chapter 3 remote sensing satellites, sensors and datader.topo.auth.gr/rs/tel_eng_03_satellites and...

Remote sensing satellites,sensors and data

3.1. Digital image storage and display

The primary data used in remote sensing are images of the earth surfaces,which have a digital form so that they can be stored and processed incomputers. This means that an image is in fact a set of numbers, whichrepresents a quadratic record just like a traditional analog image. Thedigitalization is achieved by dividing the depicted area into small squarepicture elements or pixels, of equal area, arranged in rows and columns.Thus the mathematical model for a digital image is a matrix of R rowsand C columns, containing CRN ×= elements, as many as the imagepixels. The matrix element ijx of the i

th row and jth column, is the valueof the corresponding pixel, representing a level of light intensity or “toneof gray” by means of a real number within a predefined range. Thesmaller possible value corresponds to “no intensity” or black color andthe largest one to “full intensity” or white color. To achieve economy incomputer storage, where the binary number system is used, only integervalues are used; with k bits disposable for each value the range ofpossible k2 values is between 0 and 12 −k . For example, in the mostcommon choice of 8-bit storage, the integer values are 25628 = and liebetween 0 (black) and 255 (white), which are stored in binary form as00000000 and 11111111, respectively.

A digital image viewed as a computerfile consists of all the image values to-

gether with some additional necessary information, which is storedin a specific initial part of the file (or sometimes in a separate file) and iscalled the header. The header contains all the necessary informa-

Chapter 3

Figure 3-1

CHAPTER 3 A. DERMANIS: REMOTE SENSING

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tion in order to reconstruct the image from the file, such as the number ofbits used for each value, the order with which the values are stored andthe number of columns in the image. It may also contain other auxiliaryuseful information about the particular image. Although analog imagescan be digitized using digitizing scanners, the vast majority of imagesused are captured directly in digital form, using sensors such as digitalcameras or scanners. These consist of sensors, which are eithersimultaneously “looking” at different parts of the recorded scene (pixels),or they are sequentially directed to different pixels, using a mechanicalscanning procedure, or they combine both possibilities.A digital image is always a “black and white” image. Color images are infact a combination of three different images of exactly the same scene,each one depicting the intensity of one of the three basic colors (redgreen and blue), which combined can reproduce all other possible colors.Since red, green and blue refer to different areas of the electromagneticspectrum, a color image is in a certain sense a “multispectral image”.A more generalized type of multispectral images is used in remotesensing. A multispectral image is an assembly of images, each onereferring to exactly the same scene and with identical partitions intopixels, where each one is a record of the intensity of electromagneticradiation only within a different area of the spectrum or spectral band.Thus a multispectral image consists of related images in differentspectral bands, which are not confined in the visible part of the spectrum.The bands used in remote sensing are in the visible and infrared part ofthe spectrum. The ultraviolet part is also rarely used, while microwavebands are used for active remote sensing, where the electromagneticenergy reflected from the earth surface and recorded, originates from aradar system aboard an airplane or satellite.The file for a multispectral image contains the values kijx for all rows

Ri ,,1= , all columns Cj ,,1= and all bands Bk ,,1= of the

image. Depending on the order with which the values kijx are stored wemay distinguish between three fundamental generic formats: BSQ, BIPand BIL.

In the Band Sequential format (BSQ) thebands are stored one after the other in their natural order (increasingwavelength), while each band is stored in the way a page is read, line byline from left to right. Thus the order of the pixel values in BSQ is thefollowing

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11

111 Cj xxx …

1111 iCiji xxx …

1111 RCRjR xxx

kC

kj

k xxx 1111 …kiC

kij

ki xxx 1 …

kRC

kRj

kR xxx 1

BC

Bj

B xxx 1111 …BiC

Bij

Bi xxx 1 …

BRC

BRj

BR xxx 1

Figure 3-2

Figure 3-3

Remote sensing satellites, sensors and data

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In the Band Interleaved by Pixel (BIP) format the values for all bands foreach pixel are stored together, while pixel arrangement follows that ofpage reading:

Bk xxx 1111111 …

Bj

kjj xxx 11

11 …

BC

kCC xxx 11

11

Bi

kii xxx 11

11 …

Bij

kijij xxx

1 … BiCkiCiC xxx

1

BR

kRR xxx 11

11 …

BRj

kRjRj xxx

1 … BRCkRCRC xxx

1

In the Band Interleaved by Line (BIL) format, priority is given to thelines, which are stored band by band. Once a line in all bands is storedthe next line follows:

