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Multi-Spectral Imaging

ByAlaa Mohammed Khattab

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Introduction. Electromagnetic Spectrum. Multi-Spectral Imaging. How multi-Spectral Imaging can be used. Multi-Spectral Cameras. Applications. Compression.

Content

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Electromagnetic Spectrum

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Blue: 450-515..520 nm. Used for atmospheric and deep water imaging.

Green: 515..520-590..600 nm. Used for imaging of vegetation and deep water structures.

Red: 600..630-680..690 nm. Used for imaging of man-made objects.

Visible Light

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RGB which lie in the visible range can be

combined to produce a full color image for viewing on a display.

But, RGB are not sufficient to specify the appearance of an object under varying illuminants and viewing conditions.

Visible Light

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Near infrared: 750-900 nm, is used primarily

for imaging of vegetation. Mid-infrared: 1550-1750 nm, is used for

imaging vegetation, soil moisture content, and some forest fires.

Far-infrared: 2080-2350 nm, is used for imaging soil, moisture, geological features, silicates, clays, and fires.

Infrared

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Green vs. NIR

Green Reflectance NIR Reflectance

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A multispectral image is one that captures

image data from two or more ranges of frequencies along the spectrum, such as visible light and infrared energy.

In multispectral images, the same spatial region is captured multiple times using different imaging modalities.

Multi-Spectral Imaging

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Recording data from the visible spectrum

showing an object as it is seen by the eye.

Take images in area of spectrum infrared region (invisible).

How can be used

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Multi-Spectral

Imaging

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Multi- spectral sys tems make it pos si ble to look

beyond those bound aries. Quest inno va tions is lead ing in multi- spectral cam era tech nol ogy.

Multispectral Cameras

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Higher res o lu tion pic tures due to larger

sensors

Able to see in Wave lengths from 250 – 2200 nm

Cam eras rang ing from 1 to 5 chan nels (a reg u lar cam era is 1 channel)

Multispectral Cameras

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More chan nels show sev eral diff er ent infor ma

tion layers

Cus tomized soft ware to ana lyze every chan nel sep a rately and combined

Com pact and light sys tems (cam era, infor ma tion stor age, GPS/ IMU integration)

Multispectral Cameras

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Remote Sensing:• The sensing platform is usually an aircraft

or satellite.• The scene being imaged is usually the

earth's surface.• The sensor and the target are so far apart, so

each pixel in the image can correspond to tens or even hundreds of square meters on the ground.

Applications

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Applications

Athens as seen by the SPOT 5 satellite in 2002Spot-5 Satellite

French Spot

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American Thematic mapper sensors feature

seven bands of image data (three in visible wavelengths, four in infrared)

Applications

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VASARI SYSTEM:

• VASARI imaging system developed at the National Gallery in London.

• we are investigating the use of seven filters spanning the visible spectrum. These seven channels can be combined to give accurate measurements.

Applications

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Applications

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 Interpretation of ancient papyri, such as those

found at Herculaneum, by imaging the fragments in the infrared range (1000 nm).

Used for document inspection and restoration as hidden markings can be become visible in infrared.

Applications

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Applications

Fingerprint Spoof Detection

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Applications

Extremely Dry Skin Multispectral

Fingerprint Spoof Detection

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Medical:• Multispectral medical images can be formed

from different medical imaging modalities such as MRI, CT, and X-ray. These multimodal images are useful for identifying and diagnosing medical disorders.

Applications

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For example ,The French SPOT and American

thematic mapper are older systems, use only a handful of spectral bands.

More modern systems, however, can incorporate hundreds of spectral bands into a single image.

Compression

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It is used to minimize transmission bandwidth

from the sensing platform to a ground station and to archive the captured image.

Steps:• Transformation.

› Karhunen-Loeve (KL).› Frequency transform.

• Encoding

Compression

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Compression

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The optimal linear transformation for de-

correlating the data is the well-known Karhunen-Loeve (KL) transform.

Frequency transform such as the discrete cosine (DCT) and wavelet transforms approach the optimal KL transform point much more quickly than many other transforms, so they are preferred in actual compression systems.

Transformation

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Multispectral data can be represented as a 2D

image with vector-valued pixels. Each pixel consists of one sample from each

image plane All spectral band:• sampled at the same resolution.• sampled over the same spatial extent.• are perfectly registered, so each pixel

component corresponds to the same exact location in the scene.

Transformation

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Transform-based multispectral coders is

categorized into three classes:

• Spectral-spatial transform.• Spatial-spectral transform.• Complex spatial-spectral transform.

Transformation

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All images:• Same spatial resolution.• Registered.

Spectral-spatial transform:

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Infrared band may have lower spatial

resolution than a visible band. All images:• Not the same spatial resolution.• Registered.

Spatial-spectral transform:

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A spatial shift in one image plane relative to

another. All images:• Not the same spatial resolution.• Not registered.

Complex spatial-spectral transform:

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Multispectral image compression algorithms

fall into two general categories:

• Lossy.• Lossless.

Compression

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Lossy algorithms typically obtain much

higher compression ratios but introduce distortions in the decompressed image.

• Ex.:› RGB color images.› Remote sensed data.

Lossy Compression

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The decoded image is identical to the original.

This gives perfect fidelity but limits the achievable compression ratio.

• Ex.:› Medical images.› Photographic images.

Lossless Compression

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Application using multispectral imaging for

search:

• Fingerprint detection.

• Detection the originality of pages, paints, documents.

• The discovery of forgery.

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