Hyperspectral Imagery Datasets Research Articles

Three-dimensional singular spectrum analysis for precise land cover classification from UAV-borne hyperspectral benchmark datasets

The precise classification of land covers with hyperspectral imagery (HSI) is a major research-focused topic in remote sensing, especially using unmanned aerial vehicle (UAV) systems as the abundant data sources have brought severe intra-class spectral variability and high spatial heterogeneity challenges, making precise classification difficult. To this end, a novel three-dimensional singular spectrum analysis (3DSSA) method is proposed for the 3D feature extraction of HSI. It aims to construct a low-rank trajectory tensor containing global and local features and extract both spectral discrimination features and spatial contextual features in conjunction with tensor singular value decomposition (t-SVD). To reduce the risk of tensor operations exceeding memory on large-scale HSI data, the extended regional clustering (RC) 3DSSA framework (RC-3DSSA) is proposed for precise HSI classification. RC-3DSSA uses RC processing to alleviate the scale diversity and further applies 3DSSA to tackle issues of intra-class spectral variability and spatial heterogeneity. In order to effectively evaluate the performance of RC-3DSSA, a new challenging classification dataset namely the Qingdao UAV-borne HSI (QUH) dataset was further built. It consists of three sub-datasets: QUH-Tangdaowan, QUH-Qingyun, and QUH-Pingan, which are freely available as benchmarks for precise land cover classification. The experimental results on QUH and two publicly available datasets show that the RC-3DSSA can accurately distinguish ground objects and reliably map their distribution when benchmarked with ten state-of-the-art methods. Specifically, the overall accuracies achieved are 86.62%, 87.51%, and 87.35% under 10% spatially disjoint training samples for the three UAV-borne HSI datasets, respectively, providing the best performance.

Attention-Enhanced Generative Adversarial Network for Hyperspectral Imagery Spatial Super-Resolution

Hyperspectral imagery (HSI) with high spectral resolution contributes to better material discrimination, while the spatial resolution limited by the sensor technique prevents it from accurately distinguishing and analyzing targets. Though generative adversarial network-based HSI super-resolution methods have achieved remarkable progress, the problems of treating vital and unessential features equally in feature expression and training instability still exist. To address these issues, an attention-enhanced generative adversarial network (AEGAN) for HSI spatial super-resolution is proposed, which elaborately designs the enhanced spatial attention module (ESAM) and refined spectral attention module (RSAM) in the attention-enhanced generator. Specifically, the devised ESAM equipped with residual spatial attention blocks (RSABs) facilitates the generator that is more focused on the spatial parts of HSI that are difficult to produce and recover, and RSAM with spectral attention refines spectral interdependencies and guarantees the spectral consistency at the respective pixel positions. Additionally, an especial U-Net discriminator with spectral normalization is enclosed to pay more attention to the detailed informations of HSI and yield to stabilize the training. For producing more realistic and detailed super-resolved HSIs, an attention-enhanced generative loss is constructed to train and constrain the AEGAN model and investigate the high correlation of spatial context and spectral information in HSI. Moreover, to better simulate the complicated and authentic degradation, pseudo-real data are also generated with a high-order degradation model to train the overall network. Experiments on three benchmark HSI datasets illustrate the superior performance of the proposed AEGAN method in HSI spatial super-resolution over the compared methods.

Precise crop classification of UAV hyperspectral imagery using kernel tensor slice sparse coding based classifier

Precise crop classification plays a significant role in the agriculture field. An appropriate data source for precise crop classification is high spatial resolutions hyperspectral imagery (H2 imagery) acquired by unmanned aerial vehicle (UAV). However, for imagery with many different classes of crops, crop classification of UAV H2 imagery is a huge challenge. The significant spectral diversity, spatial heterogeneity and nonlinear data structure of UAV H2 imagery results in poor spectral discriminability. To improve the discriminability, a kernel tensor slice sparse coding-based classifier (KTSSCC) is proposed for precise crop classification of UAV H2 imagery in this research. The kernel tensor representation mechanism in KTSSCC can reduce the nonlinear separation while well preserving the spectral characteristics and spatial constraints of land-covers, and thus the discriminability is greatly improved. Furthermore, this paper puts forward the kernel tensor slice sparse orthogonal matching pursuit (KTSSOMP) algorithm to optimize kernel tensor slice sparse coding in the spectral space, which greatly reduces the computation cost. Moreover, there are very few parameters to be tuned in our proposed model. We assess the performance of KTSSCC on two real UAV hyperspectral imagery datasets, and find that, based on visual and quantitative results, it provides satisfactory crop classification results and outperforms the state-of-the-art approaches.