11

11

111 Cj xxx …

kC

kj

k xxx 1111 …BC

Bj

B xxx 1111

1111 iCiji xxx …

kiC

kij

ki xxx 1 …

BiC

Bij

Bi xxx 1

1111 RCRjR xxx …

kRC

kRj

kR xxx 1 …

BRC

BRj

BR xxx 1

The order of storage for the three formats is depicted in figure 3.4.Unlike an analog image, a digital image has no dimensions of its own.When depicted on a computer screen, or printed on paper, it attains aparticular scale depending upon the resolution of its presentation, i.e. onthe size allocated to each pixel. The number of pixels along a unit oflength usually expresses the resolution of image recording media. Theterm dpi (dots per inch) is typically used to refer to the number of pixels(dots) per inch. For example on a screen of typical resolution 80 dpi,

64008080 =× pixels are depicted within a square inch. For a printer theminimum requirement for reasonably good printing quality is a resolutionof 300 dpi, where 90000300300 =× pixels are printed within a squareinch.A single-band image is displayed by assigning different tones of grayfrom black to white in direct analogy to the pixel values. On a color dis-plays using the RGB (R = red, G = green, B = blue) system the tones ofgray are achieved by combining the same amount of the three basic col-ors. When image bands are available in the red, green and blue parts ofthe spectrum, they can be naturally combined in order to display a “truecolor” image. For a multispectral image any three of the available bandscan be used with an arbitrary assignment of the RGB colors to produce a“false color” display of the image. Color displays certainly convey moreinformation than black and white ones and appropriate “false color”


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Figure 3-4: Formats of digital multispectral images: BSQ (=band sequentilal), BIP (band interleaved by pixel andBIL (band interleaved by line).


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combinations of bands may be found which enhance certain characteris-tics of a particular scene. Any color is uniquely define by the three valuesR, G, B assigned to the red, green and blue colors, respectively. For ex-ample in the 8-bit case, where the values from 0 to 255 are used, blackcolor corresponds to R = 0, G = 0, B = 0, white to R = 255, G = 255, B =255, while the various tones of gray to choices of common values R = G= B between 0 and 255.If the three colors are viewed as axes in 3 dimensions, each color corre-sponds to a particular point having the R, G, B values as coordinates. Inthis way all colors are contained inside the color cube (fig. 3.5) definedby the limits 2550 ≤≤ R , 2550 ≤≤ G , 2550 ≤≤ B . The tones of grayare located on the diagonal of the cube joining black (0,0,0) with white(255, 255, 255). The combination of red and green gives yellow (R=255,G=255, B=0), the combination of red and blue gives violet (R=255, G=0,B=255) while the combination of green and blue gives cyan (R=0,G=255, B=255).Another color system used in computer image processing software is theCMYK system (fig. 3.6), which is related to color printing. It producesall colors by combining cyan (C) with magenta (M), yellow (Y) andtones of gray (K). Color in printing is achieved by printing dots of vary-ing size in these four colors against the white background of the paper.The values for each colors are percentages of the maximum size, thus thezero values 0KYMC ==== corresponds to white. In order toachieve good printing quality it is necessary to separately introduce theblack color K, rather than to try to create it by combining other colors, asdone in the RGB system. The %100K = value creates black independ-ently of the values of C, M, and Y on which it is superimposed. Red iscreated by combining magenta and yellow; green by combining cyan andyellow; blue by combining cyan and magenta.A third color system the HSI (or HSB) appeals more to perception (fig.3.7). Hue (H) refers to the various colors arranged in cyclic order withcharacteristic values at red (H=0°=360°), yellow (H=60°), green(H=120°), cyan (H=180°), blue (H=240°) and magenta (H=300°). Satu-ration (S) refers to the percentage of the color defined by hue, while in-tensity (I) to the percentage of “white” where zero refers to the blackcolor. The value %0I = gives black independently of the values of Hand S. %0S = (no color) and %100I = (no black) gives white for anyvalue of H. The pure color corresponding to a specific value of H isgiven by setting in addition %100S = and %100I = . The values of grayare reproduced by varying the values of intensity I, while holding

%0S = and any value of H.


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Figure 3-5: The color cube associated with the RGB (red, green, blue) color system


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Figure 3-6: The CMYK color system. Color cubes for the CMY axes (cyan, magenta, yellow) for two different tonesof grey (K=0 and K=40)


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Figure 3-7: The HSI color system. Combination of intensity I (% of white) and saturation S (% of color) for variousangular values of hue H (color)

3.2. Satellites sensors

The first images used for extracting information about the earth surfacewere the usual analog aerial photographs, which record electromagneticradiation over the whole visible part of the spectrum. The related disci-pline, photointerpretation was based mostly on texture information in or-der to identify the various land cover classes, as well as other types of in-formation. Although texture is not irrelevant, the main source of infor-mation in remote sensing comes from variations in different spectralbands. Thus the beginning of remote sensing could be traced back to theuse of aerial photographs sensitive only to a particular band through the


