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UNIT 5 IMAGE RESOLUTIONS
Structure
5.1 Introduction Objectives
5.2 What is Image Resolution?
5.3 Types of Image Resolutions
Spatial Resolution Spectral Resolution Radiometric Resolution Temporal Resolution
5.4 Resolution in Microwave Remote Sensing
5.5 Relationship Between Different Types of Resolution
5.6 Activity
5.7 Summary
5.8 Unit End Questions
5.9 References
5.10 Further/Suggested Reading
5.11 Answers
5.1 INTRODUCTION
In the previous unit you have studied about remote sensing platforms and also the sensors, which are used to record the ground features. Utility and importance of any remote sensing data depend on its capability. Thus, image resolution refers to the ability of a remote sensing system to record and display the finer details, including the quality of data. One of the important characteristics of remote sensing system is their capability to capture details of the ground features. These details are broadly referred to as resolution, which can be described in terms of time, space, spectral and radiometry. Resolution of a sensor system is its capability to discriminate two closely spaced objects from each other. In this unit, you will study about the resolution and its types.
Objectives
After reading this unit, you should be able to:
- discuss the basic concepts and importance of image resolution in remote sensing;
- describe the types of image resolution;
- explain resolution in microwave remote sensing; and
- discuss the relationship between different types of resolution.
5.2 WHAT IS IMAGE RESOLUTION?
The term image resolution is applied to digital images, film images, and other types of images and it describes the details that an image holds. Resolution
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can be broadly defined as ability of a remote sensor to capture and display details of the ground features. In other words, resolution refers to the level of detail to which a ground feature can be described and mapped. Resolution varies from sensor to sensor. Resolution is broadly described as coarse and fine. Data having coarse resolution have coarser information whereas data with fine resolution provide finer details. Resolution characteristics of remote sensing data determine its application potential because data of different resolutions provide different levels of details and hence are useful for mapping particular features at a specific mapping scale.
The image resolution also depends on the character of the scene that has been imaged, apart from atmospheric conditions, illumination and experience and ability of an image interpreter. Finer details can be seen in high resolution image. On the other hand a coarse or low resolution image is one with large resolution size i.e., only coarse features can be observed in the image (Fig. 5.1).
Fig. 5.1: Schematics explaining concept of resolution. How close can two points be before you cannot distinguish them?
5.3 TYPES OF IMAGE RESOLUTION
Image resolution can be measured in various ways like spatial, spectral, temporal and radiometric. Based on these parameters image resolution is categorised into following four types:
- spatial resolution – it refers to variations in the reflectance/emittance determined by the shape, size and texture of the target
- spectral resolution – it infers changes in the reflectance or emittance as a function of wavelength
- temporal resolution – it involves diurnal and/or seasonal changes in reflectance or emittance and
- radiometric resolution – it includes changes in the polarisation of the radiation reflected or emitted by an object. We will discuss in detail about each of these types of resolution.
5.3.1 Spatial Resolution
There are different definitions of spatial resolution but in a general and practical sense, it can be referred to as the size of each pixel. It is commonly measured in units of distance, i.e. cm or m. In other words, spatial resolution is a measure of the sensor’s ability to capture closely spaced objects on the ground and their discrimination as separate objects. Spatial resolution of a data depends on altitude of the platform used to record the data and sensor parameters. Relationship of spatial resolution with altitude can be understood with the following example. You can compare an astronaut on-board a space shuttle looking at the Earth to what he/she can see from an airplane. The
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Remote sensing instrument is located on a sub-orbital or satellite platform, where è of IFOV, is the angular field of view of the sensor (Fig. 5.4B). The segment of the ground surface measured within the IFOV is normally a circle of diameter D given by
D = è* H ………………...(1) where, D = diameter of the circular ground area viewed, H = flying height above the terrain, and è = IFOV of the system (expressed in radians).
The ground segment sensed at any instant is called ground resolution element or resolution cell.
Fig. 5.4: Schematics showing (A) relationship of IFOV and FOV and (B) concept of IFOV
Spatial resolution of remote sensing system is influenced by the swath width. Spatial resolution and swath width determine the degree of detail that is revealed by the sensors and the area of coverage. Remote sensing sensors are generally categorised into coarse, intermediate and high spatial resolution sensors based on their spatial resolution. Sensors having coarse resolution provide much less detail than the high spatial resolution sensors. Because of the level of details the sensors provide, they are used for mapping at different scales. High spatial resolution sensors are used for large scale mapping (small area mapping) whereas coarse spatial resolution data are used for regional, national and global scale mapping.
