Introduction to remote sensing
This presentation is the property of its rightful owner.
Sponsored Links
1 / 64

Introduction to Remote Sensing PowerPoint PPT Presentation


  • 98 Views
  • Uploaded on
  • Presentation posted in: General

Introduction to Remote Sensing. Cons 340. Lab Review. Connect to a folder Make sure your default geodatabase is the one you are working on Save your map document in your workspace (where your geodatabase lives). Project management. Be organized

Download Presentation

Introduction to Remote Sensing

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Introduction to remote sensing

Introduction to Remote Sensing

Cons 340


Lab review

Lab Review

  • Connect to a folder

  • Make sure your default geodatabase is the one you are working on

  • Save your map document in your workspace (where your geodatabase lives)


Project management

Project management

  • Be organized

    • Lots of data from lots of sources – it’s easy to get lost

    • Develop good habits early on

      • Verify the validity of your data

      • Read metadata first

      • Check GCS and projection

    • Use a consistent file/directory naming convention


The basics

The Basics

  • An “image” is digital as opposed to a “picture” which you take with a camera

  • Images are made up of Pixels which is short for Picture Elements

  • Pixels contain values (numbers)

  • The more Pixels etc. the larger the image file size


File structure

File Structure

  • Common file formats:

    • JPEG Joint Photographic Experts Group

    • TIFF Tag Image File Format

    • GIF Graphics Interchange Format

    • BMP Bitmap File

    • PICT Macintosh Picture File

    • TGA Targa Image File

  • Graphics files typically have a header (file format info) and then a table of numbers that represent pixel values as seen on the right


Pixels and color depth

Pixels and Color Depth

  • Each pixel has numerous values associated with it

  • The # of bits in a value defines the color depth of the image

    • 1 bit = 2 colors

    • 8 bits = 256 colors

    • 16 bits = 65k colors (hi-color)

    • 24 bit = 16 million colors (true-color)

  • As color depth increases the space required for the image’s storage increases as well


Color spaces

Color Spaces

  • RGB (Red-Green-Blue; Additive)

  • CMY (Cyan-Magenta-Yellow; Subtractive)

  • CMYK (Cyan-Magenta-Yellow-Black)

  • HSV (Hue-Saturation-Value)

  • Grayscale (Shades of Gray)

  • 1-bit (line art; only two colors i.e. Black and White)

Equivalent RGB, CMY, and HSV values


Additive vs subtractive

Additive vs. Subtractive


Rgb red green blue

RGB (Red-Green-Blue)

  • An RGB image is comprised of three layers

  • RGB is an additive color space, meaning that pixel values are added to black to create new colors


Cmyk cyan magenta yellow black

CMYK (Cyan-Magenta-Yellow-Black)

  • A CMYK image is comprised of four layers

  • CMYK is a subtractive color space, meaning that pixel values are subtracted from white to create new colors


Image dimensions

Image Dimensions

  • Referred to as (Horizontal dimension by Vertical dimension)

  • (200 x 340) or (100 x 170)

  • Relates to the size of the image in bytes

    • 200 x 300 = 200 Kb

    • 100 x 170 = 50 Kb


Resolution dpi

Resolution (DPI)

  • DPI = Dots per Inch

  • The greater the DPI per equivalent areas the greater the image’s file size

  • Average screen resolution is 72 DPI

  • Typical printer resolution is 300 DPI


Spatial resolution

Spatial Resolution

  • When an image refers to something in the “real world” we say it has Spatial Resolution

  • This refers to the unit of measure in the “real world” that a pixel represents in the image

  • e.g. 30 meter Digital Elevation Models (DEM)


Which brings us to remote sensing and a selection of major rs programs

Which brings us to Remote Sensing(and a selection of major RS programs)

http://www.ersc.wisc.edu/resources/EOSC.html


Examples

Examples

This one-meter resolution satellite image of Manhattan, New York was collected at 11:43 a.m. EDT on Sept. 12, 2001 by Space Imaging's IKONOS satellite.

IKONOS travels 423 miles above the Earth's surface at a speed of 17,500 miles per hour.


Examples quickbird

Examples – Quickbird


Land surface from satellite

Land surface from satellite

  • Four landsat-5 Thematic Mapper multispectral image mosaic displayed in 4,3,2-RGB (false color)

  • August 2nd and 27th, 1998, 10:15a.m. pst.

  • 16 day repeat, 30m


Ocean color

Ocean Color

  • SeaWiFs classified ocean color image with unclassified land surface displayed 6,3,2-RGB

  • August 16th, 1999

  • Daily, 1km


Time series

Time series

One year of daily AVHRR at 1km of the Amazon Basin


A remote sensing system

A Remote Sensing System

  • Energy source

  • platform

  • sensor

  • data recording / transmission

  • ground receiving station

  • data processing

  • expert interpretation / data users


A remote sensing system1

A Remote Sensing System


Introduction to remote sensing

Energy


Basic concepts em spectrum

Basic Concepts: EM Spectrum

l 1 nm, 1mm, 1m

f 3x1017 Hz, 3x1011 Hz, 3x108 Hz,

Sometime use frequency, f = c / l,

where c = 3x108 m/s (speed of light)


You are a remote sensing platform

You are a remote sensing platform!

