Deep Learning for Hyperspectral Image Analysis and Classification
暫譯: 深度學習在高光譜影像分析與分類中的應用
Tao, Linmi, Mughees, Atif
- 出版商: Springer
- 出版日期: 2021-02-21
- 售價: $7,920
- 貴賓價: 9.5 折 $7,524
- 語言: 英文
- 頁數: 207
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 9813344199
- ISBN-13: 9789813344198
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相關分類:
DeepLearning
海外代購書籍(需單獨結帳)
相關主題
商品描述
This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly.
This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
商品描述(中文翻譯)
本書專注於基於深度學習的方法來分析高光譜影像（HSI）。提出了一種無監督的光譜-空間自適應帶噪聲因子模型，用於HSI噪聲檢測和帶類別化。該方法對於表徵帶以及HSI的噪聲估計將顯著有助於後續的遙感技術。
本書從兩個方面進行發展：一方面，旨在為希望了解高光譜獲取技術如何與深度學習架構結合以解決不同應用領域特定任務的專業人士提供最新的概覽；另一方面，作者希望針對機器學習和計算機視覺專家，從多學科的角度展示深度學習技術如何應用於高光譜數據。本書的原創貢獻在於這兩種觀點的存在，以及深度學習在遙感應用領域的納入，並突顯了一些與觀察到的發展趨勢相關的潛力和關鍵問題。
作者簡介
Linmi Tao received the B.S. degree in Biology from Zhejiang University, Zhejiang, China, the M.S. degree in Cognitive Science from the Chinese Academy of Sciences, Beijing, China, and the Ph.D. degree in Computer Science from Tsinghua University, Beijing. He is currently an Associate Professor with the Department of Computer Science and Technology, Tsinghua University. He has studied and worked with the International Institute for Advanced Scientific Studies and the University of Verona, Italy, and Tsinghua University on computational visual perception, 3D visual information processing, and computer vision. His research work covers a broad spectrum of computer vision, computational cognitive vision, and human-centered computing based on his cross-disciplinary background. Currently, his research is mainly focused on vision and machine learning areas, including deep learning based hyperspectral image processing, medical image processing, and visual scene understanding.
Atif Mughees received his B.E. and M.S. degree in Computer Software from the National University of Science and Technology Islamabad, Pakistan, in 2005 and 2009, respectively, and Ph.D. degree in Computer Vision and Deep Learning from the Key Laboratory of Pervasive Computing, Department of Computer Science and Technology, Tsinghua University, Beijing, China, in 2018. His research interests include image processing, remote sensing applications, and machine learning with a special focus on spectral and spatial techniques for hyperspectral image classification.
作者簡介(中文翻譯)
林米陶（Linmi Tao）於中國浙江省浙江大學獲得生物學學士學位，於中國北京中國科學院獲得認知科學碩士學位，並於清華大學獲得計算機科學博士學位。他目前是清華大學計算機科學與技術系的副教授。他曾在國際高級科學研究所、意大利維羅納大學及清華大學從事計算視覺感知、3D視覺信息處理和計算機視覺的研究。他的研究工作涵蓋了計算機視覺、計算認知視覺和以人為中心的計算，基於他的跨學科背景。目前，他的研究主要集中在視覺和機器學習領域，包括基於深度學習的高光譜影像處理、醫學影像處理和視覺場景理解。
阿提夫·穆赫斯（Atif Mughees）於2005年和2009年分別在巴基斯坦伊斯蘭堡國立科技大學獲得計算機軟體的工程學士和碩士學位，並於2018年在中國北京清華大學計算機科學與技術系的普適計算重點實驗室獲得計算機視覺和深度學習的博士學位。他的研究興趣包括影像處理、遙感應用和機器學習，特別專注於高光譜影像分類的光譜和空間技術。