5.3.2 Spectral Resolution
We all know that the Sun is a major source of electromagnetic radiation used in the optical remote sensing. Different materials on the Earth’s surface exhibit different spectral reflectance and emissivities. The differences (variations) in reflectance and emissivities are used to distinguish features. However, the spectral signature does not give continuous spectral information and rather it gives spectral information at some selected wavelengths. These wavelength regions of observation are called
As you go up in the sky/ space, your field of view (FOV), i.e. the total view angle of the sensor, increases. The FOV defines the swath. Swath is the width of the strip of the ground, i.e. recorded by the camera or sensor.
(A)
(B)
gives spectral information at some selected wavelengths. These wavelength^ Image Resolutions regions of observation are called spectral bands. The spectral bands are defined in terms of a ‘central wavelength’ and a ‘band width’. For example, a sensor which is making measurements at green wavelength region (0.5 μm-0. μm) will have central wavelength 0.55 μm and band width is 0.1 μm. Besides the location of the central wavelength and band width, total number of bands is also another important aspect of spectral band selection. The number and dimension of specific wavelength intervals in the electromagnetic spectrum to which a remote sensing instrument is sensitive is called spectral resolution. The use of well-chosen and sufficiently numerous spectral bands is a necessity. The selection of spectral band location primarily depends on the feature characteristics. The finer the spectral resolution, narrower the wavelengths range for a particular band (Fig. 5.5) and as you know the values of spectral reflectance of objects averaged over different, well-defined wavelength intervals comprise spectral signature of the object or feature by which they can be distinguished.
Fig. 5.5: Spectral reflectance signature of (a) different targets including (1) cloud, (2) snow, (3) vegetation, (4) soil and (5) water along with location of IRS-P3 MOS- A, B and C sensor channels and (b) various rock types (source: Navalgund et. al, 2007 and www.nrcan.gc.ca/earth-sciences/geography-boundary/remote- sensing/fundamentals/2234)
(b)
(a)
5.3.3 Radiometric Resolution^ Image Resolutions
As the arrangement of pixels describes spatial structure of an image, the radiometric characteristics describe actual information content in an image. The information content in an image is determined by the number of digital levels (quantisation levels) used to express the data collected by the sensors. In other words, a definite number of discrete quantisation levels are used to record (digitise) the intensity of flow of radiation (radiant flux) reflected or emitted from ground features. The smallest change in intensity level that can be detected by a sensing system is called radiometric resolutions. The quantisation levels are expressed as n binary bits, such as 7 bit, 8 bit, 10 bit, etc. 8 bit digitisation implies 2^8 or 256 discrete levels (i.e. 0-255). Similarly, 7 bit digitisation implies 2^7 or 128th^ discrete levels (i.e. 0-127).
The radiometric resolution of an imaging system determines its ability to discriminate very slight differences in energy. Coarse radiometric resolution would record a scene using only a few brightness levels (i.e. at very high contrast) whereas fine radiometric resolution would record the same scene using many brightness levels. A 7 bit data is considered having coarse radiometric resolution in comparison to a 8 bit or 10 bit data. The higher the radiometric resolution of a sensor the more sensitive it is in detecting small differences in reflected or emitted energy. In other words, the higher the radiometric resolution, the better subtle differences of intensity or reflectivity can be represented. In practice, the effective radiometric resolution is typically limited by the noise level, rather than by the number of bits of representation.
As seen in Fig. 5.7, 4-bit quantisation (16 levels) seems acceptable as digitisation using a small number of quantisation levels does not affect very much the visual quality of the image. Each photograph has a dynamic range that is determined by its physical properties. Radiometric resolution also refers to the dynamic range or number of possible data-file values in each band. Dynamic range is the difference between a photograph’s lightest and darkest areas.
