Environment Remote Sensing Instructor: Prof. Prashanth Reddy Marpu - - PowerPoint PPT Presentation

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Environment Remote Sensing Instructor: Prof. Prashanth Reddy Marpu - - PowerPoint PPT Presentation

Oct 28, 2022 291 likes •1.1k views

Environment Remote Sensing Instructor: Prof. Prashanth Reddy Marpu SAMPLE METEOSAT SEVIRI CHANNELS 96 acquisitions per day (12 channels in each acquisition) Channel 04 (IR3.9) Channel 09 (IR10.8) MODIS Data Real-Time Monitoring of Dust Sources

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Environment Remote Sensing

Instructor:

  • Prof. Prashanth Reddy Marpu
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Channel 04 (IR3.9) Channel 09 (IR10.8)

SAMPLE METEOSAT SEVIRI CHANNELS

96 acquisitions per day (12 channels in each acquisition)

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MODIS Data

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RGB composite image captured on March 19, 2012 (Masdar Institute receiving station)

Satellite-based dust monitoring tool

Real-Time Monitoring of Dust Sources in the Region

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Recent Dust Storm MODIS Aqua Image March 18, 2012 (NASA)

Real-Time Monitoring of Dust Sources in the Region

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Recent Dust Storm MODIS Aqua Image March 19, 2012 (NASA)

Real-Time Monitoring of Dust Sources in the Region

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ASTER maps of LST, land cover and ISA percentage (Summer) ASTER maps of LST, land cover and ISA percentage (Winter)

Thermal Mapping (Abu Dhabi)

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Water Quality Assessment and Monitoring

  • Protecting seawater intakes for

major desalination plants in the United Arab Emirates: Developing an automated tool for oil spills detection and monitoring using active microwave satellite data.

  • Developing a fluorescence-

based model for MODIS Satellite to detect and monitor red tide

  • utbreaks in the Arabian Gulf.
  • Using medium and high

resolution satellite images in monitoring water quality surrounding the discharges of desalination plants in the UAE

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Water Quality Assessment and Monitoring

Monitoring water quality surrounding the discharges of desalination plants in the UAE using medium and high resolution satellite images.

10 20 30 40 50 5 10 15 20 25 30 35 40 45 50 0.05 0.1 0.15 0.2 0.25 10 20 30 40 50 5 10 15 20 25 30 35 40 45 50 0.05 0.1 0.15 0.2 0.25 10 20 30 40 50 5 10 15 20 25 30 35 40 45 50 0.05 0.1 0.15 0.2 0.25

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Example: MODIS products derived from a scene acquired over Abu Dhabi coastline

MODIS/Aqua RGB image March 20, 2005, 9:30 GMT

RGB true-color composite shows the clear atmosphere SeaDAS-derived total suspended sediment (TSS) concentrations (mg/L).

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Slides adopted from Jensen, 2007 and lecture notes of Dr. Mathias Disney, UCL Geography, University College London

REMOTE SENSING

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One of the first RS images using 7 Kites carrying a 23 Kg camera

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EM Spectrum

Spectrum: For the purpose of this workshop – Reflective / Emissive

VNIR SWIR MWIR LWIR

Reflective Emissive microns

Base image - http://upload.wikimedia.org/wikipedia/commons/7/7c/Atmospheric_Transmission.png

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Electromagnetic Spectrum- Visible

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Departure from blackbody radiation

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  • Spatial: the size of the field-of-view, e.g. 10 x 10 m.
  • Spectral: the number and size of spectral regions

the sensor records data in, e.g. blue, green, red, near- infrared thermal infrared, microwave (radar).

  • Temporal: how often the sensor acquires data over

the same location, e.g. every 15 min, 30 min, 12 hrs, 5 days…etc.

  • Radiometric: the sensitivity of detectors to small

differences in electromagnetic energy.

Remote Sensor Resolutions

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The Next Era of Satellite Remote Sensing Systems

Today, we are on the verge of…

1972 - 80m resolution Today - 0.6m resolution

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30 meter resolution 1982 Landsat Technology 20 meter resolution 1986 SPOT Technology 10 meter resolution 1986 SPOT Technology 1 meter resolution Technology Available Since 1999

Spatial Resolution

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WorldView

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Spectral Resolution

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Spectral Resolution

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Airborne Visible Infrared Imaging Spectrometer (AVIRIS) Data cube of Sullivan’s Island Obtained

  • n October 26, 1998

Color-infrared color composite on top

  • f the datacube was

created using three

  • f the 224 bands

at 10 nm nominal bandwidth.

