EMVCC: Enhanced Multi-View Contrastive Clustering for Hyperspectral Images

Authors: fulin  luo
Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-04-08 17:42:27

Abstract: Cross-view consensus representation plays a critical role in hyperspectral images (HSIs) clustering. Recent multi-view contrastive cluster methods utilize contrastive loss to extract contextual consensus representation. However, these methods have a fatal flaw: contrastive learning may treat similar heterogeneous views as positive sample pairs and dissimilar homogeneous views as negative sample pairs. At the same time, the data representation via self-supervised contrastive loss is not specifically designed for clustering. Thus, to tackle this challenge, we propose a novel multi-view clustering method, i.e., Enhanced Multi-View Contrastive Clustering (EMVCC). First, the spatial multi-view is designed to learn the diverse features for contrastive clustering, and the globally relevant information of spectrum-view is extracted by Transformer, enhancing the spatial multi-view differences between neighboring samples. Then, a joint self-supervised loss is designed to constrain the consensus representation from different perspectives to efficiently avoid false negative pairs. Specifically, to preserve the diversity of multi-view information, the features are enhanced by using probabilistic contrastive loss, and the data is projected into a semantic representation space, ensuring that the similar samples in this space are closer in distance. Finally, we design a novel clustering loss that aligns the view feature representation with high confidence pseudo-labels for promoting the network to learn cluster-friendly features. In the training process, the joint self-supervised loss is used to optimize the cross-view features. Abundant experiment studies on numerous benchmarks verify the superiority of EMVCC in comparison to some state-of-the-art clustering methods. The codes are available at https://github.com/YiLiu1999/EMVCC.

