center for remote sensing and spatial analysis, rutgers university remote sensing: digital image...

Center for Remote Sensing and Spatial Analysis, Rutgers University

Remote Sensing: Digital Image Analysis


Lecture Notes 1: Overview of Remote Sensing

A number of these slides were originally produced by Scott Madry and Chuck Colvard with some subsequent modification by Rick Lathrop. Additional slides were produced by Rick Lathrop.


The remote sensing cycle


Design of A Remote Sensing Effort

• Clear definition of the problem and information need

• Evaluation of the overall potential of remote sensing

• Identification of appropriate remote sensing data & acquisition procedures

• Determination of the data interpretation & analysis techniques

• Identification of the criteria by which the quality of information can be evaluated


Resolution• Four kinds of resolution determined by user needs:• Spatial Resolution: How small an object do you need

to see (pixel size) and how large an area do you need to cover (swath width)

• Spectral Resolution: What part of the spectrum do you want to measure

• Radiometric Resolution: How finely do you need to quantify the data

• Temporal Resolution: How often do you need to look


Detection vs Discrimination vs Identification

• Detection: spectral signal from object of interest is above background noise

- there is some kind of signal there• Discrimination: spectral signal from object of interest

is detectable and also different from surrounding features

- I can discern a distinct feature • Identification: spectral signal whose spectral pattern

can be discriminated and uniquely attributed to a specific type of biophysical surface material or object

- I can positively identify the feature


Spatial resolution

Instantaneous Field of View (IFOV)determines the dimension, D, of the Ground Resolution Cell (GRC) imaged on the ground

IFOV


In scanning systems, the Ground Resolution Cell (GRC) is similar to the concept of a Ground Sample Distance (GSD) in digital cameras. While not always strictly true, the GSD and GRC are equated with the pixel size of the image projected onto the ground. No guarantee that you will be able to discriminate objects that are the same size as the GSD – depend where the pixels fall in relation to the object of interest

How small a GRC do I need?

Object same size as GSD but doesn’t dominate any one pixel


General Rule of Thumb: GRC should be less than one half the size of the smallest object of interest (which at a minimum equals to 4 pixels for simple square object).


For identification purposes, will often need to be much smaller, i.e., need multiple pixels within object.


19951 meter ground spatial resolution per

pixel

20021 foot ground spatial resolution per pixel

Digital Orthophotography: the new standard


Mixed pixels: more than 1 land cover within GRC

Landsat TM 30m pixel/GRC boundaries on IKONOS 4m pixel image backdrop


Can you identify the rectangular objects in the IKONOS image?


Spatialresolutionkeeps gettingbetter...


Spatial resolution


1, 3, and 10 meters


ultra-high spatial resolution

• 24 inch (60 cm)

• 6 inches (15 cm)


Tradeoffs: Swath width vs. GRC vs. disk storageLandsat GRC:30m SW:185km

SPOT GRC:10-20m SW:60 km

IKONOS GRC:1-4m SW:11km• 80 m = 40 Mb-4 bands (MSS)• 30 m = 320 Mb-6 bands (TM)• 10 m = 342.25 Mb-1band• 1 m = 34.225 Tb - 1 band

How broad of a region do we need?

How much data can we store and process? 185 by 185 km


Spectral Resolution: slicing up the EMR


The electromagnetic spectrum

Comparative Sizes: from subatomic to human scales

Atom Nucleus

Atom

Molecule

Bacteria

Pinhead

Honeybee

Human & larger

From NY Times graphic 4/8/2003


Spectral wavebands of Landsat TM


Landsat TM-7 bands-8 bit dataSpectral(where we look)

Radiometric(how finely can wemeasure the return)0-63, 0-255, 0-1023

Landsat TM BAND 1 2 3 4 5 7 6


An example-plant leaves• Chlorophyll absorbs large % of red and

blue for photosynthesis- and strongly reflects in green (.55um) um=micrometers or microns=1 millionth of a meter

• Peak reflectance in leaves in near infrared (.7-1.2um) up to 60% of infrared energy per leaf is scattered up or down due to cell wall size, shape, leaf condition (age, stress, disease), etc.

