Fusion of SVMs in Wavelet Domain for Hyperspectral Data Classification

Discrete wavelet transform (DWT) provide a multiresolution view of hyperspectral data. This paper proposes a method to combine the wavelet features at different layer to improve the classification accuracy of hyperspectral data, where both global and local spectral features could be exploited. After feature extraction using DWT, the wavelet feature set of each layer is processed independently by support vector machines (SVMs). Then, the probability outputs of SVMs at each layer are fused to get the final class probability, and the classification result will be the class label with the maximum final class probability. Experimented with the Washington DC Mall hyperspectral data, the results demonstrate that the proposed method can outperform the same classifier with original features, the wavelet feat res (without fusion), and the wavelet energy features

Spatially adaptive semi‐supervised learning with Gaussian processes for hyperspectral data analysis

This paper presents a semi‐supervised learning algorithm called Gaussian process expectation‐maximization (GP‐EM), for classification of landcover based on hyperspectral data analysis. Model parameters for each land cover class are first estimated by a supervised algorithm using Gaussian process regressions to find spatially adaptive parameters, and the estimated parameters are then used to initialize a spatially adaptive mixture‐of‐Gaussians model. The mixture model is updated by expectation‐maximization iterations using the unlabeled data, and the spatially adaptive parameters for unlabeled instances are obtained by Gaussian process regressions with soft assignments. Spatially and temporally distant hyperspectral images taken from the Botswana area by the NASA EO‐1 satellite are used for experiments. Detailed empirical evaluations show that the proposed framework performs significantly better than all previously reported results by a wide variety of alternative approaches and algorithms on the same datasets. © 2011 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 4: 358–371, 2011Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87150/1/10119_ftp.pd

lnvestigation on methods for dimensionality reduction on hyperspectral image data

Neste estudo é proposta uma nova metodologia para fins de redução da dimensionalidade dos dados em imagens hiperespectrais. Nesta abordagem, parte-se da suposição de que a curva de resposta espectral em cada pixel, pode ser adequadamente descrita por um número menor de parâmetros estatísticos, em substituição aos contadores digitais (CDs) originais, para utilização em um classificador. Neste processo, a curva de resposta espectral é inicialmente particionada em um certo número de segmentos. Em uma segunda etapa, os contadores digitais (CDs) que caracterizam cada segmento são substituídos por um número menor de parâmetros estatísticos, como a média e a variância estimada a partir dos CD's em cada segmento individual. Para a segmentação da curva de resposta espectral de cada pixel, são propostos três algoritmos de fácil implementação e computacionalmente eficientes. Usando uma estratégia top-down, o comprimento de cada segmento ao longo da curva espectral pode ser ajustado seqüencialmente. Experimentos são realizados utilizando dados adquiridos pelo sensor AVIRIS. Resultados animadores são obtidos em termos de acurácia de classificação e tempo de execução, sugerindo a eficácia dos algoritmos propostos.In the present study, we propose a new simple approach to reduce the dimensionality in hyperspectral image data. The basic assumption consists in assuming that a pixel's curve of spectral response, as defined in the spectral space by the recorded digital numbers (DNs) at the available spectral bands, can be segmented and each segment can be replaced by a smaller number of statistics, e.g., the mean and the variance, describing the main characteristics of a pixel's spectral response. Results suggest that this procedure can be accomplished without signiftcant loss of information. The DNs at every spectral band can be used to estimate a few statistical parameters that will replace them in a classifier. For the pixel's spectral curve segmentation, three sub-optimal algorithms are proposed, being easy to implement and also computationally efficient. Using a top-down strategy, the length of the segments along the spectral curves can be adjusted sequentially. Experiments using a parametric classifier are performed using an AVIRIS data set. Encouraging results have been obtained in terms of classification accuracy and execution time, suggesting the effectiveness of the proposed algorithms

Limitations of Principal Component Analysis for Dimensionality-Reduction for Classification of Hyperspectral Data

It is a popular practice in the remote-sensing community to apply principal component analysis (PCA) on a higher-dimensional feature space to achieve dimensionality-reduction. Several factors that have led to the popularity of PCA include its simplicity, ease of use, availability as part of popular remote-sensing packages, and optimal nature in terms of mean square error. These advantages have prompted the remote-sensing research community to overlook many limitations of PCA when used as a dimensionality-reduction tool for classification and target-detection applications. This thesis addresses the limitations of PCA when used as a dimensionality-reduction technique for extracting discriminating features from hyperspectral data. Theoretical and experimental analyses are presented to demonstrate that PCA is not necessarily an appropriate feature-extraction method for high-dimensional data when the objective is classification or target-recognition. The influence of certain data-distribution characteristics, such as within-class covariance, between-class covariance, and correlation on PCA transformation, is analyzed in this thesis. The classification accuracies obtained using PCA features are compared to accuracies obtained using other feature-extraction methods like variants of Karhunen-Loève transform and greedy search algorithms on spectral and wavelet domains. Experimental analyses are conducted for both two-class and multi-class cases. The classification accuracies obtained from higher-order PCA components are compared to the classification accuracies of features extracted from different regions of the spectrum. The comparative study done on the classification accuracies that are obtained using above feature-extraction methods, ascertain that PCA may not be an appropriate tool for dimensionality-reduction of certain hyperspectral data-distributions, when the objective is classification or target-recognition

Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihoodbased classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrencebased texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms