| 书目名称 | Hyperspectral Image Analysis |
| 副标题 | Advances in Machine |
| 编辑 | Saurabh Prasad,Jocelyn Chanussot |
| 视频video | |
| 概述 | Provides a comprehensive review of the state of the art in hyperspectral image analysis.Presents perspectives from experts who are pioneers in a broad range of signal processing and machine learning f |
| 丛书名称 | Advances in Computer Vision and Pattern Recognition |
| 图书封面 |  |
| 描述 | .This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas ofimage analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, g |
| 出版日期 | Book 2020 |
| 关键词 | Hyperspectral Image Analysis; Manifold Learning; Subspace Learning; Computational Imaging; Target Recogn |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-38617-7 |
| isbn_softcover | 978-3-030-38619-1 |
| isbn_ebook | 978-3-030-38617-7Series ISSN 2191-6586 Series E-ISSN 2191-6594 |
| issn_series | 2191-6586 |
| copyright | Springer Nature Switzerland AG 2020 |
发表于 2025-3-21 16:22:49