Main paper: 
 introduction: 
 Unlike traditional grayscale or RGB images, hyperspectral image (HSI) is imaged with one spectral dimension and two spatial dimensions. The spatial dimensions can provide the spatial characteristics of scenes, such as structure and texture. In particular, the spectral dimension captures fine spectral characteristics of scenes with tens to hundreds of continuous spectral bands and reveals the unique spectral fingerprint. These distinctive characteristics have enabled the wide application of HSI in the fields of mineral exploration [10] , vegetation monitoring [49] , and military reconnaissance [40] . High-precision interpretation of HSI always relies on abundant high-quality labeled data [12, 14, 25] . However, in practice, large amounts of data labeling are often laborious, expensive, and impractical [8, 9] . Unsupervised learning methods, especially data clustering, have received widespread attention due to their unique value in exploring the intrinsic structure and hidden patterns of unlabeled data.In particular, HSI clustering methods distinguish and label pixels based on their inherent similarity, maximizing intra-class and minimizing inter-class similarity [4] . As a powerful unsupervised machine learning paradigm, self-supervised learning aims to learn a representation which is beneficial to various downstream tasks [29, 30, 41] without any prior knowledge. The mainstream of selfsupervised learning is the contrastive learning with dual-branch, and the self-supervised contrastive learning generates data representations by treating augmented views of data as positive samples. These models are updated by encouraging the positive samples close to each other and the negative samples to move away from each other. For example, SimCLR [5] learns feature representation by maximizing the consistency between different augmented views of the same data samples. BYOL [11] relies on interaction and mutual learning online. MoCo [17] facilitates unsupervised contrastive learning by constructing a dynamic dictionary with queues. SwAV [3] enforces consistency between cluster assignments from different views of the same image. Contrastive learning has also been widely used in HSI clustering [13, 15] . However, although these methods have achieved good results, the collision problem [1] of contrastive learning remains unsolved. Meanwhile, a single neighbor pixel of HSI can make this problem more prominent due to extremely similar spatial features.To address this challenge in contrastive learning, some studies [24, 28, 34, 39, 45] proposed to combine multi-view clustering with contrastive learning by integrating diverse and complementary information between different views. CMSCGC [15] proposed a novel deep multi-view subspace clustering algorithm to learn textural and spectral-spatial information. SSMLC [37] proposed a correlation coefficient-based spectral delineation method to generate multiple spectrum-views to obtain a consistent representation of HSI. GoMIC [7] proposed a self-supervised multi-view image clustering technique under contrastive heterogeneous graph learning to reveal intra-class and inter-class relationships in data. [20] designed a two-branch dense spectrum-spatial network for HSI clustering to extract spectral and spatial features separately. However, most existing methods tend to focus on the consistency of the same samples in different views, and the potential for similar but distinct views in the cross feature domain is often overlooked [50] . In this study, we propose a novel Enhanced Multi-view Contrastive Clustering (EMVCC), as illustrated in Figure 1 , for extracting cross-view consensus representation of HSI. Unlike natural images, HSIs have rich spectral information. In natural images, multiviews are obtained by data augmentation methods. In EMVCC, we use the spectral segmentation method to obtain multi-view spatial features. Different from existing multi-view constrative clustering methods that exploit the coherence goal of latent features to explore common semantics of all views. Considering the high similarity of information of neighbor heterogeneous spatial-multi-view, it is difficult for general contrastive models to regard them as negative sample pairs. Therefore, EMVCC utilizes Transformer [43] to extract the global correlation information from the spectrum-view to enhance the difference of spatial-multi-view of neighbor samples. This provides a novel paradigm for follow-up work in using crossscene view features to reduce the homogeneity between singlescene views. After that, EMVCC optimizes the extracted consensus representation using self-supervised joint loss, which efficiently explores the common semantics in all views to obtain a discriminative "cluster-friendly" consensus representation. Compared with existing clustering methods, extensive experiments on four HSI datasets have demonstrated the effectiveness and superiority of our proposed EMVCC.The main contributions of this study can be summarized as follows.• We propose a novel Enhanced Multi-view Contrastive Clustering (EMVCC), which utilizes corss feature domain views to reduce homogeneity between single scene views to extract a cross-view consensus representation of HSI.• We design a Self-supervised Joint Loss to efficiently explore the common semantics in all views to obtain distinctive clustering-friendly consensus representation.• We allocate the samples based on the similarity between attribute features and cluster centers, allowing the network to output data labels directly without post-processing.• We conduct comprehensive experiments on HSI to validate the effectiveness of Self-supervised Joint Loss and the proposed network. 
related work 2.1 contrastive learning: 
