Hyperspectral Image Classification Using Functional Data Analysis

Aiming at the problem of "dimension disaster" in hyperspectral image classification, a method of dimension reduction based on manifold data analysis and sparse subspace projection (MDASSP) is proposed. The sparse coefficient matrix is established by the new method, and the sparse subspace projection is carried out by the optimization method. To keep the geometric structure of the manifold, the objective function is regularized by the manifold learning method. The new method combines sparse coding and manifold learning to generate features with better classification ability. The experimental results show that the new method is better than other methods in the case of small samples.

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Wavelet Transformation of Functional Data for Hyperspectral Image Classification

2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) ◽

10.1109/ithings/greencom/cpscom/smartdata.2019.00087 ◽

2019 ◽

Author(s):

Guangrun Xiao ◽

Xiaobo Wang ◽

Dezheng Liu

Keyword(s):

Image Classification ◽

Functional Data ◽

Wavelet Transformation ◽

Hyperspectral Image ◽

Hyperspectral Image Classification

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Supervised Functional Data Discriminant Analysis for Hyperspectral Image Classification

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2019.2940991 ◽

2020 ◽

Vol 58 (2) ◽

pp. 841-851 ◽

Cited By ~ 1

Author(s):

Zhijing Ye ◽

Jiaqing Chen ◽

Hong Li ◽

Yantao Wei ◽

Guangrun Xiao ◽

...

Keyword(s):

Discriminant Analysis ◽

Image Classification ◽

Functional Data ◽

Hyperspectral Image ◽

Hyperspectral Image Classification

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A Novel Analysis Dictionary Learning Model Based Hyperspectral Image Classification Method

Remote Sensing ◽

10.3390/rs11040397 ◽

2019 ◽

Vol 11 (4) ◽

pp. 397 ◽

Cited By ~ 1

Author(s):

Wei Wei ◽

Mengting Ma ◽

Cong Wang ◽

Lei Zhang ◽

Peng Zhang ◽

...

Keyword(s):

Data Analysis ◽

Image Classification ◽

Dictionary Learning ◽

Hyperspectral Image ◽

Learning Model ◽

Hyperspectral Data ◽

Experimental Results ◽

Classification Method ◽

Strong Nonlinearity ◽

Hyperspectral Image Classification

Supervised hyperspectral image (HSI) classification has been acknowledged as one of the fundamental tasks of hyperspectral data analysis. Witnessing the success of analysis dictionary learning (ADL)-based method in recent years, we propose an ADL-based supervised HSI classification method in this paper. In the proposed method, the dictionary is modeled considering both the characteristics within the spectrum and among the spectra. Specifically, to reduce the influence of strong nonlinearity within each spectrum on classification, we divide the spectrum into some segments, and based on this we propose HSI classification strategy. To preserve the relationships among spectra, similarities among pixels are introduced as constraints. Experimental results on several benchmark hyperspectral datasets demonstrate the effectiveness of the proposed method for HSI classification.

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ENHANCING MULTISCALE FRACTAL DESCRIPTORS USING FUNCTIONAL DATA ANALYSIS

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127410027805 ◽

2010 ◽

Vol 20 (11) ◽

pp. 3443-3460 ◽

Cited By ~ 14

Author(s):

JOÃO BATISTA FLORINDO ◽

MÁRIO DE CASTRO ◽

ODEMIR MARTINEZ BRUNO

Keyword(s):

Pattern Recognition ◽

Fractal Dimension ◽

Data Analysis ◽

Discriminant Analysis ◽

Image Classification ◽

Functional Data Analysis ◽

Functional Data ◽

Linear Discriminant ◽

Novel Approach ◽

Different Levels

This work presents a novel approach in order to increase the recognition power of Multiscale Fractal Dimension (MFD) techniques, when applied to image classification. The proposal uses Functional Data Analysis (FDA) with the aim of enhancing the MFD technique precision achieving a more representative descriptors vector, capable of recognizing and characterizing more precisely objects in an image. FDA is applied to signatures extracted by using the Bouligand–Minkowsky MFD technique in the generation of a descriptors vector from them. For the evaluation of the obtained improvement, an experiment using two datasets of objects was carried out. A dataset was used of characters shapes (26 characters of the Latin alphabet) carrying different levels of controlled noise and a dataset of fish images contours. A comparison with the use of the well-known methods of Fourier and wavelets descriptors was performed with the aim of verifying the performance of FDA method. The descriptor vectors were submitted to Linear Discriminant Analysis (LDA) classification method and we compared the correctness rate in the classification process among the descriptors methods. The results demonstrate that FDA overcomes the literature methods (Fourier and wavelets) in the processing of information extracted from the MFD signature. In this way, the proposed method can be considered as an interesting choice for pattern recognition and image classification using fractal analysis.

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