Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images

Hyperspectral band selection (BS) is an important technique to reduce data dimensionality for the classification applications of hyperspectral remote sensing images (HSIs). Recently, searching-based BS methods have received increasing attention for their ability to select the best subset of bands while preserving the essential information of the original data. However, existing searching-based BS methods neglect effective exploitation of the spatial and spectral prior information inherent in the data, thus limiting their performance. To address this problem, in this study, a novel unsupervised BS method called Spectral–Spatial Iterative Greedy Algorithm (SSIGA) is proposed. Specifically, to facilitate efficient local search using spectral information, SSIGA conducts clustering on all the bands by employing a K-means clustering method with balanced cluster size constraints and constructs a K-nearest neighbor graph for each cluster. Based on the nearest neighbor graphs, SSIGA can effectively explore the neighborhood solutions in local search. In addition, to efficiently evaluate the discriminability and information redundancy of the solution given by SSIGA using the spatial and spectral information of HSIs, we designed an effective objective function for SSIGA. The value of the objective function is derived by calculating the Fisher score for each band in the solution based on the results of the superpixel segmentation performed on the target HSI, as well as by computing the average information entropy and mutual information of the bands in the solution. Experimental results on three publicly available real HSI datasets demonstrate that the SSIG algorithm achieves superior performance compared to several state-of-the-art methods.

Multiobjective band selection approach via an adaptive particle swarm optimizer for remote sensing hyperspectral images

Band selection (BS), which selects a subset of original bands that contain the most useful information about objects, is an important technique to reduce the dimensionality of hyperspectral data. Dimensionality reduction before hyperspectral data classification can reduce redundancy information and even improve classification accuracy. We propose BS algorithms based on an ant colony optimization (ACO) in conjunction with objective functions such as the supervised Jeffries–Matusita distance and unsupervised simplex volume. Moreover, we propose to use a small number of selected pixels for BS in order to reduce computational cost in the unsupervised BS. In this experiment, the proposed algorithms were applied to three airborne hyperspectral datasets including urban scenes, and the results demonstrated that the ACO-based BS could find a better combination of bands than the widely used sequential forward search-based BS. It was acceptable to use a few pixels to achieve comparable BS performance with our method.

A hyperspectral band selection method based on sparse band attention network for maize seed variety identification

In the field of unsupervised band selection, minimum linear prediction (LP) error is a commonly used criterion function. To avoid the large computational complexity, sequential forward selection (SFS) is often employed for subset search in LP-based methods. In this article, we propose a highly efficient LP-based band selection method termed autocorrelation matrix-based band selection (ACMBS), which adopts the sequential backward selection (SBS) as subset search strategy. Interestingly, the LP error is finally transformed into the inverse of the autocorrelation matrix in ACMBS. Thus the computational complexity of ACMBS is significantly reduced. Moreover, we further improve the accuracy of ACMBS by employing relative error, instead of absolute error, as a cost function which is invariant to the magnitude of bands. The results of the experiment show that ACMBS is quite efficient and outperforms the other compared methods in terms of classification accuracy as well.

The high dimensionality of hyperspectral imagery is a huge challenge for remote sensing data processing. Band selection utilizes the most distinctive and informative band subset to reduce data dimensions. Although band selection can significantly alleviate the computational burden, the process itself may be time consuming because it needs to take all pixels into consideration, especially when the image spatial size is larger. An improved band similarity-based band selection method is proposed for hyperspectral imagery target detection, which includes four steps: (1) bad bands are removed by data preprocessing; (2) several selected pixels are used for band selection instead of using all the pixels to reduce the computational complexity; (3) hyperspectral imagery is analyzed for target detection; and (4) the number of selected bands is determined by adjusting the threshold of similarity metric, to ensure target detection operators have the best performance with selected bands. In the example, the well-known adaptive coherence estimator detector was used to evaluate the effectiveness of the proposed band selection method. The receiver operating characteristics curves were plotted to prove the proposed algorithm quantitatively. The experimental results show that our method can yield a better result in target detection than other band selection methods.

Band selection is one of the main methods of reducing the number of dimensions in a hyperspectral image. Recently, various methods have been proposed to address this issue. However, these methods usually obtain the band subset in the perspective of a locally optimal solution. To achieve an optimal solution with a global perspective, this paper developed a novel method for hyperspectral band selection via optimal combination strategy (OCS). The main contributions are as follows: (1) a subspace partitioning approach is proposed which can accurately obtain the partitioning points of the subspace. This ensures that similar bands can be divided into the same subspace; (2) two candidate representative bands with a large amount of information and high similarity are chosen from each subspace, which can fully represent all bands in the subspace; and (3) an optimal combination strategy is designed to acquire the optimal band subset, which achieves an optimal solution with a global perspective. The results on four public datasets illustrate that the proposed method achieves satisfactory performance against other methods.