 As one of the most effective self-supervised methods, contrastive learning aims to learn data representation by comparing the similarity between positive and negative samples. With the principle of pulling positive pairs close and promoting negative pairs far, contrastive learning methods, such as InfoNCE [33] , SimCLR [5] , MoCo [17] , BYOL [11] , SwAV [3] have been widely studied in computer vision.In Multi-view clustering (MVC) [15, 16, 50] , the idea of contrastive learning is utilized to compare the similarity and difference between different views to improve data representation and cluster performance. CMC [38] found that the more views are introduced, the better the generated representation can capture the semantic information of the underlying scene information. DealMVC [50] proposed a dual contrastive calibration mechanism to keep the consistency of similar samples while to make full use of the diversity of multi-view information for different samples in cross-scene view. MFLVC [45] introduced two objectives for the multi-view clustering of high-level features and the generation of pseudo labels, employing contrastive learning.MVC has attracted increasing attention due to its excellent performance in the unsupervised domain in recent years. MVC completes the knowledge extraction task by fusing complementary and consensus information from each view. Existing multi-view clustering approaches mainly include four types: graph-based MVC, multiple kernel-based MVC, subspace-based MVC, and scalable-based MVC.The graph-based MVC [22, 23] utilizes graph topology and connectivity between nodes to capture the intrinsic feature and the associative information of the data to improve the clustering performance. [32] proposed an auto-weighted multiple graph learning (AMGL) framework to learn a set of weights automatically for all the graphs. The multiple kernel-based MVC [6] is able to capture the nonlinear structure inherent in many real-world data. [27] proposed a multiple kernel k-means (MKKM) clustering with a matrix-induced regularization to reduce redundancy and enhance the diversity of the selected kernels. The subspace-based MVC [36, 44] assumes that data are usually located in a subspace of low dimension embedding. [31] designed a subspace representation learning method that simultaneously learned a view-consistent representation and a set of view-specific representation for multi-view subspace clustering. [52] proposed a latent multi-view subspace clustering (LMSC) method, which clustered data points with latent representation. The ensemble-based MVC integrates clustering results from different views to obtain the final clustering results. The scalable-based MVC is a clustering method for processing largescale data sets. [21] proposed a scalable and parameter-free graph fusion framework for multi-view clustering. [54] proposed a scalable multiplex network embedding model to represent information on multi-type relations in a unified embedding space.The above methods can fully utilize the information of multiple views, but they do not capture shallow representation of multi-view data, and the discriminative ability of the obtained representation is thus limited. Benefiting from robust feature representation, deep networks [19, 45, 51, 53] have the ability to extract finer feature representation. Through deep multi-view clustering, potential useful consensus representation of multi-view data can be efficiently discovered for improving data clustering performance. 
method: 
 This section proposes a novel enhanced multi-view contrastive clustering, termed EMVCC. The framework is shown in Figure 2 .For multi-view data, it is significant to make good use of the information from different views. Obviously, different bands in the HSI can be considered as different views because they can reflect different properties of the same scene. To construct multi-view data, we use spectral segmentation to construct multi-view by treating spectral features as different views, and we use Principal Component Analysis (PCA) [35] to reduce the dimensionality of the divided data. Subsequently, a sliding window is used to extract the pixel and its adjacent neighbor pixels, and the data points are represented as patches. Finally, the patches are transformed by using multiple data augmentation methods (such as cropping, flipping, rotating, and shearing) to generate multi-view features along the spatial dimension. Therefore, we can obtain multi-view features {𝑋 𝑣 } 𝑉 𝑣=1 , where 𝑋 𝑝 ∈ 𝑅 𝑁 ×𝑝 ×𝑝 ×𝑑 , 𝑝 is the patch size, 𝑑 is the dimension of the spatial-view. In other branch, we use the spectral information of the pixels as additional spectrum-view data 𝑆 ∈ 𝑅 𝑁 ×𝑑 , 𝑑 is the dimension of the spectrum-view.To fully utilize the spatial spectral information of HSI, we designed a two-branch deep neural network. The feature extraction module maps multi-view samples to feature vectors.In the spatial model, we use ResNet [18] as the basic feature extractor. In the {𝑋 𝑣 } 𝑉 𝑣=1 spatial-view, the spatial information of each location is included. By ResNet, the complex spatial features in the views can be effectively captured. We make 𝑥 1 𝑖 and 𝑥 2 𝑖 denote the first view and second view of spatial-view respectively. In fact, the spatial model is equivalent to a nonlinear embedding function 𝑓 𝑅 (•), which maps the inputs 𝑥 1 𝑖 and 𝑥 2 𝑖 to deep features ℎ𝑅 1 𝑖 and ℎ𝑅 2𝑖 . The formula is as follows.EQUATIONwhere ℎ𝑅 𝑣 𝑖 ∈ 𝑅 𝑁 ×𝑏 is the output after the average pooling layer. Then, we use a MLP with one hidden layer as the projection head 𝑝𝑟𝑜 𝑗 (•) and the classification head 𝑝𝑟𝑒𝑑 (•) respectively, and map the representation ℎ𝑅 𝑣 𝑖 into the contrastive loss space and classification space respectively, so as to obtain𝑧𝑅 𝑣 𝑖 = 𝑝𝑟𝑜 𝑗 (ℎ𝑅 𝑣 𝑖 ) = 𝜎 (𝑊 𝑃 • ℎ𝑅 𝑣 𝑖 + 𝑏 𝑃 ) and 𝑐𝑅 𝑣 𝑖 = 𝑝𝑟𝑒𝑑 (ℎ𝑅 𝑣 𝑖 ) = 𝜎 (𝑊 𝐶 • ℎ𝑅 𝑖 + 𝑏 𝐶 ),where 𝜎 is the ReLU nonlinearity and the 𝑊 and 𝑏 are the weight and bias parameters.In the spectrum model, considering that the spectral data of each pixel from the HSI changes continuously, the global relationship is crucial to understand the entire spectrum-view. Therefore, we use Transformer [43] to extract global relevant information of the spectrum-view. We use 𝑆 𝑖 to