Fig. 5.7: Images showing the effect of degrading the radiometric resolution (source: www.crisp.nus.edu.sg/~research/tutorial/image.htm)
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5.3.4 Temporal Resolution
In addition to spatial, spectral and radiometric resolution, it is also important to consider the concept of temporal resolution in a remote sensing system. As we have studied in Unit 1 Principles of remote Sensing , one of the advantages of remote sensing is its ability to observe a part of the Earth (scene) at regular intervals. The interval at which a given scene can be imaged is called temporal resolution. Temporal resolution is usually expressed in days. For instance, IRS-1A has 22 days temporal resolution, meaning it can acquire image of a particular area in 22 days interval, respectively. Low temporal resolution refers to infrequent repeat coverage whereas high temporal resolution refers to frequent repeat coverage. Temporal resolution is useful for agricultural application (Fig. 5.8) or natural disasters like flooding (Fig. 5.9) when you would like to re-visit the same location within every few days. The requirement of temporal resolution varies with different applications. For example, to monitor agricultural activity, image interval of 10 days would be required, but intervals of one year would be appropriate to monitor urban growth patterns.
Fig. 5.8: Temporal variations of remote sensing data used to monitor changes in agriculture, showing crop conditions in different months (source: Navalgund et. al, 2007)
Fig. 5.9: Showing the importance of temporal resolution. View of the flood situation at Brisbane, Australia (a) pre flood and (b) post flood (source: http://abc.net.au)
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There are two types of microwave remote sensing: Passive and Active. In passive microwave remote sensing the wavelength are so long, the energy available is quite small compared to optical wavelengths. Thus, IFOV must be large to detect enough energy to record a signal. Most passive microwave sensors are therefore characterised by low spatial resolution. Active remote sensors are generally divided into two distinct categories: imaging and non- imaging. The most common form of imaging active microwave sensors is imaging RADAR. RADAR is an acronym for Radio Detection And Ranging , which essentially characterises the function and operation of a radar sensors. Unlike optical systems, radar’s spatial resolution depends on specific properties of the microwave radiation and its geometrical effects. Imaging radar is classified into real aperture radar (RAR) and synthetic aperture radar (SAR). Spatial resolution varies in these two cases. Radar resolution has two dimensions which are range (across-track) and azimuth (along-track) as shown in Fig. 5.10.
Fig. 5.10: Range (D) refers to the across-track dimension perpendicular to the flight direction, while azimuth (E) refers to the along-track dimension parallel to the flight direction. Similar to optical systems, the platform travels forward in the flight direction with the nadir directly beneath the platform. The microwave beam is transmitted obliquely at right angles to the direction of flight illuminating a swath which is offset from nadir (source: Gonzalez and Woods, 2002) The ground resolution cell size of a SLAR (Side-Looking Airborne Radar) system is controlled by two independent sensing system parameters: pulse length and antenna beam width. A shot of electromagnetic energy that radar
γ
sends out in a straight line to detect a target is known as pulse. It is measured^ Image Resolutions in terms of time (the interval between two such successive burst). The radar beam width is inversely proportional to the antenna length (also referred to as aperture), which implies that a longer antenna (or aperture) produces a narrower beam and finer resolution. Finer along-track resolution can be achieved by increasing the antenna length. Unlike optical systems, radar’s spatial resolution depends on specific properties of the microwave radiation and its geometrical effects. If a RAR is used for imaging purpose (as in SLAR), a single transmit pulse and the back scattered signal are used to form the image. In this case, the resolution is dependent on the effective length of the pulse in the slant range direction and on the width of the illumination in the azimuth direction. Resolution is determined by antenna beam width in the along-track direction.
The range or across-track resolution is the ability of the radar to discriminate two targets that are closely spaced in range. For example, a range resolution of 10 m means that two targets that are on the same azimuth and 10 m apart in range can be resolved. It is dependent on the length of the pulse (P), as shown in Fig. 5.11 and Fig. 5.13. Two distinct targets on the surface are resolved in the range dimension if their separation is greater than half the pulse length. For example, in Fig. 5.11, targets 1 and 2 are not separable while targets 3 and 4 can be easily separated into ground range coordinates; the resolution in ground range is dependent of the incidence angle. Thus, for fixed slant range resolution, the ground range resolution decreases with increasing range.
Tc Rr = ———— ......................................... (2) 2 cos γ
Where,
T = duration of the radar pulse, c = speed of light, and γ = depression angle
Fig. 5.11: Range or across-track spatial resolution (source: Gonzalez and Woods, 2002)
Image Resolutions
5.5 RELATIONSHIP BETWEEN DIFFERENT
TYPES OF RESOLUTION
You have read in earlier sections that the arrangement of pixels describes spatial structure of an image whereas radiometric characteristics describe the actual information content in an image. 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. In addition to above basic parameters, sensor must also have a high geometric fidelity, and images of different bands should be well registered to enable multi-spectral classification.