And your eyes are the sensors


Spectral signatures

Spectral Signatures


Spectral resolution

Spectral Resolution


Introduction to remote sensing

Platforms


Airborne platforms

Airborne Platforms

Aerial platforms are primarily stable wing aircraft, although helicopters are occasionally used.

  • Aircraft are often used to collect very detailed images and facilitate the collection of data over virtually any portion of the Earth's surface at any time.


Satellite platforms

Satellite Platforms

  • In space, remote sensing is sometimes conducted from the space shuttle or, more commonly, from satellites.

  • Because of their orbits, satellites permit repetitive coverage of the Earth's surface on a continuing basis.

  • Cost is often a significant factor in choosing among the various platform options.


Geostationary orbit

Geostationary Orbit

  • geostationary (36 000 km altitude)


Near polar orbit

Near-Polar Orbit

  • polar orbiting (200-1000 km altitude)


Introduction to remote sensing

Sensors


Cameras versus digital sensors

Cameras versus digital sensors


The business end of rs and for that matter your digital camera or camcorder

The Business End of RS (and for that matter your Digital Camera or Camcorder)

MERIS(MEdium Resolution Image Spectrometer Instrument)

charge-coupled device (CCD)

Many of these put together in a grid is referred to as a CCD Array


Passive sensor

Passive Sensor


Active sensor

Active Sensor


Radar

Radar

  • real aperture radar

    • microwave

    • energy emitted across-track

    • return time measured

    • amount of energy (scattering)

  • synthetic aperture radar

    • microwave

    • higher resolution - extended antenna simulated by forward motion of platform

    • ERS-1, -2 SAR (AMI), Radarsat SAR, JERS SAR


Introduction to remote sensing

Data


The pixel

The “PIXEL”


Visualizing numbers

Visualizing numbers


Band combinations

Band Combinations

3,2,1

4,3,2

5,4,3


Introduction to remote sensing

Issues


Spatial data resolution problem

Spatial data resolution problem

  • trade-off pixel size vs. spatial coverage

  • quantization and data volume

  • data merge from different sources

  • grid displacement in time

  • information content of different resolutions

  • raster-vector conversion


Multi resolution merging 20m multi spectral 10m pan of spot

Multi-resolution merging20m Multi-spectral + 10m PAN of SPOT


Geometric registration

Geometric registration


Introduction to remote sensing

Image

Processing


Simple ip techniques

Simple IP Techniques

  • These techniques are accomplished by applying mathematical algorithms to individual pixel values

  • e.g. Brightness simply adds a constant value to each pixel


Convolution filters

Convolution Filters

  • A matrix of multipliers that is applied to each pixel as it is moved across the image

  • They are typically moved from left to right as you would read a book


Examples of convolution filters at work

Examples of Convolution Filters at work

  • (a) Input image

  • (b) Laplacian of (a)

  • (c) sum of (a) and (b)

  • (d) Sobel gradient of (a) smoothed by a 5x5 box filter

  • (e) Product of (b) and (d)

  • (f) sum of (a) and (e)


Introduction to remote sensing

Interpretation


Image interpretation

Image Interpretation

  • Interpretation:Data Information

  • Visual Interpretation :Uses visual methods to interpret Analog Data (maps)

  • Digital Interpretation :Uses computer-based methods to interpret digital data


Visual interpretation

Visual Interpretation

Shape

Size

Pattern

Tone

Texture

Shadows

Site

Association


Image enhancement

Image Enhancement

  • Usually done to more effectively display or record the data for subsequent visual interpretation.

    • Contrast stretching

    • Filtering

    • Edge enhancement


Image transformation

Image Transformation

  • Arithmetic operations done to combine and transform the original bands into "new" images which better display or highlight certain features in the scene. e.g Normalized Difference Vegetation Index (NDVI)

  • These use multiple bands


Ndvi image

NDVI Image


Image classification

Image Classification

  • To categorize all pixels in an image into land cover classes or themes

  • Multi-spectral data are used to perform classification

  • Spectral patterns present within data used as numerical basis for categorization


Classification

Classification


Unsupervised classification

Unsupervised Classification


Supervised classification

Supervised Classification

  • In this type of classification the analyst “supervises” the classification by specifying inputs on various land cover classes to the computer

  • Analyst identifies representative ‘training areas’ for different categories

  • These training areas provide numerical spectral attributes of each land cover type.


Supervised classification1

Supervised Classification


Classified product

Classified Product


Ground truthing

Ground Truthing


Integrating remotely sensed data with gis

Integrating Remotely Sensed Data with GIS

  • What GIS has to offer remote sensing:

    - control points

    - themes

    - training sites

  • What remote sensing has to offer GIS:

    - rapid updates

    - change detection

    - vegetation indices

  • What the future may hold:

    - fully integrated systems

    - transparent data integration


Image processing software and portability of formats

Image processing software and portability of formats

  • ARC/Info GRIDvarious basic raster formats, tif, sun, gis, lan, img, bil, bip, bsq, grass, adrg, rlc

  • Arcview ERDAS lan, img, grid, tif

  • ERDAS IMAGINEArc/info live link, no conversion needed

  • PCI EASI PACEArc/Info GeoGateway for multiple formats

  • ENVI/IDLimports shapefiles, e00, dxf, USGS, SDTS, dlg,

    exportsArcView grid, uses own vector format

  • ERMAPPERvarious raster formats, import of dxf and SeisWorks, uses own vector format

  • other packages: TNT, IDRISI, ILWIS...


  • Login