represent the samples in the spectral dimension. In the standard Transformer architecture, the architecture consists of multi-head self-attention and MLP blocks. Furthermore, a layer-norm operation is employed before each block and residual connections are merged after each block. Transformer applies a self-attention layer to model the global relationship between the input embeddings, which improves the global modeling ability of the spectral information in the HSI data. The formula is as follows.EQUATIONFor each pixel in HSI, adjacent pixels of different spectral channels are strongly correlated, and adjacent pixels often belong to the same object (especially for very high-resolution data). However, adjacent pixels that are not in the same category also have similar spatial characteristics. General contrastive loss directly utilizes the data representation obtained by these spatial features, and these samples will be regarded as false negative pairs. In order to enhance the similarity between the positive samples and maximize the difference between the negative samples, we used the extracted data representation ℎ𝑇 𝑖 from the spectral module to strengthen the extracted data representation 𝑧𝑅 𝑣 𝑖 from the spatial model, and the subsequent experiments have demonstrated the effectiveness of this approach. The consensus representation 𝑧 𝑣 𝑖 is expressed as follows.EQUATIONwhere 𝑝𝑟𝑜 𝑗 (•) denotes the projection head used after the Transformer block. In order to retain sufficient categorization information in the classification space, we fuse the label prediction probabilities of multi-view as follows.EQUATIONwhere 𝑝𝑟𝑒𝑑 (•) denotes the classification head used after the Transformer block.For data clustering, we employ the self-supervised loss to effectively integrate both the spatial model and the spectrum model for joint training. Our overall loss function is defined as follows:EQUATIONwhere 𝛼 ≥ 0 and 𝛽 ≥ 0 are two hyperparameters to balance the contribution between L 𝐹 𝐸 and L 𝑐 𝑓 .Contrastive learning can learn representation from unlabeled data. It can sample positive pairs, which should be similar samples with similar representation. We use SimCLR [5] , which learns representation by maximizing consistency between different augmented views of the same data samples with contrastive loss. More specifically, the positive pair in our network is usually obtained by taking two views of the same sample and then yielding 𝑧 2𝑘 -1 and 𝑧 2𝑘 for contrastive loss, the contrastive loss L 𝑁𝑇 between a pair of positive cases 𝑖 and 𝑗 is as follows:EQUATIONwhere 𝑠𝑖𝑚(•, •) is cosine similarity between two vectors, and 𝜏 is a temperature scalar.Loss. We apply multi-view consistent contrastive learning for the consistency representation to distinguish samples more clearly in the feature space. Based on this, we adopt and improve the probabilistic contrast loss function of DCEnet [30] , which can stabilize the model and find the boundaries of different classes by constraining the output of the siamese branch based on cosine similarity.EQUATIONwhere 𝑆𝑡𝑜𝑝𝐺𝑟𝑎𝑑 (•) is a common technique used to implement parameter sharing between branches and facilitate successful contrastive learning. This technique typically involves stopping the gradient propagation in one branch while only receiving gradient information from the other branch when computing the similarity between the outputs of the two branches. 𝐷 (•, •) represents the calculation of cosine similarity, which can be defined as follows.EQUATIONwhere ∥•∥ 2 is ℓ 2 -norm. Since the data representation obtained by contrastive learning are not specifically designed for clustering, the use of these representation features directly makes the clustering effect unsatisfactory. For this reason, we design a novel clustering loss as in Figure 3 that aligns consensus representation of view features with high-confidence pseudo-labels to learn clustering-friendly representation that captures similarity between instances. Its expression is as follows.EQUATIONwhere 𝐶𝐸 denotes the use of a cross-entropy loss function, 𝜇 𝑖 is 𝑖th cluster center, and 𝐴𝑆𝑁 operation is used to preprocess the output of model to make it more suitable for subsequent clustering tasks. More specifically, we first use 𝑁𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒 to make the distribution of the representation smoother, helping the model learn more discriminative and semantically informative representation. Then, 𝑆𝑜 𝑓 𝑡𝑚𝑎𝑥 is used to convert the raw scores into probability distribution to reduce the differences between the representation. Finally, 𝐴𝑟𝑔𝑚𝑎𝑥 is used to convert the model's output into pseudo-labels. 
experiments: 
 In this section, four benchmark datasets are used to analyze our EMVCC. Subsequently, the experimental results are elaborated in depth through a detailed analysis.4.1.1 Benchmarks. Since HSIs have too many bands and are difficult to process, the general approach uses dimensionality reduction or band selection to reduce the number of bands for clustering.However, it will cause information loss, because different bands contain different land-cover information. Therefore, we simply split spectral channels in half to create multi-view data, preserving key spectral information and providing diverse views to achieve the contrast of data. In experiments, we adopt the same multi-view data input for the other methods. To ascertain the efficacy of our EMVCC, we conducted comprehensive experiments on the four HSI datasets: Salinas, Botswana, Indian_Pines, and Houston. Table 1 provides a brief overview of these datasets. To quantify performance, we utilize a serial of clustering performance metrics, including overall accuracy (OA), Fowlkes-Mallows index (FMI), adjusted rand index (ARI), adjusted mutual information (AMI), and normalized mutual information (NMI), as well as employing purity and kappa coefficient (Kappa) to evaluate the clustering performance on the datasets. Across all these methods, the number of clusters