L09 - Characteristics of remote sensing data -...

Characteristics of remote sensing

data & pre-processing

Introduction to Photogrammetry and Remote Sensing (SGHG 1473)

Dr. Muhammad Zulkarnain Abdul Rahman

Radiometric resolution

• Radiometric Resolution refers to the smallest change in intensity level that can be detected by the sensing system

• Radiometric resolution - the number of discrete quantization levels used to digitize the continuous intensity value

• The radiometric characteristics describe the actual information content in an image

• Every time an image is acquired on film or by a sensor, its sensitivity to the magnitude of the electromagnetic energy determines the radiometric resolution

• The radiometric resolution of an imaging system describes its ability to discriminate very slight differences in energy

• The finer the radiometric resolution of a sensor, the more sensitive it is to detecting small differences in reflected or emitted energy

Spectral resolution

• Radiance – is observed for a spectral band not for a single wavelength

• Spectral band – interval of EM spectrum for which average radiance is measured

• Panchromatic camera, radar, laser – only measure in one specific band

• Multispectral scanner / digital camera – measure is several spectral bands at the same time

• Multispectral sensor – several channels, one for each spectral band

Spectral band

• Hyperspectral sensor – higher spectral resolution than the multispectral sensor

• A laser instrument can emit and detect almost monochrome radiation with a wavelength band not wider than 10 nm

• Panchromatic – stands for “all across color” – brightness detector and can’t sense color

• Spectral resolution - The wavelength width of the different frequency bands recorded. Usually, this is related to the number of frequency bands recorded by the platform

• Example:

– Hyperion sensor on Earth Observing-1 resolves 220 bands from 0.4 to 2.5 μm, with a spectral resolution of 0.10 to 0.11 μm per band (Hyperspectral)

– Landsat sensor with seven bands, including several in the infra-red spectrum, ranging from a spectral resolution of 0.07 to 2.1 μm (Multispectral)

Spatial resolution• Pixels – picture elements contain DNs which correspond to the

radiance of light reflected from a small ground area viewed by the sensor

• The smaller the detector – the smaller the area on the ground which corresponds to one pixel

• The size of “spatial resolution” – refer to the pixel size on the ground

• Spatial resolution - refers to the size of the smallest object that can be resolved on the ground

• If a sensor has a spatial resolution of 20 metres and an image from that sensor is displayed at full resolution, each pixel represents an area of 20m x 20m on the ground

• If the feature is smaller than the spatial resolution, it may not be detectable as the average brightness of all features in that resolution cell will be recorded

Worldview-2

0.46 m (high spatial resolution)

Landsat TM-7

15 m (moderate spatial resolution)

MODIS

250 m (low spatial resolution)

Eruption of Mt. Etna

Temporal resolution• Temporal resolution – similar to the concept of revisit period, which refers

to the length of time it takes for a satellite to complete one entire orbit cycle

• The absolute temporal resolution of a remote sensing system to image the exact same area at the same viewing angle a second time is equal to revisit period

• The ability to collect imagery of the same area of the Earth's surface at different periods of time is one of the most important elements for applying remote sensing data

• Spectral characteristics of features may change over time and these changes can be detected by collecting and comparing multi-temporal imagery

• By imaging on a continuing basis at different times we are able to monitor the changes that take place on the Earth's surface:

– naturally occurring (such as changes in natural vegetation cover or flooding) or

– induced by humans (such as urban development or deforestation)

Pre-processing of remote sensing image

• Preprocessing functions involve those operations that are normally required prior to the main data analysis and extraction of information

• Generally grouped as radiometric or geometric corrections

• Intended to correct for sensor- and platform-specific radiometric and geometric distortions of data

• Radiometric corrections may be necessary due to:

– variations in scene illumination and viewing geometry,

– atmospheric conditions, and

– sensor noise and response

Pre-processing of remote sensing image

• Each of these will vary depending on the specific sensor and platform used to acquire the data and the conditions during data acquisition

• It may be desirable to convert and/or calibrate the data to known (absolute) radiation or reflectance units to facilitate comparison between data

• Radiometric corrections - correcting the data for sensor irregularities and unwanted sensor or atmospheric noise, and converting the data so they accurately represent the reflected or emitted radiation measured by the sensor

• Geometric corrections - correcting for geometric distortions due to sensor-Earth geometry variations, and conversion of the data to real world coordinates (e.g. latitude and longitude) on the Earth's surface

Radiometric correction: Haze removal

• Bulk Correction of Atmospheric Effects

• Frequently, detailed correction for the scattering and absorbing effects of the atmosphere is not required

• Often the necessary ancilliary information such as visibility and relative humidity (for detailed atmospheric correction) is not readily available

• In these cases: if the effect of the atmosphere is judged to be a problem in imagery, approximate correction can be carried out


• The concept and procedures:

– Assumption: each band of data for a given scene should have contained some pixels at or close to zero brightness value (e.g. area of shadow or for a very dark object such as large lake)

– Assumption: The atmospheric effects, and especially path radiance, has added a constant value to each pixel in a band

– If we observe values much greater than zero, then they are considered to have resulted from atmospheric scattering