MSRA-G: Combination of multi-scale residual attention network and generative adversarial networks for hyperspectral image classification

Deep learning-based technology has been introduced to increase the classification accuracy of hyperspectral imagery (HSI). Nevertheless, it is still a challenging issue to derive a satisfying classification accuracy from limited training samples. A novel method (MSRA-G) that combines multi-scale residual attention (MSRA) with Generative Adversarial Networks (GANs) was proposed. In view of the low classification accuracy with limited training samples, the is first used to generate more separable synthetic samples. A network is then proposed to extract multi-scale context information for improving HSI classification. The proposed method constructs two multi-scale feature extraction modules to identify high-level spatial–spectral features based on the 3D–2D hybrid network. In addition, the residual connection mode and the attention mechanism are combined to establish the channel and spatial residual attention modules. Different weights are assigned to different features in the channel dimension and spatial dimension, and the features are selectively learned. Furthermore, to verify the performance of MSRA-G, experiments were carried out on three publicly available HSI datasets of Indian Pines, University of Pavia and Salinas Valley. The experimental results show that our proposed MSRA-G is superior to several popular classification models. It can still achieve satisfactory classification accuracies, even in the case of insufficient training samples.

Semi-supervised feature learning for disjoint hyperspectral imagery classification

With the introduction of spatial-spectral fusion and deep learning, the classification performance of hyperspectral imagery (HSI) has been promoted greatly. For some widely used datasets, the classification accuracy almost reaches 100%. However, for hyperspectral image classification, random sampling is still the most common strategy to collect the training and test samples. Because the training and test samples are randomly selected from the same image, so they have a high correlation and the classification results are overoptimistic. Besides, random sampling is not a good choice for practical applications because we cannot always collect training and test samples from the same region. Disjoint sampling selects training and testing samples from different local regions, which will provide a more objective performance evaluation for HSI classification models. In this paper, we first show the huge classification performance difference caused by different sampling strategies with a simple experiment, then we analyze the underlying reasons from the spectral information, spatial-spectral combination, sample overlapping and spatial distance, finally, a semi-supervised feature learning method is proposed for disjoint HSI classification, in which the spatial and spectral information are exploited effectively and reasonably. The experimental results based on three HSI datasets demonstrate the effectiveness of the proposed method.

Hyperspectral Target Detection via Spectral Aggregation and Separation Network With Target Band Random Mask

Hyperspectral target detection (HTD) is a pixel-wise detection method based on limited prior targets and spectral differences, which has been widely studied and applied in many fields. Recently, deep learning (DL) plays an important role in hyperspectral imagery (HSI) processing. However, for HTD, the severe lack of class-balanced training sets is an enormous challenge. Meanwhile, it is difficult to suppress backgrounds while highlighting targets through the deep network. To address these issues, we propose a spectral aggregation and separation network (SASN) with a target band random mask (TBRM) for HTD in this paper. For the training sets of SASN, a multifarious representative background selection strategy (MRBS) is first proposed to obtain a multifarious and representative background training set. Next, aiming at the notorious class imbalance, a data augmentation (DA) method, TBRM, is proposed to generate adequate target training set by repeating randomly zero-masking the spectral bands of a prior target. Subsequently, in the training of SASN, residual connection and squeeze-and-excitation (SE) channel attention mechanism are applied to fully extract high discriminative features and nonlinear ones in the spectra. Besides, to better separate the targets and backgrounds, a triplet-soft loss function is presented, which makes the training in the direction of spectral separation of background samples from both the prior target and target samples. During testing, the trained SASN distinguishes the spectral similarities and differences simultaneously for highlighting targets and suppressing backgrounds. Moreover, extensive experimental results validate that the proposed method has superior detection performances, background suppression capacity, and separability compared with ten cutting-edge HTD algorithms on six benchmark HSI datasets.