is set by referencing the ground truth in each dataset. Subsequently, we evaluated clustering validity of EMVCC in comparison to several state-of-the-art (SOTA) models, including MFLVC (CVPR 2022) [46] , GCFAgg (CVPR 2023) [47] , ACCMVC (TNNLS 2024) [48] , CMSCGC (TGRS 2024) [15] , GCOT (TGRS 2022) [26] , EKGCSC (TGRS 2020) [2] , DSCRLE (TGRS 2023) [55] and SDST (TGRS 2024) [29] . In the self-supervised joint loss, the hyperparameters 𝛼 and 𝛽 were set to 1 and 0.1. The model was trained by Adam optimizer with an initial learning rate of 0.0001, and the weight decay is 0.000001. The computational environment was conducted on a computer with an NVIDIA RTX A6000 48G GPU.4.2.1 Quantitative Evaluation. We compare EMVCC with six baselines, including traditional subspace clustering methods (GCOT [26] , EKGCSC [2] ), self-supervised deep clustering methods (DSCRLE [55] , SDST [29] ) and contrasting multi-view clustering methods (MFLVC [46] , GCFAgg [47] , ACCMVC [48] and CMSCGC [15] ). Table 2 lists the clustering performance of all compared methods on the four datasets. From these results, we make the following observations. Compared with these clustering algorithms, our EMVCC consistently achieves the most favorable clustering results in most datasets. For example, when examining Salinas' results, EMVCC outperforms the second best method by 1.29%, 1.95%, 9.13%, 2.83%, 2.22%, 2.90%, and 2.22% in OA, Kappa, Purity, ARI, AMI, FMI, and NMI, respectively. We speculate the reason is that these selfsupervised methods do not consider the multi-view information of data. Compared with deep multi-view clustering methods, our method also has obvious advantages, which also verifies that singleview data is difficult to provide sufficient information for clustering. In addition, the occurrence of false negative pairs also affects the clustering performance. In summary, the above observations confirm the superior performance of our proposed EMVCC. datasets. As can be seen from the plots, the regions acquired by our proposed EMVCC are more homogeneous and have the least number of misclassified pixels compared with all the other benchmark methods. This is especially evident in the regions where the mixed ground objects have similar spectral characteristics. Taking the Indian_Pines dataset as an example (see Figure 4 ), the pixels of "Soybean-mintill" and "Soybean-notill" land cover types are prone to be misclassified by most of methods due to their overly similar spectral features. Most notably, our method effectively distinguishes between these two objects. Our method employs an advanced multiview feature extraction method that effectively identifies and exploits relevant features in HSI data. By comparing the clustering plots, we show the difference in identifying the feature continuity of our method on the Indian Pine dataset. The clustering plots clearly show that our method has a significant advantage in terms of edge sharpness and continuity on the Indian_Pines as well as Salinas datasets. The classification plot shows that the proposed EMVCC significantly improves the discrimination of similar ground objects.In addition, we visualized the learned embedding distribution of EMVCC on the Indian_Pines and Botswana datasets by the t-SNE [42] . From the results in Figure 6 , we conclude that EMVCC is able to reveal the intrinsic clustering structure better than the original features. In EMVCC, the overall loss function primarily has two parameters: 𝛼 and 𝛽, which have a significant impact on the clustering performance. We use a grid search strategy to find the optimal 𝛼 and 𝛽 on the Indian_Pines and Botswana datasets. As can be seen from Figure 7 , the best clustering results are obtained when 𝛼 and 𝛽 are set to 1 and 0.1 respectively. Thus, by setting 𝛼 and 𝛽 to 1 and 0.1, our method achieves satisfactory performance. In this experiment, the impact of Spatial model, Spectrum model, Feature Enhanced Loss and Cluster-Friendly Loss of EMVCC is evaluated on the Indian_Pines dataset. The results are listed in Table 3. These methods are simplified from EMVCC-1 to EMVCC-6.The results reveal the necessity of each key component in EMVCC. Specifically, the clustering accuracy of EMVCC-6 is higher than that of EMVCC-1 and EMVCC-2, indicating that the spectrum-enhanced spatial-view feature is effective. Compared with EMVCC-3, the introduction of Feature Enhanced Loss enhances the consensus representation in EMVCC-4 producing better results for EMVCC, which confirms the important role of Feature Enhanced Loss. Similarly, the accuracy of EMVCC-5 is significantly better than that of EMVCC-3, demonstrating that the Cluster-Friendly Loss enables EMVCC to produce a feature representation that is more favourable to clustering. Finally, the OA value of EMVCC-6 exceeds that of EMVCC-1 to EMVCC-5, proving the superiority of EMVCC. Thus, the proposed Spectrum-Enhanced Spatial View Feature, Feature Enhanced Loss and Cluster-Friendly Loss both improve the clustering accuracy of EMVCC. 
conclusion: 
 In this paper, we design a novel Enhanced Multi-View Contrastive Clustering, called EMVCC. Specifically, we propose an enhancedview mechanism to increase the differentiation between dissimilar samples and the similarity between similar samples. Then, we designed a Self-supervised Joint Loss to constrain the consensus representation of different views for avoiding false negative pairs.To further leverage the consensus representation of multi-view data, we use a contrast loss to maximize the consistency between different augmented views of the same data samples. Using Feature Enhanced Loss enhances the discriminative features to ensure the diversity of multi-view information. Finally, we also design a novel Cluster-Friendly Loss that aligns view feature representations with high-confidence pseudo-labels to constrain the network for achieving favorable clustering. Complete experiments on four HSI datasets convincingly demonstrate the effectiveness of our proposed EMVCC. 
 
