactivity time, order statistics and entropies are derived. The method of maximum likelihood is proposed to estimate the model parameters. A small simulation study is alsoreported. Two real data sets are applied to illustrate the flexibility of the proposed model compared with some nested and non-nested models.

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Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification

Remote Sensing ◽

10.3390/rs12020280 ◽

2020 ◽

Vol 12 (2) ◽

pp. 280 ◽

Cited By ~ 3

Author(s):

Liqin Liu ◽

Zhenwei Shi ◽

Bin Pan ◽

Ning Zhang ◽

Huanlin Luo ◽

...

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Hyperspectral Image ◽

Feature Fusion ◽

Hyperspectral Data ◽

Learning Technology ◽

Learning Networks ◽

Spatial Features ◽

Training Samples ◽

Rgb Image

In recent years, deep learning technology has been widely used in the field of hyperspectral image classification and achieved good performance. However, deep learning networks need a large amount of training samples, which conflicts with the limited labeled samples of hyperspectral images. Traditional deep networks usually construct each pixel as a subject, ignoring the integrity of the hyperspectral data and the methods based on feature extraction are likely to lose the edge information which plays a crucial role in the pixel-level classification. To overcome the limit of annotation samples, we propose a new three-channel image build method (virtual RGB image) by which the trained networks on natural images are used to extract the spatial features. Through the trained network, the hyperspectral data are disposed as a whole. Meanwhile, we propose a multiscale feature fusion method to combine both the detailed and semantic characteristics, thus promoting the accuracy of classification. Experiments show that the proposed method can achieve ideal results better than the state-of-art methods. In addition, the virtual RGB image can be extended to other hyperspectral processing methods that need to use three-channel images.

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