Unsupervised textural classification of rocks in large imagery datasets

• Rock imaging characterisation techniques capture composition and texture. • A new feature extraction method for rock texture from imaging data is documented. • A large dataset of hyperspectral rock images was classified using this new method. • The results prove a robust and repeatable approach for textural analysis. • Textural outputs can be incorporated in geometallurgical modelling. The value of rock characterisation, whether it is for mineral exploration , extraction, or concentration, lies in the ability to describe its composition and texture. In the past century, extensive technological developments have provided new opportunities to assess compositional properties, both geochemical and mineralogical, and at different scales: X-ray diffraction, X-ray fluorescence, inductively coupled plasma mass spectrometry , reflectance spectroscopy , etc. More recently, the advent of imaging characterisation techniques and high-performance computing power has enabled the assessment of mineral texture in a robust and quantitative manner. This study proposes and validates an end-user focused workflow for the identification of textural families in a large drill-core hyperspectral imagery dataset, based on a novel textural feature extraction method named Mineral Co-Occurrence Probability Field (MCOPF). This workflow combines vintage image textural assessment methods with modern machine learning techniques for the automated unsupervised classification of textures within a drill core hyperspectral imagery dataset. The results demonstrate a meaningful and robust identification of rock textural families (clusters), enabling a wide range of applications in geology, mining, and metallurgy in the academic and industrial sectors.

Hyperspectral Image Restoration via Auto-Weighted Nonlocal Tensor Ring Rank Minimization

Hyperspectral Imagery(HSI) restoration is a fundamental problem as a preprocessing step. In this letter, we present a novel Auto-weighted Nonlocal Tensor Ring Rank Minimization (ANTRRM) to reduce noise in HSI. Firstly, Nonlocal Cuboid Tensorization(NCT), built by similar grouping cuboids in HSI data, exploits the nonlocal self-similarity and the spatial-spectral correlation simultaneously. Then, the proposed model introduces Nuclear Norm (NN) regularization via nonlocal tensor ring with mode-&#x007B;<i>d, l</i>&#x007D; unfolding. An auto-weighted optimization is employed to represent the different importance of TR unfolding. Finally, the Alternating Direction Method of Multipliers(ADMM) scheme is employed to solve the proposed model efficiently. Experiments on two simulation HSIs datasets and a real HSI dataset were carried out, compared with representative approaches in both visual and quantitative comparison. The proposed ANTRRM method is superior except in a few cases.

A Multiscale Superpixel-Level Group Clustering Framework for Hyperspectral Band Selection

Hyperspectral imagery (HSI) contains hundreds of bands, which provide a wealth of spectral information and enable better characterization of features. However, the excessive dimensionality also poses a dimensional disaster for subsequent processing. Fortunately, band selection (BS) gives a straightforward and effective way to pick out a subset of bands with rich information and low correlation. Although many hyperspectral BS methods, especially clustering-based ones, have been proposed by researchers in recent years, the contextual information of adjacent bands and the spatial structural information of materials are not well investigated. Therefore, in this article, a multiscale superpixel-level group-clustering framework (MSGCF) has been proposed for hyperspectral BS. Different from previous, a new superpixel-level distance measure is elaborately utilized to group and cluster the spectral bands, which jointly considers the spectral context and spatial structure information. Concretely, to preserve the spatial structural information of HSI, multiple superpixel segmentation is first performed to generate superpixel maps in multiscales, which enables complementarity of multiple superpixel segmentation algorithms and adaptation to diverse scales of land cover types. Second, the grouping and clustering paradigm is introduced to conduct the contextual information among bands. Here the maximum points of superpixel-level KL- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> distance of adjacent bands are adopted as partition points to separate bands into groups, which encourages adjacent bands with strong correlation to be divided into the same group. Third, a superpixel-level fast density-based clustering method (SuFDPC) with superpixel-level <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2, 1}$ </tex-math></inline-formula> distance is developed to select representative bands in every group. Finally, BS results are achieved with a ranking-based voting strategy by concerning information entropy and frequency of occurrence in a unified scheme. A series of ablation analyses and experimental comparisons on four real HSI datasets have been conducted, as well as similarity comparisons for the selected bands. The experimental results consistently demonstrated the effectiveness of our MSGCF approach. The codes of this work will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">http://jiasen.tech/papers/</uri> for the sake of reproducibility.

