Deep Learning Algorithms for Satellite Imagery

This book provides key insights into the world of Deep Learning pertaining to satellite image understanding. It highlights what differentiates satellite image datasets from other natural or synthetic images and how to tackle problems specific to these imagery data. From answering questions like how to select optimal training data to weekly supervised and unsupervised learning and how to tackle loosely labeled data, it is a valuable source of information for anyone interested in understanding the theory behind satellite image analytics and provides key insights on the application of various state-of-the-art Deep Learning algorithms on these datasets.

本書提供了關於衛星影像理解的深度學習的關鍵見解。它強調了衛星影像數據集與其他自然或合成影像的區別，以及如何解決這些影像數據特定問題的方法。從回答如何選擇最佳訓練數據到週期性監督和非監督學習，以及如何處理鬆散標記的數據，本書對於任何對於理解衛星影像分析理論感興趣的人來說都是一個寶貴的資訊來源，並提供了關於在這些數據集上應用各種最先進的深度學習算法的關鍵見解。

Saikat Basu is working as a research scientist in the Facebook Maps team in Boston. He received his PhD in Computer Science from Louisiana State University in 2016. He received his Bachelor of Technology in Computer Science and Engineering from National Institute of Technology, Durgapur, India in 2011. During his doctoral program, he has been doing research on the analysis of various kinds of imagery data using Computer Vision and Deep Learning algorithms for the analysis of satellite imagery data. During his PhD, he has worked as a research associate at NASA Ames Research Center, Moffett Field, California and an intern at the Facebook Maps team in Boston.

Saikat Basu 目前在波士頓的 Facebook 地圖團隊擔任研究科學家。他於 2016 年從路易斯安那州立大學獲得計算機科學博士學位。他於 2011 年從印度國家技術研究院 Durgapur 分校獲得計算機科學和工程學士學位。在博士期間，他一直在使用計算機視覺和深度學習算法分析各種圖像數據，尤其是衛星圖像數據。在博士期間，他曾在加利福尼亞州莫菲特菲爾德的 NASA Ames 研究中心擔任研究助理，並在波士頓的 Facebook 地圖團隊實習過。