Last edited by Tojasida
Sunday, July 19, 2020 | History

2 edition of Computer analysis of remote sensing and geologic datasets found in the catalog.

Computer analysis of remote sensing and geologic datasets

Lisa Kaye Johnson

Computer analysis of remote sensing and geologic datasets

by Lisa Kaye Johnson

  • 125 Want to read
  • 20 Currently reading

Published .
Written in English

    Subjects:
  • Faults (Geology) -- Data processing.,
  • Joints (Geology) -- Data processing.

  • Edition Notes

    Statementby Lisa Kaye Johnson.
    The Physical Object
    Paginationxi, 215 leaves, bound :
    Number of Pages215
    ID Numbers
    Open LibraryOL16695357M

    From the Publisher: The book provides the non-specialist with an introduction to quantitative evaluation of satellite and aircraft derived from remotely retrieved data. Each chapter covers the pros and cons of digital remotely sensed data, without detailed mathematical treatment of computer based algorithms, but in a manner conductive to an understanding of their . Remote sensing acquires and interprets small or large-scale data about the Earth from a distance. Using a wide range of spatial, spectral, temporal, and radi.

    objective of the study, geologic interpretation of remotely sensed data may be simple or complicated. Remote sensing is a tool that makes some tasks easier, makes possible some tasks that would otherwise be impossible, but is inappropriate for some tasks. Depending on the individual situation, remote sensing may be extremely valuable. Optical remote sensing involves acquisition and analysis of optical data – electromagnetic radiation captured by the sensing modality after reflecting off an area of interest on ground. Optical image acquisition modalities have come a long way – from gray-scale photogrammetric images to hyperspectral images.

    The main goal in many geological surveys no longer is to create a single geologic map but to create a database from which many types of geologic and engineering geology maps can be derived. This requires a database design or "data model" that is sufficiently robust to manage complex geologic concepts such as three dimensional (spatial) and. This course gives an introduction to geological, regolith and soil remote sensing in the application of earth resources mapping. It includes the integration of remote sensing imagery with geoscience data sets, regional geophysics (e.g. aeromagnetics, radiometrics) and DEMs for geological interpretation and the generation of geological maps.


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Computer analysis of remote sensing and geologic datasets by Lisa Kaye Johnson Download PDF EPUB FB2

Analysis of imagery for geobotanical remote sensing, remote geochemistry, modern analogs to ancient environments, and astrogeology The book covers how to initiate a project, including determining the objective, choosingthe right tools, and selecting by: 3.

This book provides the material required for a single semester course in Environmental Remote Sensing plus additional, more advanced, reading for students specialising in some aspect of the subject.

It is written largely in non-technical language yet it provides insights into more advanced topics that some may consider too difficult for a non. Geological remote sensing currently encompasses multi-temporal, multi-source and multi-scale approaches.

The retrieval of big data in disseminated archives, as well as (near) real-time processing. An Overview of Thermal Infrared Remote Sensing with Applications to Geothermal and Mineral Exploration in the Great Basin, Western United States Author(s) James V.

Taranik. Remote sensing is becoming an important and useful tool in mapping large, remote areas. and has many applications in geosciences such as geologic and geo-structural mapping, mineral and water exploration, hydrocarbon exploration, natural hazards analysis, and.

The power of image-analysis methods Figure Combined display of remote sensing and other data. Contoured magnetics are plotted over LANDSAT data, shown in gray-scale format. 1 1 6 Remote Sensing and Image Analysis to enhance subtle features, and to manipulate large data sets, is often a benefit in itself.

Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and ations should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.

Remote sensing images have been widely and successfully used for mineral exploration for decades. Multi spectral remote sensing (LANDSAT+ and ASTER) image enhancement and interpretation proved to be useful tool in identification, detection, and delineation of lithological rock units and geologic structures associated with gold Size: KB.

