Introductory Digital Image Processing

Slides:

Advertisements

Similar presentations

Introduction to Remote Sensing

Advertisements

AMwww.Remote-Sensing.info Ch.1 Remote Sensing and Digital Image Processing

Optical Imaging and Field Spectroscopy: CLPX 2002 and 2003 Thomas H. Painter.

Active Remote Sensing Systems March 2, 2005 Spectral Characteristics of Vegetation Temporal Characteristics of Agricultural Crops Vegetation Indices Biodiversity.

AMwww.Remote-Sensing.info Ch.2 Remote Sensing Data Collection

Liang APEIS Capacity Building Workshop on Integrated Environmental Monitoring of Asia-Pacific Region September 2002, Beijing,, China Atmospheric.

Multispectral Remote Sensing Systems

Remote Sensing of Our Environment Using Satellite Digital Images to Analyze the Earth’s Surface.

Hyperspectral Imagery

Detector Configurations Used for Panchromatic, Multispectral and Hyperspectral Remote Sensing Jensen, 2000.

Remote Sensing What can we do with it?. The early years.

Remote Sensing Part 1.

Remote Sensing II Introduction. Scientists formulate hypotheses and then attempt to accept or reject them in a systematic, unbiased fashion. The data.

Lesson: Remote sensing imagery Zirek Malikova OShTU, Software Engineering Department Module: Data Acquisition and data interpretation.

INTRODUCTION TO REMOTE SENSING & DIGITAL IMAGE PROCESSING Course: Introduction to RS & DIP Mirza Muhammad Waqar Contact:

Abbie Harris - NOAA Ocean Acidification Think Tank #5 Current and Future Research at the Institute for Marine Remote Sensing Abbie Rae Harris Institute.

HyspIRI Airborne Preparatory Mission Large Area Mapping In California Benefits to Remote Sensing of the Delta

Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.

Remote Sensing Hyperspectral Remote Sensing. 1. Hyperspectral Remote Sensing ► Collects image data in many narrow contiguous spectral bands through the.

Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.

Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.

Assessment of Regional Vegetation Productivity: Using NDVI Temporal Profile Metrics Background NOAA satellite AVHRR data archive NDVI temporal profile.

Liane Guild, Brad Lobitz, Randy Berthold, Jeremy Kerr Biospheric Science Branch, NASA Ames Research Center, CA Roy Armstrong, James Goodman, Yasmine Detres,

The role of remote sensing in Climate Change Mitigation and Adaptation.

Resolution A sensor's various resolutions are very important characteristics. These resolution categories include: spatial spectral temporal radiometric.

Remote Sensing Defined Resolution Electromagnetic Energy (EMR) Types Interpretation Applications.

Chapter 5 Remote Sensing Crop Science 6 Fall 2004 October 22, 2004.

West Hills College Farm of the Future. West Hills College Farm of the Future Precision Agriculture – Lesson 4 Remote Sensing A group of techniques for.

Christine Urbanowicz Prepared for NC Climate Fellows Workshop June 21, 2011.

1 Suborbital Science Program Airborne Remote Sensing of the SF Bay NASA Ames Research Center University of California Santa Cruz Airborne Science & Technology.

Chapter 4. Remote Sensing Information Process. n Remote sensing can provide fundamental biophysical information, including x,y location, z elevation or.

 Introduction to Remote Sensing Example Applications and Principles  Exploring Images with MultiSpec User Interface and Band Combinations  Questions…

Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry

Remote Sensing Remote Sensing is defined as the science and technology by which the characteristics of objects of interest can be identified, measured.

Mirza Muhammad Waqar HYPERSPECTRAL REMOTE SENSING - SENSORS 1 Contact:

Physics-Based Modeling of Coastal Waters Donald Z. Taylor RIT College of Imaging Science.

Hyperspectral remote sensing

1 October 8, 2015 GIS Day 2015 Geospatial Technologies GPS (global positioning system) –Car GPS systems, yield monitors, smart phones RS (remote sensing)

Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC Dr. John R. Jensen Department of Geography University of South.

Hyperspectral Remote Sensing Ruiliang Pu Center for Assessment and Monitoring of Forest and Environmental Resources Department of Environmental Science,

Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC Dr. John R. Jensen Department of Geography University of South.

CRSC 6 October 1, 2004 CRSC 6 – Intro to Precision Ag Fall 2004 October 1, 2004.

SCM x330 Ocean Discovery through Technology Area F GE.

Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry

Data compression – For image archiving (satellite data) – For image transfer over internet.

Digital Image Processing

Introduction to Remote Sensing.

Initial Display Alternatives and Scientific Visualization

Mapping Variations in Crop Growth Using Satellite Data

AVIRIS By: Evan, Mike, Ana Belen ER-2 Twin Otter.

Radiometric Preprocessing: Atmospheric Correction

Hyperspectral Sensing – Imaging Spectroscopy

Department of Geography University of New Orleans, Louisiana

Week Six Aerial Photography Cities at Night Video

Hyperspectral Remote Sensing

Performance Performance is fundamentally limited by: Size of data

Remote Sensing of Vegetation

Remote Sensing What is Remote Sensing? Sample Images

Digital Numbers The Remote Sensing world calls cell values are also called a digital number or DN. In most of the imagery we work with the DN represents.

RapidEye 1-5 (Germany) Resolution: 5 m Bands: Altitude: 630 km

Satellite Sensors – Historical Perspectives

Hyperspectral remote sensing ('Image spectroscopy')

Elements of Image Interpretation

Sources of Variability in Canopy Spectra and the Convergent Properties of Plants Funding From: S.V. Ollinger, L. Lepine, H. Wicklein, F. Sullivan, M. Day.

Class Project for Ian Mullet

Remote Sensing Section 3.

Your Name A suitable title for your presentation Include the date of your presentation.

Hyperspectral Terminology

Hyperspectral Remote Sensing

Presentation transcript:

Introductory Digital Image Processing Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC 29208 Jensen, 2003

In Support of Remote Sensing Measurement In situ Measurement In Support of Remote Sensing Measurement In situ spectroradiometer measurement of soybeans In situ ceptometer leaf-area-index (LAI) measurement Jensen, 2003

Three-way Interaction Model Between the Mapping Sciences as Used in the Physical, Biological, and Social Sciences Jensen, 2000

Developmental Stages of a Scientific Discipline Time Jensen, 2000

Spectral Resolution Jensen, 2000

of the datacube was created using three Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Datacube of Sullivan’s Island Obtained on October 26, 1998 Color-infrared color composite on top of the datacube was created using three of the 224 bands at 10 nm nominal bandwidth. Jensen, 2000

Spatial Resolution Jensen, 2000

Remote Sensor Data Acquisition Temporal Resolution Remote Sensor Data Acquisition June 1, 2001 June 17, 2002 July 3, 2003 16 days Jensen, 2000

Radiometric Resolution 8-bit (0 - 255) 9-bit (0 - 511) 10-bit (0 - 1023) Jensen, 2000

Remote Sensing Earth System Science Jensen, 2000