AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution
- URL: http://arxiv.org/abs/2502.06894v1
- Date: Sun, 09 Feb 2025 12:44:16 GMT
- Title: AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution
- Authors: David S. Bhatti, Yougin Choi, Rahman S M Wahidur, Maleeka Bakhtawar, Sumin Kim, Surin Lee, Yongtae Lee, Heung-No Lee,
- Abstract summary: Hyperspectral imaging (HSI) captures spatial and spectral data, enabling analysis of features invisible to conventional systems.
This study provides an overview of the HSI, its applications, challenges in data fusion and the role of deep learning models in processing HSI data.
Deep learning enhances HSI analysis in areas like feature extraction, change detection, denoising unmixing, dimensionality reduction, landcover mapping, data augmentation, spectral construction and super resolution.
- Score: 1.2647816797166165
- License:
- Abstract: Hyperspectral imaging (HSI) captures spatial and spectral data, enabling analysis of features invisible to conventional systems. The technology is vital in fields such as weather monitoring, food quality control, counterfeit detection, healthcare diagnostics, and extending into defense, agriculture, and industrial automation at the same time. HSI has advanced with improvements in spectral resolution, miniaturization, and computational methods. This study provides an overview of the HSI, its applications, challenges in data fusion and the role of deep learning models in processing HSI data. We discuss how integration of multimodal HSI with AI, particularly with deep learning, improves classification accuracy and operational efficiency. Deep learning enhances HSI analysis in areas like feature extraction, change detection, denoising unmixing, dimensionality reduction, landcover mapping, data augmentation, spectral construction and super resolution. An emerging focus is the fusion of hyperspectral cameras with large language models (LLMs), referred as highbrain LLMs, enabling the development of advanced applications such as low visibility crash detection and face antispoofing. We also highlight key players in HSI industry, its compound annual growth rate and the growing industrial significance. The purpose is to offer insight to both technical and non-technical audience, covering HSI's images, trends, and future directions, while providing valuable information on HSI datasets and software libraries.
Related papers
- Hybrid State-Space and GRU-based Graph Tokenization Mamba for Hyperspectral Image Classification [14.250184447492208]
Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning.
Traditional methods, including machine learning and convolutional neural networks (CNNs), often struggle to effectively capture these intricate spectral-spatial features.
This work proposes GraphMamba, a hybrid model that combines spectral-spatial token generation, graph-based token prioritization, and cross-attention mechanisms.
arXiv Detail & Related papers (2025-02-10T13:02:19Z) - HSIGene: A Foundation Model For Hyperspectral Image Generation [46.745198868466545]
Hyperspectral image (HSI) plays a vital role in various fields such as agriculture and environmental monitoring.
Due to the expensive acquisition cost, the number of hyperspectral images is limited, degenerating the performance of downstream tasks.
We propose HSIGene, a novel HSI generation foundation model which is based on latent diffusion and supports multi-condition control.
Experiments demonstrate that the proposed model is capable of generating a vast quantity of realistic HSIs for downstream tasks such as denoising and super-resolution.
arXiv Detail & Related papers (2024-09-19T05:17:44Z) - HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model [88.13261547704444]
Hyper SIGMA is a vision transformer-based foundation model for HSI interpretation.
It integrates spatial and spectral features using a specially designed spectral enhancement module.
It shows significant advantages in scalability, robustness, cross-modal transferring capability, and real-world applicability.
arXiv Detail & Related papers (2024-06-17T13:22:58Z) - HSIMamba: Hyperpsectral Imaging Efficient Feature Learning with Bidirectional State Space for Classification [16.742768644585684]
HSIMamba is a novel framework that uses bidirectional reversed convolutional neural network pathways to extract spectral features more efficiently.
Our approach combines the operational efficiency of CNNs with the dynamic feature extraction capability of attention mechanisms found in Transformers.
This approach improves classification accuracy beyond current benchmarks and addresses computational inefficiencies encountered with advanced models like Transformers.
arXiv Detail & Related papers (2024-03-30T07:27:36Z) - ADASR: An Adversarial Auto-Augmentation Framework for Hyperspectral and
Multispectral Data Fusion [54.668445421149364]
Deep learning-based hyperspectral image (HSI) super-resolution aims to generate high spatial resolution HSI (HR-HSI) by fusing hyperspectral image (HSI) and multispectral image (MSI) with deep neural networks (DNNs)
In this letter, we propose a novel adversarial automatic data augmentation framework ADASR that automatically optimize and augments HSI-MSI sample pairs to enrich data diversity for HSI-MSI fusion.
arXiv Detail & Related papers (2023-10-11T07:30:37Z) - A comprehensive review of 3D convolutional neural network-based
classification techniques of diseased and defective crops using non-UAV-based
hyperspectral images [0.1338174941551702]
Hyperspectral imaging (HSI) is a non-destructive and contactless technology that provides valuable information about the structure and composition of an object.
Due to its wide spectral range, HSI can be a more effective tool for monitoring crop health and productivity.
With the advent of this imaging tool in agrotechnology, researchers can more accurately address issues related to the detection of diseased and defective crops.
arXiv Detail & Related papers (2023-06-15T18:02:53Z) - Low-Light Hyperspectral Image Enhancement [90.84144276935464]
This work focuses on the low-light HSI enhancement task, which aims to reveal the spatial-spectral information hidden in darkened areas.
Based on Laplacian pyramid decomposition and reconstruction, we developed an end-to-end data-driven low-light HSI enhancement (HSIE) approach.
The effectiveness and efficiency of HSIE both in quantitative assessment measures and visual effects are demonstrated.
arXiv Detail & Related papers (2022-08-05T08:45:52Z) - Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image
Reconstruction [127.20208645280438]
Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from a 2D measurement.
Modeling the inter-spectra interactions is beneficial for HSI reconstruction.
Mask-guided Spectral-wise Transformer (MST) proposes a novel framework for HSI reconstruction.
arXiv Detail & Related papers (2021-11-15T16:59:48Z) - Interpretable Hyperspectral AI: When Non-Convex Modeling meets
Hyperspectral Remote Sensing [57.52865154829273]
Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience remote sensing (RS)
In the past decade efforts have been made to process analyze these hyperspectral (HS) products mainly by means of seasoned experts.
For this reason, it is urgent to develop more intelligent and automatic approaches for various HS RS applications.
arXiv Detail & Related papers (2021-03-02T03:32:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.