The proposed course provides basic understanding about satellite based Remote Sensing and Digital Image Processing technologies. Presently, remote sensing datasets available from various earth orbiting satellites are being used extensively in various domains including in civil engineering, water resources, earth sciences, transportation engineering, navigation etc.

Application of Remote Sensing, Geology And Geomorphological Studies For Mass Wasting Zone Analysis In Jotiba- Panhala Hill Range Area, Kolhapur District, Maharashtra, India Gurav Chandrakant1, Babar Md.

1, Patil Yogita2, Patil Abhijit3 and Patode H.S.4 1. Department of Geology, Dnyanopasak College, Parbhani M.S., India : Gurav Chandrakant, Babar, Patil Yogita, Patil Abhijit, Patode H.S. It has two phases: Data acquisition phase and Data analysis phase. The underlying principle on which the whole Remote Sensing technique is developed is that all objects on the Earth’s surface have characteristic spectral signatures.

The applications of Remote Sensing in the Earth’s science as a whole, especially in Geology is manifold. Remote Sensing and Mineral Exploration contains the proceedings of the international workshop on remote sensing and mineral exploration, held in Bangalore, India in June The compendium is comprised of papers presented at the workshop and reflects the state of remote sensing in the field of geology and exploration for mineral and energy resources.

With this special issue we compile state-of-the-art analysis methods for converting remote sensing image data into information relevant to various earth sciences and monitoring applications.

We assume that the remote sensing image data has undergone radiometric and geometric correction processing. The second edition of this widely acclaimed book has been fully revised and updated.

The reader will find a wide range of information on various aspects of geological remote sensing, ranging from laboratory spectra of minerals and rocks, ground truth, to aerial and space-borne remote sensing. Atmosphere Remote Sensing Section Remote Sensing in Geology, Geomorphology and Hydrology Section Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning development of new remote sensing for high resolution; Validation of remote sensing data sets in challenging areas.

For nearly three decades there has been a phenomenal growth in the field of Remote Sensing. The second edition of this widely acclaimed book has been fully revised and updated.

The reader will find a wide range of information on various aspects of geological remote sensing, ranging from laboratory spectra of minerals and rocks, ground truth, to aerial and space-borne remote sensing 4/5(2). No other remote sensing book I have read hits all the fundamentals in such a succinct manner.

Be warned, this book does not cover special topics in remote sensing such as geological, environmental, or atmospheric specific techniques. This book discusses the fundamentals of remote sensing targeted for first year graduate students. A must read!Cited by: Statistical Analysis. Statistical methods are applied to data to derive patterns, make generalizations, detect trends, and to estimate the uncertainty associated with the data.

Many methods appropriate to work at the USGS, both within and beyond hydrology, can be found in the classic reference book by Helsel and Hirsch, " Statistical Methods in Water Resources.".

Abstract—Remote sensing image scene classification plays an important role in a wide range of applications and hence has been receiving remarkable attention.

During the past years, significant efforts have been made to develop various datasets or present a variety of approaches for scene classification from remote sensing images. However, a Cited by: Remote Sensing and Spectral Geology R. Bedell, A.P. Crósta, and E.

Grunsky, Editors Additional copies of this publication can be obtained from Society of Economic Geologists, Inc. Shaffer Parkway Littleton, CO ISBN:. The book provides an exhaustive coverage of optical, thermal, and microwave remote sensing, global navigation satellite systems (e.g., GPS), digital photogrammetry, and visual image analysis.

The main emphasis is on the basic concepts of remote sensing and GIS but topics such as digital image processing, spatial and attribute data model, geospatial analysis, and Reviews: 1.data sets to test for georeferencing; pan-chromatic Landsat data; final products of remote sensing data analysis done in this book; additionally the following data sets could be downloaded which are also available through the commands executed in the book (the data need to be extracted into the “raster_data” folder).Computer Processing of Remotely-Sensed Images An Introduction Fourth Edition Paul M.

Mather University of Nottingham and Magaly Koch Center for Remote Sensing Boston University A John Wiley & Sons, Ltd., Publication.