• Reflectance in Mid IR (2-4um) influenced by water content-water absorbs IR energy, so live leaves reduce mid IR return


Landsat TM: each waveband provides different information about earth surface features


Hyperspectral Data: contiguous spectral channels of narrow bandwidth


AVIRIS image of Goldfield, NV http://visibleearth.nasa.gov

Hyperspectral sensing

To detect narrow absorption features of specific chemical or mineral composition in rock, soils or vegetation


4 m multi-spectral

1 m panchromatic

Space Imaging IKONOS Imagery Sample: Bound Brook NJ

Tradeoffs: Higher spectral resolution generally has lower spatial resolution


Radiometric resolution

Dark Bright

Determined by the A-to-D quantization

6 bit = 0-63, 8 bit = 0-255, 10 bit = 0-1023

•Sensitivity of the detector to differences in EMR signal strength determines the smallest difference in brightness value that can be distinguished


Radiometric resolution

• Higher radiometric resolution is especially important for quantitative applications such as sea-surface temperature mapping where the user wants to distinguish small differences in temperature


Satellite remote sensing orbits give repeat coverage

• Geostationary Polar Sun-synchronous• Constant view of hemisphere Covers entire Earth

35,800 km

700-900 km


Hurricane Isabel hits the Outer Banks

http://www.noaanews.noaa.gov/stories/s2091.htm

Sept 18, 2003 from NOAA satellite image


SPOT has steerable mirror to increase overpass frequency


Change Detection• The ability to

monitor change is one of the benefits of remote sensing

• We can monitor human and natural changes in the landscape


Many different systems. Which to choose?


Different sensors and resolutionssensor spatial spectral radiometric temporal----------------------------------------------------------------------------------------------------------------AVHRR 1.1 and 4 KM 4 or 5 bands 10 bit 12 hours 2400 Km .58-.68, .725-1.1, 3.55-3.93 (0-1023) (1 day, 1 night) 10.3-11.3, 11.5-12.5 (um)Landsat TM 30 meters 7 bands 8 bit 16 days

185 Km .45-.52, .52-.6, .63-.69, .76-.9, 1.55-1.75, 10.4-12.5, 2.08-2.3 um

SPOT 10m P / 20m X P -1 band X- 3 bands 8 bit 26 days 60 Km P - .51-.73 um (0-255) (2 out of 5)

X - .5-.59, .61-.68, .79-.89 um IRS1 5.8 meters 1 band 6 bit 22 days

70 km .5-.75 (0-63)

IKONOS 1m P/ 4m X P -1 band .45-.9 10 bit 1-2 days11 km X-4 bands.44-.51, .52-.60, .63-.70, .76-.85 um (0-1023) (1.5 out of 3)

Quickbird .6-1m P/ 2.5-4m X P -1 band .45-.9 11 bit 1-2 days

16-21 km X-4 bands .45-.52, .52-.60, .63-.69, .76-.90 um

GeoVantage .1-1.1m 4 bands .41-.49m .51-.59, .61-.69, .80-.90 um 8 bit airborneDigital Camera .15-1.5km


Image Interpretation & Analysis

•Strong trend towards GIS-ready digital output products

•Computerized image analysis can help to enhance and extract information content of imagery in a time-efficient, cost-effective manner for direct input to GIS

•Computers can not replace the human image analyst; visual interpretation is still a valued technique

•Many recent advances in image analysis software


Digital Image Analysis software


• Design of a remote sensing effort must clearly define

information needs, analysis procedures and consider the 4 types of remote sensing resolution

spatial

spectral

radiometric

temporal

when considering the types of imagery to use

Remote Sensing - Summary

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