Different crops, as well as the same crop at different growth stages, display distinct spectral and spatial characteristics in hyperspectral images (HSIs) due to variations in their chemical composition and structural features. However, the narrow bandwidth and closely spaced spectral channels of HSIs result in significant data redundancy, posing challenges to crop identification and classification. Therefore, the dimensionality reduction in HSIs is crucial. Band selection as a widely used method for reducing dimensionality has been extensively applied in research on crop identification and mapping. In this paper, a crop superpixel-based affinity propagation (CS-AP) band selection method is proposed for crop identification and mapping in agriculture using HSIs. The approach begins by gathering crop superpixels; then, a spectral band selection criterion is developed by analyzing the variations in the spectral and spatial characteristics of crop superpixels. Finally, crop identification bands are determined through an efficient clustering approach, AP. Two typical agricultural hyperspectral data sets, the Salinas Valley data set and the Indian Pines data set, are selected for validation, each containing 16 crop classes, respectively. The experimental results show that the proposed CS-AP method achieves a mapping accuracy of 92.4% for the Salinas Valley data set and 88.6% for the Indian Pines data set. When compared to using all bands, two unsupervised band selection techniques, and three semi-supervised band selection techniques, the proposed method outperforms others with an improvement of 3.1% and 4.3% for the Salinas Valley and Indian Pines data sets, respectively. Indicate that the proposed CS-AP method achieves superior mapping accuracy by selecting fewer bands with greater crop identification capability compared to the other band selection methods. This research’s significant results demonstrate the potential of this approach in precision agriculture, offering a more cost-effective and timely solution for large-scale crop mapping and monitoring in the future.

Band selection is of great important for hyperspectral image processing, which can effectively reduce the data redundancy and computation time. In the case of unknown class labels, it is very difficult to select an effective band subset. In this paper, an unsupervised band selection algorithm is proposed which can preserve the original information of the hyperspectral image and select a low-redundancy band subset. First, a search criterion is designed to effectively search the best band subset with maximum information entropy. It is challenging to select a low-redundancy spectral band subset with maximizing the search criteria since it is a NP-hard problem. To overcome this problem, a double-graph model is proposed to capture the correlations between spectral bands with full use of the spatial information. Then, an improved Determinantal Point Process algorithm is presented as the search method to find the low-redundancy band subset from the double-graph model. Experimental results verify that our algorithm achieves better performance than other state-of-the-art methods.

In this manuscript, we propose a sparse self-representation (SSR) method to select a band subset in hyperspectral imagery (HSI) classification. The SSR method improves from multiple measurement vectors (MMV) with the measurement matrix equals to the observation matrix. The SSR regards that each band could be represented as a linear combination of the representatives of all bands and accordingly all band vectors are reconstructed by themselves accompanied with a sparse coefficient matrix. The SSR finds the indices of nonzero rows of the coefficient matrix by convex optimization to help to estimate the band subset that are constituted with representatives. Preliminary classification results on two open HSI datasets show that the SSR offers comparable results to the linear constrained minimum variance-based band correlation constraint (LCMV-BCC) method, and achieves better results than the maximum-variance principal component analysis (MVPCA) and sparse based band selection (SPaBS) methods.

Most unsupervised band selection methods take the information of bands into account, but few of them pay attention to the quality of bands. In this paper, by combining idea of noise- adjusted principal components (NAPCs) with a state-of-art band selection method (maximum determinant of covariance matrix (MDCM)), we define a new index to quantitatively measure the quality of the hyperspectral data cube. Both signal-to-noise ratios (SNRs) and correlation of bands are simultaneously considered in . Based on the new index defined in this article, we propose an unsupervised band selection method called minimum noise band selection (MNBS). Taking the quality ( ) of the data cube as selection criterion, MNBS tries to find the bands with both high SNRs and low correlation (high ). The subset selection method, sequential backward selection (SBS), is used in MNBS to improve the search efficiency. Some comparative experiments based on simulated as well as real hyperspectral data are conducted to evaluate the performance of MNBS in this study. The experimental results show that the bands selected by MNBS are always more effective than those selected by other methods in terms of classification.