 Appendix:  


Decision: 1 Accept, 2 Borderline Accept, 1 Reject

Most reviewers have positive comments to this manuscript except one reviewer recommends "Reject". I follow the majority side. The following issues need to be carefully addressed in the final paper.
-- unclear motivation
-- novelty of the research problem addressed by the method is average
-- more experiments and comparisons
-- Why perform channel halves for spectral segmentation to construct multi-view data
-- What is the input for general multi-view clustering
-- discuss the limits or the potential negative societal impacts of the proposed method

Reviews: review: paper_summary: paper_summary: The authors propose a novel multi-view clustering method called Enhanced Multi-View Contrastive Clustering (EMVCC), which utilizes spatial multi-view learning to extract diverse features for contrastive clustering. Additionally, they employ Transformer to extract the global correlation information of spectral views to enhance differences between neighboring samples. Furthermore, they design a joint self-supervised loss to effectively avoid the generation of negative sample pairs, enhancing features with probabilistic contrastive loss to maintain diversity. Data is projected into a semantic information-rich representation space, ensuring that similar samples are closer in distance. Finally, a new clustering loss is designed to encourage the network to learn cluster-friendly features.
strength_weakness: strength_weakness: 1.The authors propose a novel Enhanced Multi-View Contrastive Clustering (EMVCC) method, which reduces homogeneity between single-scene views using cross-feature-domain views to extract cross-view consensus representations of HSI. 
2.The authors design a self-supervised joint loss to efficiently explore common semantics in all views, thereby obtaining distinctive clustering-friendly consensus representations. 
3.They allocate samples based on the similarity between attribute features and cluster centers, allowing the network to directly output data labels without post-processing.
limitations: limitations: 1.	The computational complexity of the model was not calculated; could the performance improvement be due to model complexity?
2.	Only six methods were compared; it is recommended to include more comparison methods.
3.	The time consumption of the model was not shown.
4.	The novelty of the research problem addressed by the method is average.
5.	The number of datasets used in the experiments is limited; it is recommended to conduct comparisons on more datasets.
paper_summary: paper_summary: The paper presents an Enhanced Multi-View Contrastive Clustering model for Hyperspectral Images (HSIs). The proposed model generates multi-view data from HSIs and performs constrastive learning to achieve clustering for HSIs. Experiments seem to demonstrate the effectiveness of the proposed model.
strength_weakness: strength_weakness: The proposed model is easy to follow and  understand.

The experiments are sufficient to verify the proposed model.

The proposed model shows competitive performance compared to other methods.
limitations: limitations: The motivation is unclear. There are no related interpretation about the difference of data representations designed for clustering or other applications that is pointed in the Abstract.

It seems that there is an error for “a representation spatial with semantic information”  at Line 28 in the Abstract.

Related review of hypersepctral image clustering is very limited and should be improved. Besides, there is also limited review for constrative learning in HSI clustering.

The citations are not correct at LIne 107 in the Introduction. They are alll designed for general  multi-view data instead of  HSI data.

In Figure 1, “space model” should be corrected as “spatial model”.

What is the anchor centers in Figure 1. There are no  related descriptions in the main text.

