325 research outputs found
Use of ERTS-1 data: Summary report of work on ten tasks
The author has identified the following significant results. Depth mapping's for a portion of Lake Michigan and at the Little Bahama Bank test site have been verified by use of navigation charts and on-site visits. A thirteen category recognition map of Yellowstone Park has been prepared. Model calculation of atmospheric effects for various altitudes have been prepared. Radar, SLAR, and ERTS-1 data for flooded areas of Monroe County, Michigan are being studied. Water bodies can be reliably recognized and mapped using maximum likelihood processing of ERTS-1 digital data. Wetland mapping has been accomplished by slicing of single band and/or ratio processing of two bands for a single observation date. Both analog and digital processing have been used to map the Lake Ontario basin using ERTS-1 data. Operating characteristic curves were developed for the proportion estimation algorithm to determine its performance in the measurement of surface water area. The signal in band MSS-5 was related to sediment content of waters by modelling approach and by relating surface measurements of water to processed ERTS data. Radiance anomalies in ERTS-1 data could be associated with the presence of oil on water in San Francisco Bay, but the anomalies were of the same order as those caused by variations in sediment concentration and tidal flushing
Quarterly literature review of the remote sensing of natural resources
The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports
Contribution of remote sensing technologies to a holistic coastal and marine environmental management framework: a review
Coastal and marine management require the evaluation of multiple environmental threats
and issues. However, there are gaps in the necessary data and poor access or dissemination of existing
data in many countries around the world. This research identifies how remote sensing can contribute
to filling these gaps so that environmental agencies, such as the United Nations Environmental
Programme, European Environmental Agency, and International Union for Conservation of Nature,
can better implement environmental directives in a cost-e ective manner. Remote sensing (RS)
techniques generally allow for uniform data collection, with common acquisition and reporting
methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly
when governments finance satellite missions. Some of these data can be used in holistic, coastal
and marine environmental management frameworks, such as the DAPSI(W)R(M) framework
(Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures),
an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful
and holistic problem-structuring framework that can be used to assess the causes, consequences, and
responses to change in the marine environment. Six broad classifications of remote data collection
technologies are reviewed for their potential contribution to integrated marine management, including
Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned
Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this
study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications
are not expected to be all-inclusive; rather, they provide insight into the current use of the framework
as a foundation for developing further holistic resource technologies for management strategies in
the future. A significant outcome of this research will deliver practical insights for integrated coastal
and marine management and demonstrate the usefulness of RS to support the implementation of
environmental goals, descriptors, targets, and policies, such as theWater Framework Directive, Marine
Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development
Goals. Additionally, the opportunities and challenges of these technologies are discussed.Murray Foundation: 25.26022020info:eu-repo/semantics/publishedVersio
On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances and Million-AID
The past years have witnessed great progress on remote sensing (RS) image
interpretation and its wide applications. With RS images becoming more
accessible than ever before, there is an increasing demand for the automatic
interpretation of these images. In this context, the benchmark datasets serve
as essential prerequisites for developing and testing intelligent
interpretation algorithms. After reviewing existing benchmark datasets in the
research community of RS image interpretation, this article discusses the
problem of how to efficiently prepare a suitable benchmark dataset for RS image
interpretation. Specifically, we first analyze the current challenges of
developing intelligent algorithms for RS image interpretation with bibliometric
investigations. We then present the general guidances on creating benchmark
datasets in efficient manners. Following the presented guidances, we also
provide an example on building RS image dataset, i.e., Million-AID, a new
large-scale benchmark dataset containing a million instances for RS image scene
classification. Several challenges and perspectives in RS image annotation are
finally discussed to facilitate the research in benchmark dataset construction.
We do hope this paper will provide the RS community an overall perspective on
constructing large-scale and practical image datasets for further research,
especially data-driven ones
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