Chapter 5: Remote sensing

Electronics and Telecommunication Engineer um Civil Aviation Authority of Nepal, Tribhuvan International Airport, Gauchar, Kathmandu
27. Nov 2017

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Chapter 5: Remote sensing

  1. Chapter 5
  2.  In order to take advantage of and make good use of remote sensing data, we must be able to extract meaningful information from the imagery.  This brings us to the topic of discussion of interpretation and analysis.
  3.  Interpretation and analysis of remote sensing imagery involves the identification and/or measurement of various targets in an image in order to extract useful information about them.  Targets in remote sensing images may be any features or object which can be observed in an image, and have the following characteristics
  4.  Targets may be a point, line or area.  This means that they can have any form, from a bus in a parking lot or plane on a runway, to a bridge or roadway to a large expanse of water or a field.  The target must be distinguishable; it must contrast with other features around it in the image.
  5.  Much interpretation and identification of targets in remote sensing imagery is performed manually or visually, by a human interpreter.  In many cases this is done using imagery displayed in a pictorial or photograph-type format, independent of what type of sensor was used to collect the data and how the data were collected.  In this case we refer to the data as being in analog format.  Remote sensing images can also be represented in a computer as arrays of pixels, with each pixel corresponding to a digital number representing the brightness level of that pixel in the image.  Both analogue and digital imagery can be displayed as black and white images or as color images.  When remote sensing data are available in digital format, digital processing and analysis may be performed using a computer.
  6.  Digital processing may be used to enhance data as a prelude to visual interpretation.  Digital processing and analysis may also be carried out to automatically identify targets and extract information completely without manual intervention by a human interpreter.  However, rarely is digital processing and analysis carried out as a complete replacement for manual interpretation. Often it is done to supplement of remote sensing for air photo interpretation. Method Merit Demerit Human (Image Interpretation) • Interpreter’s knowledge are available • Excellent in spatial information extraction • Time Consuming • Individual difference Computer (Image processing) • Short processing time reproductively • Extraction of physical quantities or indices is possible • Human Knowledge is unavailable • Spatial information extraction is poor.
  7.  Each pixel is characterized for a brief time by some single value of radiation (e.g., reflectance) converted by the photoelectric effect into electrons and then to a number (see illustration at right)  The area coverage of the pixel (that is, the ground cell area it corresponds to) is determined by instantaneous field of view (IFOV) of the sensor system.
  8.  Resolution is defined as a measure of the ability of an optical system or other system to distinguish between signals that are spatially near or spectrally similar.
  9. • Spatial - the size of the field-of-view, e.g. 10 x 10 m. • Spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared, thermal infrared, microwave (radar). • Temporal - how often the sensor acquires data, e.g. every 30 days. • Radiometric - the sensitivity of detectors to small differences in electromagnetic energy. • Spatial - the size of the field-of-view, e.g. 10 x 10 m. • Spectral - the number and size of spectral regions the sensor records data in, e.g. blue, green, red, near-infrared, thermal infrared, microwave (radar). • Temporal - how often the sensor acquires data, e.g. every 30 days. • Radiometric - the sensitivity of detectors to small differences in electromagnetic energy. GG RR NIRNIRBB 10 m10 m 10 m10 m JanJan 1515 FebFeb 1515
  10.  The fineness of detail visible in an image. ◦ (course) Low resolution – smallest features not discernable ◦ (fine) High resolution – small objects are discernable  Factors affecting spatial resolution ◦ Atmosphere, haze, smoke, low light, particles or blurred sensor systems  Spatial resolution is ability to distinguish between two closely spaced objects on an image.  Spatial resolution depends on the field of view (FOV) , altitude and viewing angle of a sensor.  The detail discernible in an image is dependent on the spatial resolution of the sensor and refers to the size of the smallest possible feature that can be detected.
  11.  Spatial resolution of passive sensors depends primarily on their instantaneous field of view (IFOV).  The IFOV is the angular cone of visibility of the sensor and determines the area on the earth’s surface which is seen from a given altitude at one particular moment of time.  The IFOV is the angular cone of visibility of the sensor and determines the area of visibility of the sensor and determines the area on the earth surface.  The IFOV may also be defined as the area on the ground, which viewed by a single instrument from a given altitude at any given instant of time.  This area on the ground is called the resolution cell and determines a sensor spatial resolution.
  12.  The term spectral resolution refers to the width of spectral bands that a satellite imaging system can detect. Often satellite imaging systems are multi- spectral meaning that they can detect in several discrete bands, it is the width of these bands that spectral resolution refers too. The narrower the bands, the greater the spectral resolution.
  13.  Spectral resolution describes the ability of a sensor with to define fine wavelength intervals.  The finer the spectral resolution , the narrower the wavelength range for a particular channel or band. 
  14.  Resolution refers to the dimension spectral and number of wavelength regions (or band) in the EM spectrum to which the sensor is sensitive.  Based on the spectral resolution the sensors fall into the following groups: Broad-band, Narrow band, spectral and hyper spectral sensors.  It uses advanced multichannel sensors.  Detect hundreds of very narrow spectral bands throughout the visible near infrared and mid-infrared portions of the EM spectrum.  Their very high spectral resolution facilities fine discrimination between different targets based on their spectral response in each of the narrow bands.
  15. SpectralSpectral ResolutionResolution SpectralSpectral ResolutionResolution