A Divisive Hierarchical Clustering Approach to Hyperspectral Band Selection

Band selection, considered as an effective dimensionality reduction technique for hyperspectral imagery (HSI), has become a hot topic for decades. Although various clustering-based methods have been applied to band selection, only a few studies explored the hierarchical structure among different spectral bands. And with regard to conventional hierarchical clustering, implemented in an agglomerative manner, both efficiency and accuracy of band selection still remain to rise. Moreover, the noise sensitivity is a defect inherent in the procedure of clustering. To address these issues, we propose a divisive hierarchical clustering approach (DHCA) to hyperspectral band selection. Inspired by divisive analysis, DHCA is designed to obtain any number of band subsets, which captures the intrinsic hierarchy of hyperspectral bands simultaneously. By introducing the local density into average dissimilarity, it can suppress the outliers clustering separately. Also, given the order of the spectrum, channel interval makes the similarity more rational among bands. Finally, we select a representative band in each cluster from the information viewpoint to ensure the band subset with a high quality. Extensive experiments on three real public HSI datasets fully validate the superiority of the proposed method against state-of-the-art competitors.

Bayesian Subpixel Mapping of Hyperspectral Imagery via Discrete Endmember Variability Mixture Model and Markov Random Field

Although Bayesian methods have been very effective for spatial-spectral analysis of hyperspectral imagery (HSI), they had not been fully explored for enhanced sub-pixel mapping (SPM) by simultaneously considering several key issues i.e., endmember variability, the discrete nature of subpixel class labels and the spatial information in HSI. Therefore, we propose a new Bayesian SPM method based on discrete endmember variability mixture model (DEMM) and Markov random field (MRF), which has three main characteristics. First, DEMM allows the advanced SPM by completely accounting for the endmember-abundance patterns of each pixel to accommodate the endmember variability, the discrete hidden class label field of subpixels while taking into account the noise heterogeneity effect. Second, the discrete class label field modeled by MRF together with the DEMM, which can be integrated into a novel Bayesian model to better exploit the spatial contextual and spectral information. Third, the resulting Bayesian model can be efficiently solved by a designed expectation-maximization (EM) iteration, where E-step estimates the subpixel class label field using a simulated annealing (SA) algorithm and M-step estimates the endmembers for each pixel in HSI using alternating nonnegative least squares (ANLS) approach. The experimental results on three HSI datasets demonstrate that the proposed approach outperforms previously available SPM techniques.

Tree crown segmentation and species classification in a wet eucalypt forest from airborne hyperspectral and LiDAR data

ABSTRACT To sustainably manage forest biodiversity and monitor changes in species patterning, mapping the spatial distribution of tree species is indispensable. Remote sensing can provide powerful tools for mapping species, but this task is complex in areas with high plant diversity and multi-layered canopies. This paper addresses the issue of classifying wet eucalypt forest plants by examining tree crown segmentation and species classification using different combinations of remote sensing datasets against mapped tree locations. This study explores optimal segmentation parameters for tree crown delineation compared to manually digitized tree crowns. The best segmentation accuracy of 88.71%, resulted from segmenting a combined Minimum Noise Fraction (MNF) dataset derived from hyperspectral imagery (HSI) and a LiDAR-derived Canopy Height Model (CHM). Object-based classification of tree species was performed using a random forest classifier. The fused dataset of MNF and CHM produced the highest overall accuracy of 78.26% for four vegetation classes, while the fused HSI, indices, and CHM performed best (66.67%) with five vegetation classes. However, both approaches had a high overall performance. The CHM contributed to tree crown segmentation and species classification accuracy, and fused datasets were more robust to spatially discriminate wet eucalypt forest species compared to a single dataset. Eucalyptus obliqua was classified with the highest accuracy of 90.86% for four classes using the fused MNF and CHM dataset, and 86.11% for five classes using the fused HSI, indices, and CHM dataset. An important understorey species – the tree fern (Dicksonia antarctica) – was classified with the highest accuracy of 83.54% for four classes using HSI. Therefore, fusing hyperspectral and LiDAR data could classify both the overstorey and dominant understorey species, and thus play a crucial role in identifying forest biological diversity. This approach will be useful for forest managers and ecologists to plan sustainable management of eucalypt forest biodiversity and produce maps for monitoring species of interest.

