Electro-optical systems Sensor Resolution

Outline Electro-optical vs. photographic systems Spatial resolution Radiometric resolution Signal-to-noise ratio Noise equivalent reflectance, radiance, temperature Spectral Resolution Temporal Resolution

Principles of Detection Photographic camera/film systems Digital electro-optical systems

Photographic Systems vs. Electro-optical Systems Both can be used to create images photography is non-scanning electro-optical systems can be either scanning or non-scanning Both record interactions with radiation films coated with photo-sensitive silver halide emulsions to record an image electro-optical systems detect radiation through an electrical system EMR detection capabilities are quite different film is always passive, uses reflected sunlight, only works in the range of 0.4 - 1.0 mm digital systems can be active or passive, can work in all parts of EM spectrum, high spectral resolution is possible, can be non-imaging system (e.g. laser altimeter)

The DN that is recorded is proportional to the radiance at the sensor Digital Number imaging optics detectors electronics at-sensor radiance DN The DN that is recorded is proportional to the radiance at the sensor

Digital Raster Imager Format

Spatial Resolution “A measure of the smallest angular or linear separation between two objects that can be resolved by the sensor”. (Jensen, 2000) Resolving power is the ability to perceive two adjacent objects as being distinct size distance shape color contrast with background sensor characteristics

IFOV is a relative measure because it is an angle, not a length Instantaneous field of view (IFOV) is the angular field of view of the sensor, independent of height IFOV is a relative measure because it is an angle, not a length It can be measured in radians or degrees sensor b IFOV

GIFOV Ground-projected instantaneous field of view (GIFOV) depends on satellite height (H) and the IFOV

IKONOS image of Gunnison River Basin, CO 1 kilometer 1 meter resolution 250 meter resolution

SPATIAL RESOLUTION

Radiometric Resolution Number of digital values (“gray levels”) that a sensor can use to express variability of signal (“brightness”) within the data Determines the information content of the image The more digital values, the more detail can be expressed

Radiometric Resolution Determined by the number of bits of within which the digital information is encoded 21 = 2 levels (0,1) 22 = 4 levels (0,1,2,3) 28 = 256 levels (0-255) 212 = 4096 levels (0-4095)

2 bit radiometric resolution

Dynamic Range Saturation Ideal Response (offset for clarity) Image Brightness Actual Sensor Response Dark Current Signal dark Scene Brightness bright

Signal Strength Need enough photons incident on the detector to record a strong signal Signal strength depends on Energy flux from the surface Altitude of the sensor Location of the spectral bands (e.g. visible, NIR, thermal, etc.) Spectral bandwidth of the detector IFOV Dwell time (more on this next week)

Signal-to-Noise Ratio (SNR) A sensor responds to both signal strength and electronic errors from various sensor components (noise) SNR = signal-to-noise ratio signal = the actual energy reaching the detector noise = random error in the measurement (all systematic noise has been removed) To be effective, sensor must have high SNR

mDN is the mean value of the DNs in the sample population n is the number of DN values in the sample population

200 201 199 203 202 sensor 50% DNs from a single detector, measured in a laboratory (pre-launch) A uniform material of known reflectance Mean DN = 201 Noise = 1.345 SNR = 201/1.345 = 149

Noise Equivalent Radiance Noise Equivalent Reflectance Noise Equivalent Temperature A measure of the smallest magnitude of signal or of a change in signal that can be detected Can be expressed in terms of radiance (L) or reflectance (r) or temperature (T)

Converting SNR to NE: Divide the noise value by the DN value recorded over a 100% reflectance target Since the sensor reads a value of 201 over a 50% reflectance target we assume that it would read 402 over a 100% reflectance target NE = 1.345402 = 0.0033 = 0.33% sensor 50%

mDN is the mean value of the DNs in the sample population n is the number of DN values in the sample population

Noise Equivalent Radiance Noise Equivalent Reflectance Noise Equivalent Temperature “Noise equivalent” is a measure of the smallest magnitude of a real change in signal that can be detected Can be expressed in terms of radiance (L) or reflectance (r) or temperature (T)

Spectral Resolution The width and number of spectral intervals in the electromagnetic spectrum to which a remote sensing instrument is sensitive Allows characterization based on geophysical parameters (chemistry, mineralogy,etc.)

Spectral Resolution Determined by: the number of spectral bands width of each band Described by the full-width at half-maximum (FWHM) spectral response function (SRF) of each band

Multiple bands:

Surface components with very distinct spectral differences can be resolved using broad wavelength ranges

Hyperspectral Remote Sensing Hundreds of bands

Temporal Resolution The frequency of data acquisition over an area Temporal resolution depends on: the orbital parameters of the satellite latitude of the target swath width of the sensor pointing ability of the sensor

Multi-temporal imagery is important for infrequent observational opportunities (e.g., when clouds often obscure the surface) short-lived phenomenon (floods, oil spills, etc.) rapid-response (fires, hurricanes) detecting changing properties of a feature to distinguish it from otherwise similar features

Examples of sensor temporal resolution: SPOT - 26 days (1, 4-5 days with pointing) Landsat - 16 days MODIS - 16 day repeat, 1-2 day coverage AVHRR – 9 day repeat, daily coverage GOES - 30 minutes TEMPORAL RESOLUTION More about this when we discuss orbits