Band selection for hyperspectral images is an effective technique to mitigate the curse of dimensionality. A variety of band selection methods have been suggested in the past. This paper presents a novel band prioritization based on impurity function (IF) for the band selection of hyperspectral images. The proposed IF band selection (IFBS) is incorporated with particle swarm optimization (PSO) band selection which has been developed to effectively group highly correlated bands of hyperspectral images into high corrected modules. It uses a particle swarm optimization scheme, which is a well-known method to solve the optimization problems, to develop an effective feature extraction algorithm for hyperspectral imagery. After PSO method is applied to the band reduction of hyperspectral images, the proposed IFBS is applied to enhance the efficiency of band selection. The propose method is evaluated by MODIS/ASTER airborne simulator (MASTER) for land cover classification during the Pacrim II campaign. The performance of IFBS is validated by the supervised k-nearest neighbor (KNN) classifier. Experimental results demonstrate that the proposed IFBS approach is an effective method for dimensionality reduction and feature extraction. Compared to other band selection methods, IFBS can effectively select the most significant bands for the image classification of hyperspectral images.

Hyperspectral band selection plays an important role in overcoming the curse of dimensionality. Recently, clustering-based band selection methods have shown promise in the selection of informative and representative bands from hyperspectral images (HSIs). However, most existing clustering-based band selection methods involve the clustering of original HSIs, limiting their performance because of the high dimensionality of hyperspectral bands. To tackle this problem, a novel hyperspectral band selection method termed joint learning of correlation-constrained fuzzy clustering and discriminative non-negative representation for hyperspectral band selection (CFNR) is presented. In CFNR, graph regularized non-negative matrix factorization (GNMF) and constrained fuzzy C-means (FCM) are integrated into a unified model to perform clustering on the learned feature representation of bands rather than on the original high-dimensional data. Specifically, the proposed CFNR aims to learn the discriminative non-negative representation of each band for clustering by introducing GNMF into the model of the constrained FCM and making full use of the intrinsic manifold structure of HSIs. Moreover, based on the band correlation property of HSIs, a correlation constraint, which enforces the similarity of clustering results between neighboring bands, is imposed on the membership matrix of FCM in the CFNR model to obtain clustering results that meet the needs of band selection. The alternating direction multiplier method is adopted to solve the joint optimization model. Compared with existing methods, CFNR can obtain a more informative and representative band subset, thus can improve the reliability of hyperspectral image classifications. Experimental results on five real hyperspectral datasets demonstrate that CFNR can achieve superior performance compared with several state-of-the-art methods.

Band selection is an important dimensionality reduction (DR) methodology for hyperspectral images (HSI). In recent years, many ranking-based clustering band selection methods have been developed. However, these methods do not consider the combination of bands in different clusters but only select the desired number of clustering centers based on band ranking to construct the reduced band subset, which may lead to obtaining a set of bands with low redundancy but little information or a set of bands with a large amount of information but high redundancy, thus falling into the local optimal solution set. To solve this problem, an unsupervised hyperspectral band selection method based on shared nearest neighbor and correlation analysis (SNNCA) is proposed in this paper. The proposed SNNCA method considers the interaction of bands in different clusters, and can obtain a set of bands with a large amount of information and low redundancy. First, this method uses the shared nearest neighbor to describe the local density of each band and takes the product of local density and distance factor as the weight to rank each band to select the required number of clustering centers, which ensures low redundancy among the clustering centers. Then, all bands are grouped into several clusters based on the Euclidean distance matrix and the clustering centers. Finally, the correlation among intra-cluster and inter-cluster bands and the information entropy are further analyzed, and the most representative band is selected from each cluster. The experimental results on two HSI datasets demonstrate that the proposed SNNCA method achieves better classification performance than that of other state-of-the-art comparison methods and possesses competitive running time.

Hyperspectral image (HSI) provided with a substantial number of correlated bands causes calculation consumption and an undesirable "dimension disaster" problem for the classification. Band selection (BS) is an effective measure to reduce the information redundancy with the physics spectrum preserved for HSI. Although the existing BS methods have achieved noticeable progress, the correlation between neighbor bands still needs to be mined deeply for an effective selection criterion. This paper proposes a BS approach to collecting the discriminative band subset for hyperspectral image classification (HSIC), which adopts the self-supervised learning paradigm to implement the BS by auxiliary spectrum rebuilding task. In specific, we utilized a Convolutional neural network (CNN) and Graph Convolutional Network (GCN) for the spectral-spatial feature extraction. Next, GCN and CNN are developed for the refinement of the band correlation sequentially. Afterward, the selected bands in terms of the acquired correlation are fed into the presented self-supervised spectrum rebuilding network for spectral reconstruction. Simultaneously, the proposed architecture completed the selection with the optimization of the band reconstruction by a defined loss function. In this way, we supply substitution for selection criterion and path searching through the end-to-end framework. The extensive experimental results and analysis demonstrated that the proposed hybrid architecture provided a competitive band subset for the classification, and the accuracies with different types of classifiers are more effective than the compared BS methods.