Most first-time users of remote sensing assume that higher resolution provides more detail which, in turn, must yield more information for feature identification and analyses. Although it is true that higher resolution generates more data, however, it is not always synonymous with more information.
The particular challenge in monitoring land areas is to capture the patterns of spatially detailed land cover change, within the context of seasonal land cover dynamics (Fig. 5.14). Imagery is always acquired within a spatial, spectral and temporal context. Fig. 5.14 depicts the relation between spatial resolution and temporal resolution in various types of data.
Fig. 5.14: Temporal vs. spatial resolution ranges for particular applications and the trade offs in terms of data density and volume (source: Davis et. al., 1991; Goward and Williams, 1997)
Images acquired repetitatively through time record the dynamics of surface cover change that result from biophysical, geochemical, and socio-economic processes operating within the Earth system. Remote sensing provides the facility of monitoring the sudden and short lived changes, requiring swift data acquisition and analyses to monitor and evaluate the impact of events such as storms, tsunamis, hurricanes, flood, effluent discharge, droughts, forest fire, locust plagues, dispersion
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rate of oil slicks and development and movement of phytoplankton blooms in estuaries. In some instances, more detail merely introduces noise that obscures identification of features of interest, for example, we do not need to discern every grassy patch in order to determine whether the area is urban or farmland.
There are trade-offs between spatial, spectral, and radiometric resolution which are taken into consideration when engineers design a sensor. For high spatial resolution, the sensor has to have a small IFOV. However, this reduces the amount of energy that can be detected as the area of the ground resolution cell within the IFOV becomes smaller. This leads to reduced radiometric resolution i.e. the ability to detect fine energy differences. To increase the amount of energy detected (and radiometric resolution) without reducing spatial resolution, we have to broaden the wavelength range detected for a particular channel or band. Unfortunately, this reduces the spectral resolution of the sensor. Conversely, coarser spatial resolution would allow improved radiometric and/or spectral resolution. Thus, these three types of resolution must be balanced against the desired capabilities and objectives of the sensor.
You have seen that the timely use of remote sensing information, which has increased over past twenty years, provides information essential for monitoring and mitigating environmental problems that occur suddenly on a big scale. The ability to obtain data rapidly and inexpensively over large geographic regions means that remote sensing can help us document the local, regional and global consequences of acute and chronic changes in ecosystems and environment. Finally, in addition to its spatial and temporal advantages remote sensing offers the advantage of a wide spectral coverage. During the geological fieldwork our eyes are capable of detecting particular lithology based on the colour and we have no difficulty in distinguishing sandstone from granite based on colour. Because different lithology reflects radiation and their colour or spectral signature often contrasts with that of the background, we can use remote sensing to identify and detect rock-types within the landscape.
Check Your Progress II
- Name the types of resolution in microwave remote sensing. ......................................................................................................................
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- Differentiate between azimuth and range resolution. ......................................................................................................................
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5.6 ACTIVITY
Take a photograph of an object in the maximum resolution (megapixel) of
Spend 5 mins
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development of terrestrial monitoring , Photogrammetric Engineering and Remote Sensing, V. 63(7), 887-900p.
- http://abc.net.au.
- http://rst.gsfc.nasa.gov.
- Navalgund, R. R., Jayaraman, V. and Roy, P. S. (2007), Remote sensing applications: an overview, Current Science, V. 93(12), 1747-1766p.
- www.crisp.nus.edu.sg/~research/tutorial/image.htm
- www.nrcan.gc.ca/earth-sciences/geography-boundary/remote-sensing/ fundamentals/ All the above websites were retrieved on 25th^ February 2011.
5.10 FURTHER/SUGGESTED READINGS
- Campbell, J. B. (1996) Introduction to Remote Sensing, 2 nd^ Ed., Taylor and Francis Co., 622p.
- Drury, S.A. (1990) A Guide to Remote Sensing , Oxford, 199p.
- Sabins, F.F. (1987) Remote Sensing Principles and Interpretation , 2 nd^ Ed., W.H. Freeman & Co., 449p.
5.11 ANSWERS
Check Your Progress I
- Spatial, spectral, radiometric and temporal
- Refer beginning of the sections 5.1 and 5.2.
- Refer section 5.3.3.
Check Your Progress II
- Range or across-track resolution and azimuth or along-track resolution.
- Refer section 5.
Unit End Questions
For question, 1 and 2 refer section 5.1 and 5.2, respectively. For question, 3 and 4 refer section 5.3 and 5.4, respectively.
