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Image Processing and Analysis Methods

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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.

Document Type: Research Article

Affiliations: 1: Center for Geospatial Research, Department of Geography, University of Georgia, Athens, GA, USA 2: Environmental Remote Sensing Research Group, Department of Geology, Geography and Environment. Universidad de Alcalá, Spain 3: Department of Geography, University of Utah, Salt Lake City, UT, USA 4: Department of Landscape Architecture and Environmental Planning, College of Environmental Design, University of California Berkeley, Berkeley, CA, USA 5: Laboratory of Forest Management and Remote Sensing, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Greece 6: Center for Earth System Science, Tsinghua University, Beijing, China 7: University of Maryland, Department of Geographical Sciences, College Park, MD, USA 8: Google, Mountain View, CA, USA 9: Japan International Research Center for Agricultural Sciences, Tsukuba, Ibaraki, Japan 10: Department of Psychology, University of Georgia, Athens, GA, USA 11: Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA 12: University of Technology, Sydney, Sydney, Australia 13: Department of Geographical Sciences, University of Maryland, College Park, MD, USA 14: Spatial Sciences Center, Montana State University, Bozeman, MT, USA 15: Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China 16: Dow Agrosciences LLC, Indianapolis, IN, USA 17: Department of Natural Resources and Environmental Management, University of Hawaii at Manoa, Honolulu, HI, USA 18: Department of Environmental Resources Engineering, State University of New York College of Environmental Science and Forestry, Syracuse, NY, USA 19: Department of Civil Engineering, National Institute of Technology, Kurukshetra, Haryana, India 20: Department of Geography, University of California, Santa Barbara, CA, USA 21: Center for Geospatial Analytics, North Carolina State University, Raleigh, NC, USA 22: Division of Forest, Nature and Landscape, Katholieke Universiteit Leuven, Leuven, Belgium 23: Grand Valley State University, Allendale, MI, USA 24: Flemish Institute for Technological Research (VITO), Remote Sensing Unit, Mol, Belgium 25: U.S. Geological Survey, Rolla, MO, USA 26: NASA Goddard Space Flight Center, Greenbelt, MD, USA 27: Department of Geography, University of South Carolina, Columbia, SC, USA 28: Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN, USA 29: Universities Space Research Association, Goddard Earth Sciences and Technology Center, NASA Goddard Space Flight Center, Biospheric Sciences Lab, Greenbelt, MD, USA 30: Aichi Prefectural University, Nagakute, Japan 31: School of Public Administration, China University of Geosciences, Wuhan, China

Publication date: January 1, 2019

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