Deep neural networks have proven to be very effective for computer vision tasks, such as image classification, object detection, and semantic segmentation -- these are primarily applied to color imagery and video. In recent years, there has been an emergence of deep learning algorithms being applied to hyperspectral and multispectral imagery for remote sensing and biomedicine tasks. These multi-channel images come with their own unique set of challenges that must be addressed for effective image analysis. Challenges include limited ground truth (annotation is expensive and extensive labeling is often not feasible), and high dimensional nature of the data (each pixel is represented by hundreds of spectral bands), despite being presented by a large amount of unlabeled data and the potential to leverage multiple sensors/sources that observe the same scene. In this chapter, we will review recent advances in the community that leverage deep learning for robust hyperspectral image analysis despite these unique challenges -- specifically, we will review unsupervised, semi-supervised and active learning approaches to image analysis, as well as transfer learning approaches for multi-source (e.g. multi-sensor, or multi-temporal) image analysis.
Addressing Annotation Scarcity in Hyperspectral Brain Image Segmentation with Unsupervised Domain Adaptation
Technical University of Munich, Munich, Germany（慕尼黑技术大学）；Department of Computing, Imperial College London, London, UK（伦敦帝国学院计算机系）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML））
King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
Hyperspectral imaging has attracted significant attention to identify spectral signatures for image classification and automated pattern recognition in computer vision. State-of-the-art implementations of snapshot hyperspectral imaging rely on bulky, non-integrated, and expensive optical elements, including lenses, spectrometers, and filters. These macroscopic components do not allow fast data processing for, e.g real-time and high-resolution videos. This work introduces Hyplex, a new integrated architecture addressing the limitations discussed above. Hyplex is a CMOS-compatible, fast hyperspe…
Semantic segmentation of surgical hyperspectral images under geometric domain shifts
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberg University Hospital（海德堡大学医院）
Handling Geometric Domain Shifts in Semantic Segmentation of Surgical RGB and Hyperspectral Images
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Information and Data Science School for Health（亥姆霍兹健康信息与数据科学学院）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；Heidelberg University Hospital（海德堡大学医院）；Medical Faculty, Heidelberg University（海德堡大学医学院）
Fruit-HSNet: A Machine Learning Approach for Hyperspectral Image-Based Fruit Ripeness Prediction
Efrei Research Lab, Paris Panthéon-Assas University（巴黎先贤祠-阿萨斯大学埃弗雷研究所）；Institute of Applied Computer Science, Lodz University of Technology（罗兹理工大学应用计算机科学研究所）
SSUMamba: Spatial-Spectral Selective State Space Model for Hyperspectral Image Denoising
School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
该研究提出 SSUMamba，通过六种空间-光谱连续扫描顺序、双向 SSM 与 3D 卷积高效建模高光谱图像的长程及局部依赖，在降低每批内存消耗的同时提升去噪性能。
Xidian University（西安电子科技大学）；Beihang University（北京航空航天大学）；University of Technology, Sydney（悉尼科技大学）
Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly exploit the discriminative information borrowed from related domains. To address this issues, in this paper we show a novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL). The main idea of our method is to retrain the multi-kernel classifier by utilizing the available labeled samples from source domain, and adding minimum …
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学光电与智能研究院）；School of Computer Science and School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（西北工业大学计算机学院与光电与智能研究院）
In recent years, hyperspectral anomaly detection (HAD) has become an active topic and plays a significant role in military and civilian fields. As a classic HAD method, the collaboration representation-based detector (CRD) has attracted extensive attention and in-depth research. Despite the good performance of the CRD method, its computational cost mainly arising from the sliding dual window strategy is too high for wide applications. Moreover, it takes multiple repeated tests to determine the size of the dual window, which needs to be reset once the dataset changes and cannot be identified in…
Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging
Mohamed Khider University（Mohamed Khider大学）；National Research Council of Italy（意大利国家研究理事会）；Université Polytechnique Hauts-de-France（法国上法兰西理工学院）；Centrale Lille（里尔中央理工大学）；CNRS（国家科学研究中心）
Multi-head Spatial-Spectral Mamba for Hyperspectral Image Classification
National University of Computer and Emerging Sciences（国家计算机与新兴科学大学）；Shaoxing University（绍兴大学）；King Saud University（国王 Saud 大学）；Innopolis University（Innopolis 大学）；University of Messina（墨西拿大学）
UFM: Unified Feature Matching Pre-training with Multi-Modal Image Assistants
Information Science and Technology College, Dalian Maritime University（大连海事大学信息科学技术学院）；National Pilot School of Software, Yunnan University（云南大学国家示范性软件学院）；Yunnan Lanyi Network Technology Co（云南揽翼网络科技有限公司）