Why do you perform channel halves for spectral segmentation to construct multi-view data? Please give the detailed reason. 

What is ASN operation? The full name of ASN is not clear.

How to achieve classification head since there are no labels in clustering tasks.

For the compared methods, [20], [23], [49] are HSI clustering methods, while others are general multi-view clustering methods. NO multi-view clustering methods designed for HSIs are compared. More related methods should be compared in the experiments, such as Tensorial multiview subspace clustering for polarimetric hyperspectral images and Contrastive Multiview Subspace Clustering of Hyperspectral Images Based on Graph Convolutional Networks.

What is the input for general multi-view clustering? Please provide detailed descriptions about the input of  general multi-view clustering methods in the experiments.
paper_summary: paper_summary: This paper has proposed the Enhanced Multi-View Contrastive Clustering for Hyperspectral Images. To avoid false negative pairs, the authors have designed the self-supervised joint loss to constrain the consensus representation of different views.  Experiments on several hyperspectral image datasets are conducted to investigate the performance of the proposed method.
strength_weakness: strength_weakness: 1、	The design of the self-supervised joint loss.
2、	A clustering loss that aligns view feature representations with high-confidence pseudo-labels for constraining the network.
limitations: limitations: 1、	In Abstract, the authors stated that “these methods have a fatal flaw: Contrastive learning treats similar heterogeneous views as positive sample pairs and dissimilar homogeneous views as negative sample pairs.” This sentence explains the main idea of contrastive learning without describing the so-called fatal flaw. 
2、	From Abstract, I cannot understand what special designs for hyperspectral images which are significantly different from natural images.
3、	Why do not give the accuracy of all hyperspectral image clustering methods?
4、	Many mistakes in grammar, for example
(1)	the data is projected into a representation spatial with semantic information,
(2)	Abundant experiment studies on numerous benchmarks verify the superiority
paper_summary: paper_summary: The authors proposed a novel enhanced multi-view contrastive clustering for Hyperspectral Images(HSIs), namely EMVCC, to mitigate the 'collision problem' in contrast learning as well as to obtain clustering-friendly consensus representations. It utilizes Transformer to extract the global correlation information of spectrum-view to enhance the difference of spatial-multi-view of neighbor samples. This provides a new paradigm for subsequent work that utilises cross-scene view features to reduce homogeneity between single scene views. A self-supervised joint loss to efficiently explore the common semantics in all views to obtain distinctive clustering-friendly consensus representations. Experiments have shown better clustering performances compared to Multi-view Clustering methods. The model allocate the samples based on the similarity between attribute features and cluster centers, allowing the network to output data labels directly without post-processing.
strength_weakness: strength_weakness: 1/ The Enhanced Multi-View Contrast Clustering (EMVCC) method has been clearly explained. It combines multi-view clustering and contrast learning to extract cluster-friendly consensus representations. Enhance the difference of spatial-multi-view of neighbour samples by learned features from the spectral view.This greatly alleviates the problem of false negatives due to similar heterogeneous samples in contrast learning. Simultaneously, EMVCC employs self-supervised joint loss optimization to refine the extracted consensus representations. It effectively explores the common semantics across all views to obtain differentiated, 'cluster-friendly' consensus representations.It may tackle the actual unsupervised problems related to various hyperspectral in satellite data.

2/ The presented results show that EMVCC outperforms other competing methods on multiple datasets based on clustering accuracy as well as a range of clustering performance metrics. The study of ablation is rigorously conducted, showing the impact of the spatial module, the spectral module on multi-view clustering, as well as the impact of feature enhancement loss and clustering optimisation loss in self-supervised joint loss.

3/ Visualization of clustering maps and t-SNE conducted are appreciated.

4/ This work could have a significant impact since large volume of remote sensing data are publicly available without labels. There is an urgent need of efficient and powerful unsupervised training models for many remote sensing applications, including precision agriculture and mineral exploration understanding and monitoring.
limitations: limitations: The authors have neither detailed the limits, nor the potential negative societal impacts (military or surveillance applications) of the proposed method. No negative societal impacts are expected.

#### question

1/ Figure 2: 'Anchor Centers' blocks are not defined.

2/ Typo: lack of consistency between "multiview" and "multi-view"

3/ Typo: Unstandardised use of "spectral" and "spectrum"

4/ Table 1: The first letter of ‘salinas’ is not capitalised.

5/ Typo: lack of consistency between "View_S" and "View_Spe"