  16.  Temporal resolution is a measure of how often data are obtained for the same area (i.e. how often an area can be revisited)  The temporal resolution varies from hours for some systems to about 20 days to others.  High temporal resolution: Daily or twice daily
  17. June 1, 2006June 1, 2006June 1, 2006June 1, 2006 June 17, 2006June 17, 2006June 17, 2006June 17, 2006 July 3, 2006July 3, 2006July 3, 2006July 3, 2006 Remote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data AcquisitionRemote Sensor Data Acquisition 16 days16 days16 days16 days
  18.  The radiometric resolution of an imaging system describes its ability to discriminate very slight difference in energy.  Radiometric resolution is a measure of the sensitivity of a sensor to differences in the intensity of the radiation measured in sensor.  Radiometric resolution is a measure of how many grey levels are measured between pure black and pure white.  The radiometric resolution measured in bit.  E.g. 1 bit system (2^1=2) only two radiation levels and 2-bit system measures four levels etc.
  19. 8-bit8-bit (0 - 255)(0 - 255) 8-bit8-bit (0 - 255)(0 - 255) 9-bit9-bit (0 - 511)(0 - 511) 9-bit9-bit (0 - 511)(0 - 511) 0 0 0 7-bit7-bit (0 - 127)(0 - 127) 7-bit7-bit (0 - 127)(0 - 127)0 10-bit10-bit (0 - 1023)(0 - 1023) 10-bit10-bit (0 - 1023)(0 - 1023)
  20.  Radiometric resolution, or radiometric sensitivity refers to the number of digital levels used to express the data collected by the sensor. In general, the greater the number of levels, the greater the detail of information. 
  21.  Suppose you have a digital image which has a radiometric resolution of 6 bits. What is the maximum value of the digital number which could be represented in that image?
  22.  The number of digital values possible in an image is equal to the number two (2 - for binary coding in a computer) raised to the exponent of the number of bits in the image. The number of values in a 6-bit image would be equal to 26 = 2 x 2 x 2 x 2 x 2 x 2 = 64. Since the range of values displayed in a digital image normally starts at zero (0), in order to have 64 values, the maximum value possible would be 63.
  23.  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.
  24.  Analysis of remote sensing imagery involves the identification of various targets in an image and those targets may be environmental or artificial features which consists of points, lines and areas.  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.  Recognizing targets is the key to interpretation and information extraction. Basic elements of image interpretation are:
  25.  Tone  Shape  Size  Pattern  Texture  Shadow  Site, Situation and Association
  26.  It refers to the relative brightness or color of objects in an image.  Generally, tone is the fundamental element for distinguishing between different targets or features.  Variations in tone also allows the elements of shape, texture, and pattern of objects to be distinguished.
  27.  It refers to the general form, structure, outline of individual objects.  Shape can be very distinctive clue for interpretation  Straight edge shape typically represents urban or agricultural targets, while natural features such as forest edge more irregular in shape, except where man has created a road or clear cuts.  Farm or crop land irrigated systems would appear as circular shape.
  28.  Size of objects in an image is a function of scale  It is important to assess the size of a target relative to other objects in a scene as well as the absolute size, to aid in the interpretation of that target.  A quick approximation of target size can direct interpretation to an approximate result more quickly.  For e.g. if an interpreter had to distinguish zones of land use and had identified an area with a number of buildings in it, large buildings such as factories or warehouses would suggest commercial property, whereas small buildings would indicate residential use.
  29.  Pattern refers to the spatial arrangement of visibility discernible (detectable) objects.  Typically an orderly repetition of similar tones and textures will produce a distinctive and ultimately recognizable pattern.  Orchards with evenly spaced trees and urban streets with regularly spaced houses are good examples of pattern.
  30.  It refers to the arrangement and frequency of tonal variations in particular areas of an image.  Rough texture would consists of mottled tone where gray levels change abruptly in small area, where as smooth texture would have very little tonal variation.  Smooth texture are most often the result of uniform, even surfaces, such as field or grass lands.  A target with a rough surface and irregular structure such as forest canopy, results in a rough textured appearance.  Texture is one of most important element for distinguishing features in RADAR imagery.
  31.  It is also very helpful in interpretation as it may provide an idea of profile and relative height of target which may make identification easier.  However, shadows can also reduce or eliminate interpretation in their area of influence, since target within shadow are much less detectable from their surrounding.  Shadow is also helpful for enhancing or identifying topography and landform, particularly in RADAR imagery.
  32.  It takes into account the relationship between other recognizable objects or features in proximity to the target of interest.  The identification of features that one would expect to associate with other features may provide information to facilitate identification.  For example, commercial properties may be associated with proximity to the major transportation routes, where as residential areas would be associated with schools, playgrounds, sport field. Other example a lake is associated with boats, a marine and adjacent recreational land.
  33.  The criteria of image interpretation is as follows:  Image reading is an element in the form image interpretation. It corresponds to simple identification of objects using such elements as shape, size, pattern, tone, texture, color, shadow and other associated relationship.  Image measurement is the extraction of physical quantities such as length, height, location, density, temperature and so on, by using reference data or calibration data.  Image analysis is the understanding of the relationship between interpreted information and the actual status or phenomenon and to evaluate the situation.  The image interpretation process is clearly shown in figure below.
  34. Figure: Image Interpretation process