Image Processing and Analysis Methods
Year: 2019
Type: book-chapter
Abstract: Recent developments for acquiring and distributing remotely-sensed data have greatly increased data availability to the user community. The past two decades have witnessed an explosion in data acquisition by a variety of ground, airborne and orbital sensors. The popularization of Unmanned Aerial Systems (UAS) and the development of reduced cost orbital platforms should guarantee that even higher data volumes will be available to future analysts. The past decades also saw the opening of image data archives (e.g., Landsat, CBERS, Sentinel), making access to a rich database of moderate resolution satellite images a reality across the globe. This increased volume and variety of remotely-sensed data increases the demand for methods and procedures for data handling and information extraction. This chapter describes recent efforts to expand the analyst’s data processing toolset and includes the theory and strategies used in manipulating remotely-sensed data by digital systems. The text focuses on presenting algorithms and techniques for image processing and analysis and emphasizes recent developments not covered by previous editions of the ASPRS Manual of Remote Sensing. Although the main topics covered by the chapter involve the direct processing of images, the text also covers concepts involved in processing remote sensing data that may not have been collected or stored as images, such as spectral curves acquired by spectroradiometers. Several sections of this chapter match this description, including Spectral Vegetation Indices and Spectral Mixture Analysis. Image processing includes not only the analysis of images, but also the necessary steps involved in preparing images for analysis, such as geometric correction, atmospheric correction and several techniques associated with image enhancement. Spectral indices resulting from the combination of multiple spectral bands are presented, with emphasis on the description of vegetated targets. A detailed treatment is given to the mixture problem resulting from the contribution of multiple materials within the instantaneous field of view (IFOV) of a given sensor. Because multiple applications can benefit from the increased explanation power provided by a large number of spectral bands, hyperspectral data processing is also presented and discussed. Further, the chapter addresses the benefits and challenges involved in combining datasets acquired by different systems (Data Fusion). Image classification addresses multiple strategies involved in assigning classes to images (e.g., Support Vector Machine, and Decision Trees); and includes advances in Object-Based Image Analysis (OBIA), particularly those related to image segmentation in preparation for classification. Given the increasing length of remotely-sensed data time series, particular attention is given to preparing sequences of images and data, including multiple techniques for smoothing, spike removal and the retrieval of metrics associated with temporal variations of targets. The chapter also brings multiple examples of use of products derived from processing remotely-sensed data as input to a variety of workflows, including modeling and analysis efforts. Finally, very current topics involving recent advances in image acquisition and availability, are presented for generating 3D surfaces from multiple images using Structure from Motion (SfM); processing of very large datasets (Big Data); and processing of images in the cloud are presented.
Authors: Sergio Bernardes, Marguerite Madden, Ike Astuti, Sergio Bernardes, Emilio Chuvieco, David Cotten, Philip E Dennison, Iryna Dronova, Ioannis Gitas, Peng Gong, Belen Franch-Gras, Matt Hancher, Akira Hirano, Allison Howard, Xuefei Hu, Alfredo Huete, Thomas Jordan, Chris Justice, Rick L Lawrence, Linlin Lu, Marguerite Madden, Deepak R Mishra, Sachidananda Mishra, Tomoaki Miura, Giorgos Mountrakis, Mahesh Pal, Caren Remillard, Dar A Roberts, Jean-Claude Roger, Kunwar K Singh, Ben Somers, Dimitris Stavrakoudis, Wanxiao Sun, Guoqing Sun, David Thau, Laurent Tits, E. Lynn Usery, Eric Vermote, Cuizhen Wang, Mingshu Wang, Qihao Weng, Wenjing Xu, Tian Yao, Hiroki Yoshioka, Lei Zhang, Qingyuan Zhang, Zhi Zhang
Institutions: University of Georgia, Universidad de Alcalá, University of Utah, University of California, Berkeley, Aristotle University of Thessaloniki, Tsinghua University, University of Maryland, College Park, Google (United States), Japan International Research Center for Agricultural Sciences, Emory University, University of Technology Sydney, Montana State University, Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, University of Hawaiʻi at Mānoa, SUNY College of Environmental Science and Forestry, National Institute of Technology Kurukshetra, University of California, Santa Barbara, Goddard Space Flight Center, North Carolina State University, KU Leuven, Grand Valley State University, Flemish Institute for Technological Research, United States Geological Survey, University of South Carolina, Indiana State University, Universities Space Research Association, National Aeronautics and Space Administration, Aichi Prefectural University, China University of Geosciences
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3015584711