Xiuping Jia

Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification

A segmented, and possibly multistage, principal components transformation (PCT) is proposed for efficient hyperspectral remote-sensing image classification and display. The scheme requires, initially, partitioning the complete set of bands into several highly correlated subgroups. After separate transformation of each subgroup, the single-band separabilities are used as a guide to carry out feature selection. The selected features can then be transformed again to achieve a satisfactory data reduction ratio and generate the three most significant components for color display. The scheme reduces the computational load significantly for feature extraction, compared with the conventional PCT. A reduced number of features will also accelerate the maximum likelihood classification process significantly, and the process will not suffer the limitations encountered by trying to use the full set of hyperspectral data when training samples are limited. Encouraging results have been obtained in terms of classification accuracy, speed, and quality of color image display using two airborne visible/infrared imaging spectrometer (AVIRIS) data sets.

Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks

Due to the advantages of deep learning, in this paper, a regularized deep feature extraction (FE) method is presented for hyperspectral image (HSI) classification using a convolutional neural network (CNN). The proposed approach employs several convolutional and pooling layers to extract deep features from HSIs, which are nonlinear, discriminant, and invariant. These features are useful for image classification and target detection. Furthermore, in order to address the common issue of imbalance between high dimensionality and limited availability of training samples for the classification of HSI, a few strategies such as L2 regularization and dropout are investigated to avoid overfitting in class data modeling. More importantly, we propose a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery. Finally, in order to further improve the performance, a virtual sample enhanced method is proposed. The proposed approaches are carried out on three widely used hyperspectral data sets: Indian Pines, University of Pavia, and Kennedy Space Center. The obtained results reveal that the proposed models with sparse constraints provide competitive results to state-of-the-art methods. In addition, the proposed deep FE opens a new window for further research.

Feature Mining for Hyperspectral Image Classification

Hyperspectral sensors record the reflectance from the Earths surface over the full range of solar wavelengths with high spectral resolution. The resulting high-dimensional data contain rich information for a wide range of applications. However, for a specific application, not all the measurements are important and useful. The original feature space may not be the most effective space for representing the data. Feature mining, which includes feature generation, feature selection (FS), and feature extraction (FE), is a critical task for hyperspectral data classification. Significant research effort has focused on this issue since hyperspectral data became available in the late 1980s. The feature mining techniques which have been developed include supervised and unsupervised, parametric and nonparametric, linear and nonlinear methods, which all seek to identify the informative subspace. This paper provides an overview of both conventional and advanced feature reduction methods, with details on a few techniques that are commonly used for analysis of hyperspectral data. A general form that represents several linear and nonlinear FE methods is also presented. Experiments using two widely available hyperspectral data sets are included to illustrate selected FS and FE methods.

Efficient maximum likelihood classification for imaging spectrometer data sets

A simplified maximum likelihood classification technique for handling remotely sensed image data is proposed which reduces, significantly, the processing time associated with traditional maximum likelihood classification when applied to imaging spectrometer data, and copes with the training of geographically small classes. Several wavelength subgroups are formed from the complete set of spectral bands in the data, based on properties of the global correlation among the bands. Discriminant values are computed for each subgroup separately and the sum of discriminants is used for pixel labeling. Several subgrouping methods are investigated and the results show that a compromise among classification accuracy, processing time, and available training pixels can be achieved by using appropriate subgroup sizes. >

Foreword to the Special Issue on Spectral Unmixing of Remotely Sensed Data

The 19 papers in this special issue focus on the state-of-the-art and most recent developments in the area of spectral unmixing of remotely sensed data.

Adaptive Markov Random Field Approach for Classification of Hyperspectral Imagery

An adaptive Markov random field (MRF) approach is proposed for classification of hyperspectral imagery in this letter. The main feature of the proposed method is the introduction of a relative homogeneity index for each pixel and the use of this index to determine an appropriate weighting coefficient for the spatial contribution in the MRF classification. In this way, overcorrection of spatially high variation areas can be avoided. Support vector machines are implemented for improved class modeling and better estimate of spectral contribution to this approach. Experimental results of a synthetic hyperspectral data set and a real hyperspectral image demonstrate that the proposed method works better on both homogeneous regions and class boundaries with improved classification accuracy.