– If histograms are taken of each band (i.e. graphs of the number of pixels present as a function of brightness value for a given pixel) the lowest significant occupied brightness value will be non-zero


• The concept and procedures (continue):

– Determine the minimum value in each histogram

– The correction is applied by subtracting the minimum value from all pixel values in each respective band

– Since scattering is wavelength dependent the minimum values will vary from band to band

Radiometric correction: Correction of

Instrumentation Errors

• Noise in an image may be due to irregularities or errors that occur in the sensor response and/or data recording and transmission

• Common forms of noise include systematic striping or banding and dropped lines

• Striping was common in early Landsat MSS data due to variations and drift in the response over time of the six MSS detectors



• The concept and procedures (linear approach):

– Assumption: the detectors used for data acquisition within a band produce signals statistically similar to each other

• if the means and standard deviations are computed for the signals recorded by the detectors then they should be the same

– Assumption: that detail within a band doesn’t change significantly over a distance equivalent to that of one scan covered by the set of the detectors (e.g. 6 line scan (detectors) for Landsat MSS)

• So differences in those statistics among the detectors can be attributed to gain and offset mismatches



• The concept and procedures:

– Mismatches can be detected by calculating pixel mean

brightness and standard deviation using lines of image

data known to come from a single detector

– Correction - by adopting one sensor as a standard

and adjusting the brightness of all pixels recorded by

each other detector so that their mean brightnesses

and standard deviations match those of the standard

detector



x - old brightness of a pixel

y - new (destriped) value

md - reference values of mean brightness

σd - reference values of standard deviation

mi – mean of the detector under consideration

σi - standard deviation of the detector under consideration

So in the case of the Landsat MSS, we have

6 histograms (6 mean and standard deviation)

Just select one detector for a reference and

force the other detectors to equal the

reference value (equalize the detector)

Sources of Geometric Distortion

• There are potentially many more sources of geometric distortion of image data than radiometric distortion and their effects are more severe

• They can be related to a number of factors, including:

(i) the rotation of the earth during image acquisition,

(ii) the finite scan rate of some sensors,

(iii) the wide field of view of some sensors,

(iv) the curvature of the earth,

(v) sensor non-idealities,

(vi) variations in platform altitude, attitude and velocity,

(vii) panoramic effects related to the imaging geometry.

Use of Mapping Polynomials for Image

Correction

• There is available a map of the region corresponding to the image, that is correct geometrically

• We then define two cartesian coordinate systems:

– 1) location of points in the map (x, y)

– 2) coordinate system defines the location of pixels in the image (u, v)

• The two coordinate systems can be related via a pair of mapping functions f and g


Correction• If these functions are known then we could locate a point in the

image knowing its position on the map

• The mapping functions are generally chosen as simple polynomials of first, second or third degree

• Example: 2nd degree / order

• If we know the coefficients a and b – we can relate the two coordinate systems

• The coefficients can be estimated using sets of features on the map that can also be identified on the image (ground control points or GCPs)


Correction• GCPs- should be well distributed and well defined

• Minimum number of GCPs for 2nd order = six

• Minimum number of GCPs for 1st order = three

• Minimum number of GCPs for 3rd order = ten

• More than the minimum number is chosen and the coefficients can estimated evaluated using least squares estimation

• Why Least squares? - any control points that contain significant positional errors either on the map or in the image will not have a large influence on the polynomial coefficients

Resampling

• The next step is to find points in the image corresponding to each location in the pixel grid previously defined over the map

• The spacing of that grid is chosen according to the pixel size required in the corrected image and need not be the same as that in the original geometrically distorted version

Interpolation

• Usually grid centres from the map-registered pixel grid will not usually project to exact pixel centre locations in the image

• Some decision has to be made therefore about what pixel brightness value should be chosen for placement on the new grid

• Three techniques can be used:

– Nearest neighbour resampling• Simply chooses the actual pixel that has its centre nearest the point

located in the image.

• This is the preferred technique if the new image is to be classified since it then consists of the original pixel brightnesses

• Preserves the original DN values of the pixels

Interpolation

– Bilinear interpolation

• Uses the four closest neighbors to perform a 2-dimensional linear interpolation to obtain the output pixel value

• This reduces the high frequency component of the image, blurring sharp edges

• DN values are altered

• Can improve image quality for visual analysis compared to the nearest neighbor approach

– Cubic convolution interpolation

• Uses the surrounding sixteen pixels

• May considerably distort the original data, but can be used to enhance data for visual display

• The most complicated processing task and requires more computations than the nearest neighbor or bilinear resampling

Choice of Control Points• Should be a distribution of control points around the edges of the image to be

corrected (well distributed)

• This is necessary to ensure that the mapping polynomials are well-behaved over the image

• The order is higher the curves pass closer to the points

• if it is presumed that the data would have continued for larger values of x with much the same trend as apparent in the points plotted then clearly the linear fit will extrapolate moderately acceptably

• The cubic curve can deviate markedly from the trend when used as an extrapolator

The higher order polynomials will be

accurate in the vicinity of the control points

themselves, which leads to significant

Errors for regions of images outside the range of the

control points

L09 - Characteristics of remote sensing data -...

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