Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: The paper has proposed a flexible, simple and scalable method for classifying hyperspectral images. The authors have proposed the idea of adding an auxiliary task to counter the lack of inductive bias in SOTA transformer models.
strengths_and_weaknesses: Strengths :

1. The paper is well-written, very well-structured and replete with necessary experimental results.
2.  It focuses on an important topic of classifying hyperspectral images by augmenting available domain knowledge to improve performance.
3. The concept put forward in the paper is very well-represented with proper explanations.
4.  The results claimed by the authors show improvement over state-of-the-art methods.

Weaknesses :

1. I think the paper is more or less flawless barring one or two spelling mistakes.
2. The acknowledgements section of the paper contains information which might breach the anonymity of the paper.



suggested_changes: I thought that the paper is well-written except a few typos.The acknowledgements section should be removed. Also, a short concluding remark with a scope for future work would be handy.
review_title: null

final_score: rating: Great Start (GS): a submission which meets some of the reviewing criteria but has room for improvement
review_content: summary_of_contributions: In this paper, the authors focus on the problem of learning-based hyperspectral image classification.  To this end, this paper proposes a self-supervised learner which can generalize to input patches of different dimensions. A scalable pretrained model is presented to improve performance on downstream classification tasks. This model has been fine-tuned on three different datasets and achieves state-of-the-art performance.
strengths_and_weaknesses: **Strengths**
* This paper proposes a method to pre-train models on HSI data, using an encoder-based self-supervised learner.
* The method consists of augmenting a flattened image patch with a random token, using it during the pretraining section. The representation learned by the encoder is combined in a learnable combination using MLPs. Thus, the network can take advantage of domain knowledge.
* The experiments cover fine-tuning on three datasets. This method achieves SOTA performance by using only 10% of the data for training. 
* The method has been compared to three SOTA methods by using different metrics.
* Lastly, the paper is written in a clear and concise manner. The finding is communicated effectively.

**Weaknesses**
* Some hyperparameters such as the patch size used for pre-training can be added in the hyperparameters section. I found these additional details lacking.
*  I find that the details provided in the paper are not sufficient to reproduce it. Without open-source code, since some details are missing, the results cannot be reproduced by others working on the same problem.
* Some grammatical errors are typos need to be fixed (mentioned in *Suggested Changes*)
* The grant information has not been removed

**Summary of the Review**

This paper aims to introduce a pre-training method for hyperspectral image classification which has the ability to generalize. Overall, the paper communicates the findings clearly and effectively. The claims appear to be correct to the best of my knowledge. The paper does not include enough information to be reproduced. Lastly, it follows the basic requirements for ICLR submission.

suggested_changes: Overall, the paper is well-written. The following changes should be made:
* Given the limited space for this format, Figure 1 can be made more descriptive by adding the names of vectors and latent variables. It would help the readers in understanding the equations.
* There are some typos which need to be fixed. For example, in the “Experimental” section, the word “we use” appears twice in the last sentence of the first paragraph. In the “Experimental” section, the last line of the Quantitative Evaluation section has the typo “SOAT methods.”
review_title: null

final_score: rating: Needs Clarification (NC): a submission which does not meet the reviewing criteria and needs clarification for its described problem or solution
review_content: summary_of_contributions: A pretraining and downstream finetuning technique for hyperspectral image classification is proposed in this paper
strengths_and_weaknesses: S1: The paper clearly shows the hyperparameters used to train the model.

S2: It can be seen from the experiments that the proposed method can improve performance.

W1: It is not very clear to me why the proposed auxiliary task works for the consider scenario, hyperspectral image classification. The paper should be clear to its readers of the motivation of the method design

W2: The paper is not properly anonymized, the acknowledge section contains information that can be used to identify the author.

suggested_changes: C1: I would suggest the author to add more about the motivation of the designed method, right now it does not give any explanations of why such a model would work well for the given task.

C2: I would also suggest adding comparisons to BeiT and MAE pretraining.

C3: In the introduction it is claimed that the proposed method can generalize to arbitrary size and spectral resolution, yet the paper does not have the experiments to valid that. I would strongly suggest to at least valid all the claims made in the paper.
review_title: null

Meta-review: metareview: 
This paper proposes a self-supervised learning method for the task of hyperspectral image classification. 
The method is designed to be able to incorporate domain knowledges of the hyperspectral image by using MLPs and an auxiliary task.

One reviewer thinks this paper is CCR, yet the other two reviewers all raise concerns about the clarity and the reproduciabilty of the paper.
It is suggested that the paper need to take into consideration of the concerns of the reviewers and revise the paper.

confidence: 5: The area chair is absolutely certain
Decision: Invite to archive