Abstract
Due to the advances in hyperspectral sensor technology, hyperspectral images have gained great attention in precision agriculture. In practical applications, vegetation classification is usually required to be conducted first and then the vegetation of interest is discriminated from the others. This study proposes an integrated scheme (SpeSpaVS_ClassPair_ScatterMatrix) for vegetation classification by simultaneously exploiting image spectral and spatial information to improve vegetation classification accuracy. In the scheme, spectral features are selected by the proposed scatter-matrix-based feature selection method (ClassPair_ScatterMatrix). In this method, the scatter-matrix-based class separability measure is calculated for each pair of classes and then averaged as final selection criterion to alleviate the problem of mutual redundancy among the selected features, based on the conventional scatter-matrix-based class separability measure (AllClass_ScatterMatrix). The feature subset search is performed by the sequential floating forward search method. Considering the high spectral similarity among different green vegetation types, Gabor features are extracted from the top two principal components to provide complementary spatial features for spectral features. The spectral features and Gabor features are stacked into a feature vector and then the ClassPair_ScatterMatrix method is used on the formed vector to overcome the over-dimensionality problem and select discriminative features for vegetation classification. The final features are fed into support vector machine classifier for classification. To verify whether the ClassPair_ScatterMatrix method could well avoid selecting mutually redundant features, the mean square correlation coefficients were calculated for the ClassPair_ScatterMatrix method and AllClass_ScatterMatrix method. The experiments were conducted on a widely used agricultural hyperspectral image. The experimental results showed that (1) the The proposed ClassPair_ScatterMatrix method could better alleviate the problem of selecting mutually redundant features, compared to the AllClass_ScatterMatrix method; (2) compared with the representative mutual information-based feature selection methods, the scatter-matrix-based feature selection methods generally achieved higher classification accuracies, and the ClassPair_ScatterMatrix method especially, produced the highest classification accuracies with respect to both data sets (87.2% and 90.1%); and (3) the proposed integrated scheme produced higher classification accuracy, compared with the decision fusion of spectral and spatial features and the methods only involving spectral features or spatial features. The comparative experiments demonstrate the effectiveness of the proposed scheme.
Highlights
Hyperspectral remotely-sensed image has gained popularity in precision agriculture applications
A well-known problem in hyperspectral image classification is the curse of dimensionality, which shows that the supervised classification accuracy decreases as the number of features increases after a few features when keeping the number of training samples constant [8]
To validate whether the proposed ClassPair_ScatterMatrix method can better avoid selecting mutually redundant features than the AllClass_ScatterMatrix method, the mean square correlation coefficients of all pairwise spectral bands selected by the two methods were calculated and compared
Summary
Hyperspectral remotely-sensed image has gained popularity in precision agriculture applications. Spectroradiometer (MODIS) images, hyperspectral images have higher spectral resolution and provide a more contiguous spectrum [1]. The first step required is to discriminate the crop of interest from the other objects and determine its planting area. As to the discrimination of different vegetation types using hyperspectral images, this is a typical hyperspectral image classification problem. With the increase in the number of spectral bands, theoretical and practical problems may arise, and traditional techniques that are applied on multispectral images are no longer applicable for processing of hyperspectral images. A well-known problem in hyperspectral image classification is the curse of dimensionality, which shows that the supervised classification accuracy decreases as the number of features increases after a few features when keeping the number of training samples constant [8]. Feature selection and feature extraction are widely used to reduce the dimensionality of features before hyperspectral image classification [10]
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