Collinearity and orthogonality of endmembers in linear spectral unmixing

Abstract Contrary to image classification, spectral unmixing techniques allow to derive abundance/fractional cover estimates for selected endmembers within the volume of a pixel. Mathematically the solution to the mixing problem is resolving a set of linear equations using least squares approaches. Practically this is done using singular value deconvolution of the endmember matrix inversion. This solution assumes orthogonality of the endmembers which determines the orthogonality of the matrix. If endmembers are highly correlated (thus collinearity or multi-collinearity occurs), the matrix becomes non-orthogonal, the inversion unstable and the inverse or estimated fractions highly sensitive to random error (e.g., noise). In practice, collinearity almost always exists but it is typically overlooked or ignored, hence with this overview we wish to create awareness to the issue and offer approaches to deal with the problem. The first part of the paper highlights the problem using a numerical example. It is shown how collinearity amplifies the error in the endmember matrix inversion. In the next paragraph we propose measures to quantify the level of (multi)collinearity in the endmember matrix: a weighted multiple correlation measure, the variance inflation factor, the partial regression coefficient. The remainder of the paper is dedicated to approaches to mitigate the problem: excluding endmembers, decorrelating endmembers, iterative approaches for endmember selection and we propose an adjustment to the unmixing equation which could be further explored. In conclusion, collinearity hampers the use of fractional abundance estimates. There is no single recipe to successfully combat this problem but in all mixture models collinearity should be tested and avoided as much as possible.

Integration of Soft and Hard Classifications Using Extended Support Vector Machines

In this letter, the supervised classification algorithm support vector machines is extended to map both pure pixels and mixed pixels using hyperspectral data. The margins between the hyperplanes formed by the pixels on the class boundaries are recognized as mixed region, and the space beyond this region is related to pure pixels. In this way, each endmember is modeled by a set of training samples instead of a single (representative) spectrum to accommodate the variations within the relative pure pixels due to system noise. Unmixing outputs generate an integrated soft- and hard-classification map. The better performance comparing with conventional spectral unmixing method was demonstrated using hyperspectral data sets.

Nonlinear Multiple Kernel Learning With Multiple-Structure-Element Extended Morphological Profiles for Hyperspectral Image Classification

In this paper, we propose a novel multiple kernel learning (MKL) framework to incorporate both spectral and spatial features for hyperspectral image classification, which is called multiple-structure-element nonlinear MKL (MultiSE-NMKL). In the proposed framework, multiple structure elements (MultiSEs) are employed to generate extended morphological profiles (EMPs) to present spatial-spectral information. In order to better mine interscale and interstructure similarity among EMPs, a nonlinear MKL (NMKL) is introduced to learn an optimal combined kernel from the predefined linear base kernels. We integrate this NMKL with support vector machines (SVMs) and reduce the min-max problem to a simple minimization problem. The optimal weight for each kernel matrix is then solved by a projection-based gradient descent algorithm. The advantages of using nonlinear combination of base kernels and multiSE-based EMP are that similarity information generated from the nonlinear interaction of different kernels is fully exploited, and the discriminability of the classes of interest is deeply enhanced. Experiments are conducted on three real hyperspectral data sets. The experimental results show that the proposed method achieves better performance for hyperspectral image classification, compared with several state-of-the-art algorithms. The MultiSE EMPs can provide much higher classification accuracy than using a single-SE EMP.

A Quantitative Analysis of Virtual Endmembers' Increased Impact on the Collinearity Effect in Spectral Unmixing

In the past decades, spectral unmixing has been studied for deriving the fractions of spectrally pure materials in a mixed pixel. However, limited attention has been given to the collinearity problem in spectral mixture analysis. In this paper, quantitative analysis and detailed simulations are provided, which show that the high correlation between the endmembers, including the virtual endmembers introduced in a nonlinear model, has a strong impact on unmixing errors through inflating the Gaussian noise. While distinctive spectra with low correlations are often selected as true endmembers, the virtual endmembers formed by their product terms can be highly correlated. It is found that a virtual-endmember-based nonlinear model generally suffers more from collinearity problems compared to linear models and may not perform as expected when the Gaussian noise is high, despite its higher modeling power. Experiments were conducted on a set of in situ measured data, and the results show that the linear mixture model performs better in 61.5% of the cases.