Deep Hyperspectral Unmixing using Transformer Network (2203.17076v1)

Abstract: Currently, this paper is under review in IEEE. Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way in the field of hyperspectral image classification and achieved promising results. In this article, we harness the power of transformers to conquer the task of hyperspectral unmixing and propose a novel deep unmixing model with transformers. We aim to utilize the ability of transformers to better capture the global feature dependencies in order to enhance the quality of the endmember spectra and the abundance maps. The proposed model is a combination of a convolutional autoencoder and a transformer. The hyperspectral data is encoded by the convolutional encoder. The transformer captures long-range dependencies between the representations derived from the encoder. The data are reconstructed using a convolutional decoder. We applied the proposed unmixing model to three widely used unmixing datasets, i.e., Samson, Apex, and Washington DC mall and compared it with the state-of-the-art in terms of root mean squared error and spectral angle distance. The source code for the proposed model will be made publicly available at \url{https://github.com/preetam22n/DeepTrans-HSU}.

• The paper leverages a novel transformer-based model combining convolutional autoencoder and multihead self-patch attention to enhance hyperspectral unmixing performance.
• It achieves significant reductions in RMSE and SAD metrics across datasets, outperforming traditional methods in spectral unmixing tasks.
• The approach offers practical insights for improving earth observation and resource mapping by capturing both local and global spectral dependencies.

Deep Hyperspectral Unmixing using Transformer Network

The manuscript "Deep Hyperspectral Unmixing using Transformer Network" addresses the challenging task of hyperspectral unmixing by leveraging state-of-the-art deep learning techniques, particularly transformers. Hyperspectral unmixing is a critical process in remotely sensed hyperspectral imaging that aims to decompose a mixed pixel into its constituent pure spectral signatures, known as endmembers, and their corresponding abundances.

Methodological Overview

The paper introduces a novel method that combines convolutional neural networks (CNNs) and transformer architectures to perform hyperspectral unmixing. The proposed deep unmixing model consists of a convolutional autoencoder (CAE) integrated with a transformer network. The model aims to capture global contextual dependencies to improve the quality of the estimated endmember spectra and abundance maps by incorporating the following elements:

1. Convolutional Autoencoder (CAE): The CAE is tasked with encoding hyperspectral data into a latent space that is lower-dimensional but rich in features. This process helps in initial feature extraction and dimensionality reduction, focusing on local feature extraction.
2. Transformer Encoder with Multihead Self-Patch Attention: The transformer aims to capture long-range dependencies and global contextual information that traditional CNNs might miss. The paper introduces a novel attention mechanism, named Multihead Self-Patch Attention, which enhances the feature extraction process by considering the long-range relationships between patches in the hyperspectral data's latent space representation.
3. Convolutional Decoder for Reconstruction: The decoder reconstructs the hyperspectral image from the transformed features, enabling the estimation of abundance maps and endmember spectra.

The integration of transformers into the unmixing process capitalizes on their ability to model complex dependencies over entire image regions, yielding improved unmixing results compared to conventional techniques.

Experimental Evaluation

The paper provides an empirical evaluation of the proposed model using three widely-recognized hyperspectral datasets: Samson, Apex, and Washington DC Mall. The authors report performance metrics in terms of Root Mean Squared Error (RMSE) and Spectral Angle Distance (SAD) to assess the accuracy of abundance maps and endmember estimation. Notably, the model demonstrates superior performance over existing state-of-the-art methods, significantly reducing the RMSE and SAD across various datasets.

Implications and Future Prospects

The results highlight the transformer network's effectiveness in hyperspectral unmixing, showing that deep learning models can leverage global context to surpass the limitations of localized feature extraction in CNNs. The promising results suggest several potential implications for practical applications:

• Enhanced Earth Observation: Improved accuracy in unmixing will enhance environmental monitoring efforts, allowing for more precise analysis of land use and cover.
• Resource Exploration and Management: Better spectral unmixing can lead to more accurate resource mapping and assessment, affecting fields like agriculture and mineral exploration.

Future research could explore further optimizations and adaptions of transformer architectures to hyperspectral data, including:

• Exploration of Different Attention Mechanisms: Additional studies could refine or propose alternative attention mechanisms to enhance feature extraction further.
• Hybrid Models Integration: Integrating transformers with other deep learning frameworks or statistical models may yield even more robust approaches to unmixing.
• Application-Specific Adaptations: Tailoring models to specific application needs or data types might result in improved performance in specialized scenarios.

In summary, the paper presents a substantial methodological advancement in hyperspectral unmixing, employing transformer networks to effectively capture intricate spectral patterns and ultimately improve the accuracy of unmixing outcomes.