Application of hyperspectral band selection method based on deep reinforcement learning to low-value recyclable waste classification

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 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.

Multi-agent deep reinforcement learning for hyperspectral band selection with hybrid teacher guide

Multiobjective band selection approach via an adaptive particle swarm optimizer for remote sensing 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 (BS) is a method for optimizing feature selection, which aims to of reduce the computational complexity of processing hyperspectral image (HSI). However, there are many BS methods applied to image classification, target detection, and anomaly detection. Furthermore, the existing BS methods ignore the spatial structure of HSI. To solve the above problems, we proposed a dynamic programming-based BS method for hyperspectral unmixing. In this paper, we use the convex geometric structure of HSI in band space to project it into the subspace to obtain depth spectral features, then construct a dynamic programming model to select representative bands. To verify the effectiveness of the proposed method, experiments are conducted on three widely used datasets, and compared with three popular BS methods. The experimental results show that the proposed method has satisfactory performance in different evaluation indexes (including signal to reconstruction error (SRE), root mean square error (RMSE)) and three quantitative evaluations (average information entropy (AIE), average correlation coefficient (ACC) and average relative entropy (ARE)).

Imaging spectroscopy in the remote sensing is an ever emerging platform that has offered the hyperspectral imaging (HSI) which delivers the Earth’s object information in hundreds of bands. HSI integrates conventional imaging with spectroscopy to get rich spectral and spatial features of the object. However, the challenges associated with HSI are its huge dimensionality and data redundancy that requests huge space, complex computations, and lengthier processing time. Therefore, this study aims to find the optimal bands to characterize the roof surfaces using supervised classifiers. To deal with high dimensionality of hyperspectral data, this study assesses the band selection method over data transformation methods. This study provides the comparison between data reduction methods and used classifiers. The height information from LiDAR was used to characterize urban roofs above the height of 2.5 meters. The optimal bands were investigated using supervised classifiers such as artificial neural network (ANN), support vector machine (SVM), and spectral angle mapper (SAM) by comparing accuracies. The classification result shows that ANN and SVM classifiers outperform whereas SAM performed poorly in roof characterization. The band selection method worked efficiently than the transformation methods. The classification algorithm successfully identifies the optimum bands with significant accuracy.

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.

Band selection (BS) is an effective technique of dimensionality reduction in hyperspectral images (HSIs), which can solve the problems of high computational complexity and information redundancy and is helpful for the classification of HSIs. Since many hyperspectral scenes have limited label samples, semisupervised BS methods have attracted much attention. In this paper, an improved graph-based semisupervised BS method is proposed. The within-class and between-class graphs are firstly constructed by utilizing the information of the labeled and unlabeled samples, then Fisher's criteria combined with sparsity regularization (FCS) is designed to obtain the optimal projection matrix, which is used to select the representative bands. The proposed algorithm performs BS while preserving the local manifold structure of data. Experimental results on three hyperspectral data sets show that the proposed BS algorithm has good performance in selecting representative bands for classification.

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

Moldy peanuts are often found in harvested and stored peanuts. Aflatoxins in moldy peanuts pose a potential risk to food safety. Hyperspectral imaging techniques is often used for rapid nondestructive testing of food. However, the information redundancy of hyperspectral data has a negative effect on the processing speed and classification accuracy. In this study, a novel band selection method, namely the band influence algorithm (BIA), was proposed to extract key features and remove redundancy for the classification of moldy peanuts. Firstly, hyperspectral images of moldy, healthy and damaged peanuts were collected with 128 bands ranging from 400 nm to 1000nm. Secondly, the BIA method was used to extract feature bands according to the influence of each band subset on the accuracy of the classification model. The effectiveness of BIA method was compared with five representative band selection methods on four classification models: decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), and ShuffleNet V2. The experimental results show that the BIA method performs superior effect and stability than other methods on all classification models. The integration of BIA and ShuffleNet V2 achieved the best classification effect. Especially when using 10 feature bands on ShuffleNet V2, the average accuracy, F1 score, and kappa coefficient of BIA reached 97.66%, 0.977, and 0.963 respectively.

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.

Band selection is an important preprocessing step for hyperspectral image processing. Many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem. In this paper, a novel multiobjective model is first built for band selection. In this model, two objective functions with a conflicting relationship are designed. One objective function is set as information entropy to represent the information contained in the selected band subsets, and the other one is set as the number of selected bands. Then, based on this model, a new unsupervised band selection method called multiobjective optimization band selection (MOBS) is proposed. In the MOBS method, these two objective functions are optimized simultaneously by a multiobjective evolutionary algorithm to find the best tradeoff solutions. The proposed method shows two unique characters. It can obtain a series of band subsets with different numbers of bands in a single run to offer more options for decision makers. Moreover, these band subsets with different numbers of bands can communicate with each other and have a coevolutionary relationship, which means that they can be optimized in a cooperative way. Since it is unsupervised, the proposed algorithm is compared with some related and recent unsupervised methods for hyperspectral image band selection to evaluate the quality of the obtained band subsets. Experimental results show that the proposed method can generate a set of band subsets with different numbers of bands in a single run and that these band subsets have a stable good performance on classification for different data sets.

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.

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.