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Temporal Resolution June 1, 2006 June 17, 2006 July 3, 2006

Remote Sensor Data Acquisition

16 days

Jensen, 2007

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Radiometric Resolution

4 bit (0-15) 8 bit (0-255) 16 bit (0-65535) 32 bit (0-4.3*10^10)

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Remote Sensing Image Interpretation

  • 1) Visual interpretation
  • 2) Digital image processing for information

extraction from sensor data sets

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Digital image processing (computer-based)

Computer-based analysis and reprocessing of raw data into new visual or numerical products, which then are interpreted either by approach 1 or are subjected to appropriate decision-making algorithms that identify and classify the scene objects into sets of information

The techniques fall into three broad categories:

Image Restoration and Rectification Image Enhancement Image Classification

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Image representation

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Keys for image interpretation

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What do you see?

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Image interpretation is a combination of experience and adaptability. The following are all important while interpreting satellite images. Spectral information, Shape, Size, Texture and Context

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Classification of RS data

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What is classification?

Classification is the task of relating pixel information in a digital image to ground truth based on spectral, spatial and contextual information.

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Classification

  • Supervised
  • Requires examples based on ground truth to train the classifiers.
  • The ground truth classes may not contain pixels with similar spectra.
  • Unsupervised
  • Clusters the data and assigns the corresponding clusters to the classes based on

user input.

  • The maximally-separable clusters in spectral space may not match our perception
  • f the important classes on the landscape.
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Supervised classification

  • 1. Selecting training regions
  • 2. Training the classifier
  • 3. Validating the results based on a test set

From Lillesand, Kiefer and Chipman (2004)

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Coal fire prone areas mapping in China

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Classification

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Hyperspectral Imaging Applications

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Hyperspectral Imaging Applications

Hyperspectral imaging using an airborne platforms can be used in surveillance applications such as: 1) Camouflage target detection. 2) Search and rescue. 3) Illegal disposal of waste. 4) Monitoring water quality in the gulf (e.g., algal blooms)

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  • Dynamic changes in Landscape (Human activities, natural disasters,

changes in vegetation cover, shrinking of glaciers due to global warming, etc).

  • Need for updating maps in short intervals.
  • Earth observation (EO) data are increasingly being made available

with better resolution.

  • Need to develop efficient methods to map changes in a timely

manner and maximize the automation to process huge amounts of data.

Change Detection

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2002 2003

D = aTF - bTG

  • Determination of a and b, so that the positive correlation between U = aTF and V = bTG is minimized.
  • Canonical correlation analysis (Hotelling, 1936).
  • Fully automatic scheme gives regularized iterated MAD variates, invariant to linear/affine

transformations, orthogonal. (Nielsen et al, 1998; Nielsen, 2007)

U = aTF V = bTG

MADs 2 (R), 3 (G), 4 (B)

Multivariate Alteration Detection

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Our approach

Time 1 Time 2 Initial change mask Change detection using IR-MAD method Post-processing Change map

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Strategies for generating the ICM

  • Multispectral images
  • The data are stretched in the range of

0-255 and the maximum difference between two times, measured over all the bands, is calculated.

  • The resulting difference image is

modelled as a mixture of 3 Gaussians and a threshold is identified to eliminate strong changes.

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Multispectral change detection using IR-MAD and initial change mask.

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Experiments with ICM

Landsat ETM+ images over Juelich, Germany taken in May, June and August, 2001. Major changes due to agricultural regions. Only the human settlement areas and mining area remain unchanged.

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Experiments

May- June May- August

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Experiments

Without Mask With Mask May -June

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Experiments

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Experiments

Without Mask With Mask Automatic radiometric normalization

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Experiments

May- August Without Mask With Mask

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Experiments

Without Mask With Mask Automatic radiometric normalization

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CD with image segments

The effect of the noise is reduced and hence better projections are identified using MAD transformation.