A spectral-spatial attention aggregation network for hyperspectral imagery classification

ABSTRACT For the classification of hyperspectral imagery (HSI), the convolutional neural network (CNN) can learn the discriminative spatial-spectral information of the image better than the traditional classification methods. However, when CNN uses the local receptive field to extract the features of HSI, it may cause the feature expression of the same pixel on the feature map to be inconsistent, and eventually cause noise in the classification results. To overcome this, we introduce the attention mechanism in the CNN model to improve the feature expressiveness. A spectral-spatial attention aggregation network (SSAAN) for HSI classification is designed, and there are two attention branches in our method. The spectral attention module with the squeeze-and-excitation (SESAM) automatically obtains the importance of each feature channel of HSI, and then enhances the useful band features and suppresses the less-useful band features according to this importance. In the spatial attention module with selective kernel (SKSAM), first, different convolution kernels of 2D-CNN are used to extract the shallow-middle-deep layer features from the principal components after dimension reduction, and the pixel spatial information from the three paths is combined and aggregated. Then, the feature maps of kernels of different sizes are aggregated according to the selection weights. Finally, the feature vectors obtained from the two branches of the spatial attention module and the spectral attention module are connected to further improve feature representation, and the classification result is obtained by the softmax function. Experimental results through three real HSI data sets show that our proposed method SSAAN achieves better performance compared to the state-of-the-art methods.

Leveraging colour-based pseudo-labels to supervise saliency detection in hyperspectral image datasets

Saliency detection mimics the natural visual attention mechanism that identifies an imagery region to be salient when it attracts visual attention more than the background. This image analysis task covers many important applications in several fields such as military science, ocean research, resources exploration, disaster and land-use monitoring tasks. Despite hundreds of models have been proposed for saliency detection in colour images, there is still a large room for improving saliency detection performances in hyperspectral imaging analysis. In the present study, an ensemble learning methodology for saliency detection in hyperspectral imagery datasets is presented. It enhances saliency assignments yielded through a robust colour-based technique with new saliency information extracted by taking advantage of the abundance of spectral information on multiple hyperspectral images. The experiments performed with the proposed methodology provide encouraging results, also compared to several competitors.

Hyperspectral Anomaly Detection via Integration of Feature Extraction and Background Purification

Anomaly detection (AD) has become a hotspot in hyperspectral imagery (HSI) processing due to its advantage in detecting potential targets without prior knowledge, and a variety of algorithms are proposed for a better performance. However, they usually either fail to extract intrinsic features underlying HSIs, or suffer from the contamination of noise and anomalies. To address these problems, we propose a new anomaly detector by integrating fractional Fourier transform (FrFT) with low rank and sparse matrix decomposition (LRaSMD). First, distinctive features of HSI data are extracted via FrFT. Then, row-constrained LRaSMD (RC-LRaSMD), which is more practical and stable than the traditional LRaSMD, is employed to separate background from noise and anomalies. Finally, we implement an atom-selection strategy to construct the background covariance matrix for detection. The experimental results with several HSI data sets demonstrate satisfying detection performance compared with other state-of-the-art detectors.

Small sample classification for hyperspectral imagery using temporal convolution and attention mechanism

ABSTRACT Recently, deep learning models have achieved remarkable results in hyperspectral imagery (HSI) classification. However, most deep learning models tend to struggle due to the serious overfitting problem under the condition of small sample. For this purpose, we design an end-to-end classification framework and optimize it with the idea of meta-learning, to improve the accuracy and robustness of HSI small sample classification. Specifically, the embedding network based on 3D convolution is used to extract spatial-spectral features in HSI, the temporal convolution is adopted to analyse the abstract relationship between feature vectors by aggregating contextual information, and the attention mechanism is introduced to pinpoint the critical information conducive to classification, so as to further improve the classification accuracy. We adopt the typical mechanism of ‘meta-training + fine-tuning’ to optimize the designed model, enabling the model to acquire stronger generalization ability through a large number of different tasks. Experiments based on three public HSI data sets demonstrate that our method outperforms the regular deep learning models under the condition of small sample.

