Spectral Signatures and Image Interpretation

 The Electromagnetic Spectrum:

• The electromagnetic spectrum ranges from the shorter wavelengths (including gamma and x-rays) to the longer wavelengths (including microwaves and broadcast radio waves).

• There are several regions of the electromagnetic spectrum which are useful for remote sensing.

Figure 1: The Electromagnetic Spectrum

Image Interpretation:

•  Analysis of remote sensing imagery involves the identification of various targets in an image.

•  Targets may be defined in terms of the way they reflect or emit radiation.

•  This radiation is measured and recorded by a sensor, and ultimately is depicted as an image product such as an air photo or a satellite image.

•  Act of examining images to identify objects and judge their significance.

•  Information extraction process from the images.

•  An interpreter is a specialist trained in study of photography or imagery, in addition to his own discipline.

•  Involves a considerable amount of subjective judgment.

•  Image is a pictorial representation of an object or a scene.

•  Image can be analog or digital.

•  A digital image is made up of square or rectangular areas called pixels.

•  Each pixel has an associated pixel value which depends on the amount reflected energy from the ground.

Figure 2: Image Structure

Figure 3: Image Pixel Value

Figure 4: Hyperspectral Cube

Figure 5: Band Combination

What makes interpretation of imagery more difficult than the everyday visual interpretation of our surroundings?

• We lose our sense of depth when viewing a two dimensional image, unless we can view it stereoscopically so as to simulate the third dimension of height.

• Viewing objects from directly above also provides a very different perspective than what we are familiar with.

• Combining an unfamiliar perspective with a very different scale and lack of recognizable detail can make even the most familiar object unrecognizable in an image.

• Finally, we are used to seeing only the visible wavelengths, and the imaging of wavelengths outside of this window is more difficult for us to comprehend.

• Spectral resolution = part of the EM spectrum measured.

• Radiometric resolution = smallest differences in energy that can be measured.

• Spatial resolution = smallest unit area measured.

• Revisit time (temporal resolution) = time between two successive image acquisitions over the same area.

Advantages of Using Images over ground observation:

• Synoptic view

• Time freezing ability

• Permanent record

• Spectral resolution

• Spatial resolution

• Cost and time effective

• Stereoscopic view

• Brings out relationship between objects

Spectral Signature:

•  Identity is whatever makes an entity recognizable.

•  A signature is that which gives an object or piece of information its identity.

•  Characteristic feature which forms key to enable an object to be identified.

•  Spectral, Spatial, temporal and polarization variations which facilitate discrimination of features on remotely sensed data.

What is a spectral reflectance curve:

A spectral reflectance curve is a graph of the spectral reflectance of an object as a function of wavelength and is very useful for choosing the wavelength regions for remotely sensed data acquisition for a certain application.

Figure 6: spectral reflectance curve

Significance of spectral signature in remote sensing:

• Spectral responses measured by RS sensors over various features.

• Spectral reflectance & spectral emittance curves.

• Variability of spectral signature: useful for evaluation of condition, not for spectral identification of earth features.

• Temporal and spatial effects on spectral response patterns.

• Change detection depends on temporal effects.

Spectral Signature for Vegetation:

•  A general characteristic of vegetation is its green colour caused by the pigment chlorophyll.

•  Chlorophyll reflects green energy more than red and blue energy, which gives plants green color.

Figure 7: Vegetation Reflection

Figure 8: Spectral Signature for Vegetation

Figure 9: Spot image

Figure 10: IKONOS image

•  The major difference in leaf reflectance between species, are dependent upon leaf thickness.

•  It affects both pigment content and physiological structure.

Figure 11: Vegetation Reflection

Figure 12: Thick leaf

Figure 13: Thin leaf

•  Leaf reflectance is reduced as a result of absorption by three major water absorption bands that occur near wavelengths of 1.4 micrometer, 1.9 m and 2.7 micrometer and two minor water absorption bands that occur near wavelengths of 0.96 micrometer, and 1.1 micrometer

Figure 14: Leaf spectral reflectance signatures in terms of moisture content

Needle-leaf trees canopies reflect significantly less near-infrared radiation compared to broad-leaf vegetation.

Figure 15: Coniferous forest

Figure 16: Deciduous forest

Immature leaves contain less chlorophyll and fewer air voids than older leaves, they reflect more visible light and less infrared radiation.

Figure 17: Mature plant

Figure 18: Immature plant

Figure 19: Leaf Maturity Signature

Reflectance is also affected by health of vegetation

Figure 20: Reflectance is also affected by health of vegetation

Spectral Signature for Soil:

The five characteristics of a soil that determine its

reflectance properties are, in order of importance:

• Moisture content

• Organic content

• Structure

• Iron oxide content

• Texture

Figure 21: Soil Reflection

Soil Moisture:

•  A wet soil generally appears darker

•  Increasing soil moisture content lowers reflectance but did not change shape of the curve

Figure 22: Dry Soil

Figure 23: Wet Soil

Figure 24: Soil Moisture Signature

Organic content:

•  A soil with 5% or more organic matter usually appears black in colour

•  Less decomposed organic materials have higher reflectance in the near ir

•  Very high decomposed organic materials show very low reflectance throughout the reflective region of the solar spectrum

Figure 25: Organic Content of soil

Soil – Iron Content:

•   The presence of iron especially as iron oxide affects the spectral reflectance

•   Reflectance in the green region decreases with increased iron content, but increases in the red region

•   Iron dominated soils have strong absorption in Mir (> 1.3 micrometer)

Figure 26: Spectral Reflectance curve of Soil-Iron

Representative reflectance spectra of surface samples of 5 minerals soils; (a) High organic content, moderately fine texture; (b) Low organic, Low iron content; (c) Low organic, medium iron content; (d) High organic content, moderately coarse texture and (e) High iron content, fine texture.

Soil structure:

•  A clay soil tends to have a strong structure, which leads to a rough surface on ploughing; clay soils also tend to have high moisture content and as a result have a fairly low diffuse reflectance.

•  Sandy soils also tend to have a low moisture content and a result have fairly high and often specular reflectance properties.

Figure 27: Clayey soil

Figure 28: Sandy soil

Spectral Signature for Water:

•  Reflection of Light - Wavelengths

•  Water Depths – Shallow, Deep

•  Suspended material

•  Chlorophyll Content

•  Surface Roughness

•  The majority of radiant flux incident upon water is either not reflected but is either absorbed or transmitted.

•  In visible wavelengths of EMR, little light is absorbed, a small amount, usually below 5% is reflected and the rest is transmitted.

•  Water absorbs NIR and MIR strongly leaving little radiation to be either reflected or transmitted. This results in sharp contrast between any water and land boundaries.

Figure 29: True Color vs False color images

Spectral Reflectance of Snow:

1. GRAIN SIZE (HENCE AGE)

   •  Reflectance falls at all wavelengths as grain size increases

2.  SNOW PACK THICKNESS

   •  Reflectance of snow decreases as it ages

3.  LIQUID WATER CONTENT

   •  Even slightly melting snow reduces reflectance

4.  CONTAMINANT PRESENT

   •  Contaminations (soot, particles, etc.) Reduce snow reflection in the visible region.

•  The lines in the figure represent average reflectance curves compiled by measuring large sample features.

•  Observe how distinctive the curves are for each feature.

•  The configuration of these curves is an indicator of the type and condition of the features to which

they apply.

•  Although the reflectance of individual features will vary considerably above and below the average, these curves demonstrate some fundamental points concerning spectral reflectance.

Figure 30: Spectral Reflectance of Snow