A Multi‐Strategy Enhanced Salp Swarm Optimization Approach to Optimal Spectral Band Selection for Hyperspectral Remote Sensing Imaging

Efficient use of hyperspectral (HS) sensors that can selectively activate individual, narrow spectral bands requires the development of optimized band-selection strategies that are adapted to the needs of specific detection and classification problems. By removing superfluous components of the HS data, optimized band selection significantly reduces the computational burden and improves robustness in classification. In this paper, a new method for selection of a subset of HS bands is proposed that is tailored to the problem of recognition of classes of rocks and minerals. Based on the analysis of the distribution of the amplitudes of the principal components (PCs) for a given training data set. this method identifies subsets of HS bands that provide highest spectral contrast. Three criteria are considered in the band selection process. The first criterion is based on ranking the HS bands according to the minimum distance among their respective PCs' amplitudes. The second criterion is based on ranking the HS bands according to a variant of the Kullback-Liebler divergence between a uniform distribution and the distribution of the amplitudes of the PCs for each HS band. This criterion assigns a high number on HS-bands whose PC-amplitudes' distribution either exhibits a wide range or a strong similarity to the uniform distribution. The third criterion is based on ranking the HS bands according to the empirical relative variances of the inter-amplitude distances of successive amplitudes at each HS band; it ranks high those HS-bands with small inter-amplitude-distance variance and large amplitude range. These band-selection strategies are applied to laboratory HS data of rocks and minerals yielding a subset of thirteen optimal multispectral bands. The classification performance with the reduced number of bands is compared with that of the 13-band Multispectral Thermal Imager showing a moderate improvement in classification.

Representative band selection for hyperspectral image 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.

Extended multiattribute profiles (EMAPs) are morphological profiles built on the extracted features of a hyperspectral image. These profiles proved, when used in a hyperspectral image classification task, their ability to combine the spectral and spatial information offered by this type of data. We propose building EMAPs on the features selected from a hyperspectral image. To do so, three band selection techniques are proposed. The first one is a modified version of the existent independent component analysis (ICA)-based band selection. The other two are based on the initialization-driven ICA. To test the effectiveness of the aforementioned feature selection methods, we used them to build the EMAPs of hyperspectral images; then, the generated profiles served as the input of two hyperspectral image analysis tasks: a hyperspectral image classification task-based on the sparse representation of EMAPs and an EMAP-based change detection technique that we are proposing in this paper.

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.

In remote sensing, spectral band selection has been a primordial step to improve the classification of hyperspectral images. It aims at finding the most important information from a set of bands by eliminating the irrelevant, noisy, and highly correlated bands. In this paper, the band selection problem is regarded as a combinatorial optimization problem. We propose a new band selection approach for hyperspectral image classification based on the Gray Wolf Optimizer (GWO) which is a new meta-heuristic that simulate the hunting process of gray wolf in nature. A new binary version of GWO based on transfer function is proposed. In addition, a new fitness function is designed using two terms: the first term is the SVM classifier and the second term of the fitness function is SNR measure (Signal to Noise Ration) which measures the capacity of discrimination. The proposed approach is benchmarked on three hyperspectral images widely used in band selection and hyperspectral images classification. The experimental results show that this approach is suitable to the challenging problem of spectral band selection and provides a higher classification accuracy rate compared to the other band selection methods.

Band selection for heterogeneity classification of hyperspectral transmission images based on multi-criteria ranking

Hyperspectral images have far more spectral bands than ordinary multispectral images. Rich band information provides more favorable conditions for the tremendous applications as well as many problems such as the curse of dimensionality. Band selection is an effective method to reduce the spectral dimension which is one of popular topics in hyperspectral remote sensing. Motivated by previous sparse representation method, we present a novel framework for band selection based on multi-dictionary sparse representation (MDSR). By obtaining the sparse solutions for each band vector and the corresponding dictionary, the contribution of each band to the raw image is derived. In terms of contribution, the appropriate band subset is selected. Five state-of-art band selection methods are compared with the MDSR on three widely used hyperspectral datasets. Experimental results show that MDSR achieves marginally better performance in hyperspectral image classification, and better performance in average correlation coefficient and computational time.

The analysis of hyperspectral images is usually very heavy from the computational point-of-view, due to their high dimensionality. In order to avoid this problem, band selection (BS) has been widely used to reduce the dimensionality before the analysis. The aim is to extract a subset of the original bands of the hyperspectral image, preserving most of the information contained in the original data. The BS technique can be performed by prioritizing the bands on the basis of a score, assigned by specific criteria; in this case, BS turns out in the so-called band prioritization (BP). This paper focuses on BP algorithms based on the following parameters: signal-to-noise ratio, kurtosis, entropy, information divergence, variance and linearly constrained minimum variance. In particular, an optimized C serial version has been developed for each algorithm from which two parallel versions have been derived using OpenMP and NVIDIA’s compute unified device architecture. The former is designed for a multi-core CPU, while the latter is designed for a many-core graphics processing unit. For each version of these algorithms, several tests have been performed on a large database containing both synthetic and real hyperspectral images. In this way, scientists can integrate the proposed suite of efficient BP algorithms into existing frameworks, choosing the most suitable technique for their specific applications.

Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model training, improving accuracy, or both. It reduces redundancy among spectral bands while trying to preserve the original information of the image. By now, many efforts have been made to develop unsupervised band selection approaches, of which the majorities are heuristic algorithms devised by trial and error. In this article, we are interested in training an intelligent agent that, given a hyperspectral image, is capable of automatically learning policy to select an optimal band subset without any hand-engineered reasoning. To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning. Once the agent is trained, it learns a band-selection policy that guides the agent to sequentially select bands by fully exploiting the hyperspectral image and previously picked bands. Furthermore, we propose two different reward schemes for the environment simulation of deep reinforcement learning and compare them in experiments. This, to the best of our knowledge, is the first study that explores a deep reinforcement learning model for hyperspectral image analysis, thus opening a new door for future research and showcasing the great potential of deep reinforcement learning in remote sensing applications. Extensive experiments are carried out on four hyperspectral data sets, and experimental results demonstrate the effectiveness of the proposed method. The code is publicly available.

This paper presents an approach to band selection fusion (BSF) which fuses bands produced by a set of different band selection (BS) methods for a given number of bands to be selected, nBS. Since each BS method has its own merit in finding the desired bands, various BS methods produce different band subsets with the same nBS. In order to take advantage of these different band subsets, the proposed BSF is performed by first finding the union of all band subsets produced by a set of BS methods as a joint band subset (JBS). Due to the fact that a band selected by one BS method in JBS may be also selected by other BS methods, in this case each band in JBS is prioritized by the frequency of the band appearing in the band subsets to be fused. Such frequency is then used to calculate the priority probability of this particular band in the JBS. Because the JBS is obtained by taking the union of all band subsets, the number of bands in the JBS is at least equal to or greater than nBS. So, there may be more than nBS bands, in which case, BSF uses the frequency-calculated priority probabilities to select nBS bands from JBS. Two versions of BSF, called progressive BSF and simultaneous BSF, are developed for this purpose. Of particular interest is that BSF can prioritize bands without band de-correlation, which has been a major issue in many BS methods using band prioritization as a criterion to select bands.

A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis. To address this issue, band selection is considered as an effective approach that removes redundant bands for HSI. Recently, many band selection methods have been proposed, but the majority of them have extremely poor accuracy in a small number of bands and require multiple iterations, which does not meet the purpose of band selection. Therefore, we propose an efficient clustering method based on shared nearest neighbor (SNNC) for hyperspectral optimal band selection, claiming the following contributions: (1) the local density of each band is obtained by shared nearest neighbor, which can more accurately reflect the local distribution characteristics; (2) in order to acquire a band subset containing a large amount of information, the information entropy is taken as one of the weight factors; (3) a method for automatically selecting the optimal band subset is designed by the slope change. The experimental results reveal that compared with other methods, the proposed method has competitive computational time and the selected bands achieve higher overall classification accuracy on different data sets, especially when the number of bands is small.

This paper presents the design and implementation of a new adaptive feature selection technique for spectral band selection prior to classification of remotely sensed hyperspectral images. This approach integrates spectral band selection and hyperspectral image classification in an adaptive fashion, with the ultimate goal of improving the analysis and interpretation of hyperspectral imaging. The four components in the proposed adaptive feature selection, including local gradient calculation, reference cluster determination, prototype classes building using a fuzzy classifier, and relevant bands selection are presented in detail. The hyperspectral image data set from the ROSIS (Reflective Optics System Imaging Spectrometer) were used as training and testing data. We tested the effect of the approach on different number of selected spectral bands. The classification accuracy for AFS was illustrated by the ROC curve. In addition, in order to compare the proposed method with other methods, we applied the proposed adaptive feature selection (AFS) approach and the principal component analysis (PCA) method to the GentleBoost classifier using different number of spectral bands after processing the ROSIS Pavia scene. The experimental results demonstrated that the classification accuracies obtained by the AFS method are higher than that of the PCA method. In addition, for each method, the higher the number of spectral bands, the higher the classification accuracy.

Hyperspectral images belong to high-dimensional data having a lot of redundancy information when they are directly used to classification. One of the most problems in hyperspectral image classification is the Hughes phenomenon caused by the irrelevant spectral bands and the high correlation between the adjacent bands. The problematic is how to find the relevant bands to classify the pixels of hyperspectral image without reducing the classification accuracy rate. This study investigated two band selection methods ( MIFS-U and NMIFS) to classify hyperspectral data. The experimentations are performed on widely used benchmark AVIRIS hyperspectral dataset. Comparisons with the state-of-the-art approach are also conducted. The analysis of the results proves that MIFS-U and NMIFS algorithms can effectively investigate the spectral band selection problem and provides a high classification accuracy rate by using a few samples for training. The retained classifier is the Support Vectors Machine (SVM).

Hyperspectral image (HSI) has become one of the most important remote sensing techniques for object interpretation by its abundant band information. As the data dimension increases, band selection technique is utilized to achieve the highest possible classification accuracy with fewer bands. Essentially, it is considered as an NP-hard problem, which is a non-deterministic problem within polynomial complexity and difficult to achieve a satisfactory solution using traditional search methods. Sine cosine algorithm (SCA) is a recently developed swarm intelligence algorithm based on the calculation of sine and cosine functions. To determine the parameters setting in SCA, Lévy flight technique is employed to improve the exploitation phase of the algorithm. In the paper, a new band selection method on the basis of SCA with a Lévy flight is proposed, and an alternative distribution is utilized to decrease the band dimension of HSI. In addition, crossover operation is conducted to enhance the optimal bits of each individual, and an evaluation criterion for assessing the performance of band selection is defined, allowing the classification accuracy and selected number of bands to be as balanced as possible. Experimental results demonstrate that the proposed band selection method is superior to other state-of-the-art approaches in terms of band subsets that achieve higher classification accuracy.