Robust Background Feature Extraction Through Homogeneous Region-Based Joint Sparse Representation for Hyperspectral Anomaly Detection

Various existing anomaly detection (AD) technologies focus on the background feature extraction and suppression, which serves as a crucial step to extrude anomalies from the hyperspectral imagery (HSI). In this article, motivated by the advantages of the joint sparse representation (JSR) model for adaptive background base selection, a robust background feature extraction method through homogeneous region-based JSR is proposed and used for AD. By segmenting the scene from the spatial domain through an eight-connected region division operation based on the clustering result, a series of nonoverlapping homogeneous regions each sharing a common sparsity pattern are obtained. After discarding small regions, JSR is performed on each region with the dictionary constituted by the overall spectral items in the corresponding cluster. By calculating the usage frequency of dictionary atoms, the most representative background bases describing each background cluster are adaptively selected and then combined into the global background bases. In addition, considering the interference of noise on detection accuracy, an energy deviation-based noise estimation strategy is presented by analyzing the residual obtained from JSR. Finally, the anomaly response of each pixel is measured by comparing its projection energy obtained from the background orthogonal subspace projection with the noise energy in its corresponding region. The proposed method overcomes the shortcomings of traditional neighborhood-based JSR in the common sparsity pattern and anomaly proportion. The spatial characteristics of HSI are fully explored. Furthermore, the interference of noise on detection accuracy is eliminated. Experiments on four HSI data sets demonstrate the superiority of the proposed method.

Stochastic Perturbations on Low-Rank Hyperspectral Data for Image Classification

Hyperspectral imagery (HSI) contains hundreds of narrow contiguous bands of spectral signals. These signals, which form spectral signatures, provide a wealth of information that can be used to characterize material substances. In recent years machine learning has been used extensively to classify HSI data. While many excellent HSI classifiers have been proposed and deployed, the focus has been more on the design of the algorithms. This paper presents a novel data preprocessing method (LRSP) to improve classification accuracy by applying stochastic perturbations to the low-rank constituent of the dataset. The proposed architecture is composed of a low-rank and sparse decomposition, a degradation function and a constraint least squares filter. Experimental results confirm that popular state-of-the-art HSI classifiers can produce better classification results if supplied by LRSP-altered datasets rather than the original HSI datasets.

Spectral Super-Resolution Network Guided by Intrinsic Properties of Hyperspectral Imagery.

Hyperspectral imagery (HSI) contains rich spectral information, which is beneficial to many tasks. However, acquiring HSI is difficult because of the limitations of current imaging technology. As an alternative method, spectral super-resolution aims at reconstructing HSI from its corresponding RGB image. Recently, deep learning has shown its power to this task, but most of the used networks are transferred from other domains, such as spatial super-resolution. In this paper, we attempt to design a spectral super-resolution network by taking advantage of two intrinsic properties of HSI. The first one is the spectral correlation. Based on this property, a decomposition subnetwork is designed to reconstruct HSI. The other one is the projection property, i.e., RGB image can be regarded as a three-dimensional projection of HSI. Inspired from it, a self-supervised subnetwork is constructed as a constraint to the decomposition subnetwork. These two subnetworks constitute our end-to-end super-resolution network. In order to test the effectiveness of it, we conduct experiments on three widely used HSI datasets (i.e., CAVE, NUS, and NTIRE2018). Experimental results show that our proposed network can achieve competitive reconstruction performance in comparison with several state-of-the-art networks.

A Search Method for Optimal Band Combination of Hyperspectral Imagery Based on Two Layers Selection Strategy

A band selection method based on two layers selection (TLS) strategy, which forms an optimal subset from all-bands set to reconstitute the original hyperspectral imagery (HSI) and aims to cost a fewer bands for better performances, is proposed in this paper. As its name implies, TLS picks out the bands with low correlation and a large amount of information into the target set to reach dimensionality reduction for HSI via two phases. Specifically, the fast density peaks clustering (FDPC) algorithm is used to select the most representative node in each cluster to build a candidate set at first. During the implementation, we normalize the local density and relative distance and utilize the dynamic cutoff distance to weaken the influence of density so that the selection is more likely to be carried out in scattered clusters than in high-density ones. After that, we conduct a further selection in the candidate set using mRMR strategy and comprehensive measurement of information (CMI), and the eventual winners will be selected into the target set. Compared with other six state-of-the-art unsupervised algorithms on three real-world HSI data sets, the results show that TLS can group the bands with lower correlation and richer information and has obvious advantages in indicators of overall accuracy (OA), average accuracy (AA), and Kappa coefficient.