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February 2020
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
with potential activities for en-suring the positional consistency
and correctness of LiDAR point clouds while considering the
fact that the point-positioning process is not based on redundant
measurements and the difficulty in identifying corresponding
points in overlapping strips and/or ground truth ele-vation data.
• Section 7: Radiometric Calibration, Correction, and Normalization
of LiDAR Data: In addi-tion to evaluating and improving
geometric quality of LiDAR data, discussed earlier, this section
deals with improving the radiometric quality of LiDAR intensity
data as well as the impact of such activity on further LiDAR
data processing activities such as classification procedures.
• Section 8: Quality Control of LiDAR Data and Their Processing
provides the technical de-tails for characterizing LiDAR
data; more specifically, evaluating the local point density and
recent developments in evaluating LiDAR data quality and
processing activities such as the segmentation process.
Chapter 6: Archiving and Access Systems for Remote Sensing
INTRODUCTION
MRS-4 Chapter 6, Archiving and Access, focuses on major
developments inaugurated by the Committee on Earth Observation
Satellites, the Group on Earth Observations System of Systems,
and the International Council for Science World Data System
at the global level; initiatives at na-tional levels to create data
centers (e.g. the National Aeronautics and Space Administration
(NASA) Distributed Active Archive Centers and other
international space agency counterparts), and non-government
systems (e.g. Center for International Earth Science Information
Network). Other major elements focus on emerging tool sets,
requirements for metadata, data storage and refresh methods, the
rise of cloud computing, and questions about what and how much
data should be saved. The sub-sections of the chapter address
topics relevant to the science, engineering and standards used
for state-of-the-art operational and experimental systems.
Chapter 7: Image Processing and Analysis Methods
INTRODUCTION
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 open-ing 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 in-formation extraction. This
chapter, Image Processing and Analysis Methods, describes recent
ef-forts 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 spec-tral 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
pre-sented, 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 ex-planation 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 Deci-sion Trees); and includes advances in
Object-Based Image Analysis (OBIA), particularly those re-lated
to image segmentation in preparation for classification. Given the
increasing length of remote-ly-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 model-ing 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.