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Band 1 Band 2 Band 3

Band 4 Band 5 Band 7

Band 6 R=3, G=2, B=1 R=4, G=3, B=2

R=7, G=4, B=2 R=7, G=5, B=4 R=5, G=7, B=4

Figure 3-8: The 7 bands of the TM sensors and various color combinations in the RGB system


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use of filters and in particular to the use of film sensitive to infrared ra-diation.Eventually instruments for digitally recording multispectral images havebeen developed for use on airplanes. However airborne data collectionfor remote sensing remains a very costly operation that can be under-taken only by large research institutes and governmental agencies. Whatreally gave a boost to the discipline was the placement of instruments onsatellites, which produced data in a routine way, made available to awide range of users at reasonable cost through distribution agencies.Thus the beginning of modern remote sensing could be set at thelaunching of the first Landsat satellite on July 23, 1972. The use of a se-ries of Landsat satellites by NASA has guaranteed a continuous flow ofdata, followed later from the French SPOT satellite series. Individual sat-ellite missions are also of importance, but the majority of practical workis in fact based on the sensors aboard the Landsat and SPOT satellites.In addition the development of various commercial software packagesmade remote sensing an expanding discipline, with a wide variety of ap-plications, such as thematic cartography, agriculture, forestry, geology,hazard assessment, environmental monitoring, etc. Today remote sensingis a major information provider for Geographical Information Systems(GIS) which have are an indispensable tool for the administration oflands and natural resources.Remote sensing satellites are placed in orbits around the earth, with char-acteristics that best fit their purpose. Circular orbits guarantee a constantdistance from the earth surface, while placement of the orbital planeclose to the poles guarantees a sun synchronous operation. This meansthat the orbital plane slowly changes position in inertial space, rotatingaround the earth axis with the same speed as the sun, so that half of theorbit is always over the sun-illuminated part of the earth. The height ofthe satellite affects the period of its revolution around the earth, as wellas the spatial resolution of the recorded images, which diminishes withheight. Due to the eastward rotation of the earth, the satellite after onerevolution does not return over the same part of the earth, but over a newlocation towards the west. This fact is used to “tune” the orbit so that itpasses over all parts of the earth, with the exception of two polar cups,within a fixed number of days say k days. Consequently the satellite re-visits the same location every k days and this repeat cycle determinesthe temporal resolution of the recorded data.We must distinguish between the “sensor” as an image recording instru-ment and individual sensor elements which at any moment record theamount of electromagnetic radiation within a specific spectral band ar-riving from a particular location (pixel) on the earth surface. A sensorhas zero sensitivity outside a spectral interval but its sensitivity withinthe band is not uniform. It typically has a maximum and gradually di-minishes towards the limits of the interval (fig. 2.2). The number of theavailable bands and the total part of the spectrum covered determine thespectral resolution of the sensor. Sensors with a number of bands in the


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order of ten are characterized as multispectral, those with bands of theorder of hundred as hyperspectral, while ultraspectral sensors with thou-sands of bands are anticipated as a future development.When a sensor element “looks” at a particular location, it does not recordin a homogeneous way the radiation arriving from a square pixel on theground. It is more sensitive at a central direction corresponding at thepixel center and less at the edges of the pixel. The area viewed is thus notprecisely determined, but it is typically taken to be the part where sensorsensitivity is more than 50% of its maximum. It is this circular essentialextend of the viewed area, as well as the strictly related angular distancebetween neighboring pixel centers, which determine the spatialresolution of the sensor. The actual pixel size (and shape) depends on theinclination of the direction of view with respect to the (horizontal) earthsurface. For this reason, for satellite sensors flying at a constant height,the spatial resolution is associated with the pixel size at nadir direction.For airborne sensors, where the height of flight is not constant, as in thesatellite case, pixel size at nadir is inversely proportional to the height offlight. For this reason spatial resolution is characterized by the angularopening corresponding to a pixel at nadir (fig. 3.9), which is calledInstantaneous Field of View (IFOV).As the satellite flies over the earth surface it collects data from a certainregion to the left and right of the trace of its orbit on the earth; the lateralextend of the covered region is called the swath of the sensor (fig. 3.9).The total angle of coverage corresponding to the swath is called Field ofView (FOV). The distance between neighboring satellite tracks on theearth, which becomes maximum at the equator, must not exceed theswath, in order to secure that no land strip between tracks is left uncov-ered.The detail used for the description of the amount of received electromag-netic radiation constitutes the radiometric resolution of the sensor, whichis directly related to the total number of values used. Since the number ofvalues is always of the form k2 , radiometric resolution is typically ex-pressed by the number k of bits used for storing a single value, which issometimes called dynamic range.Among the various technical characteristics of the various images wewill only discuss shortly those related with how the scanning of the areapixels is achieved. In the most simple case (fig. 3.10a) a different pixel isobserved at each instant, while pixels in a row perpendicular to the di-rection of satellite motion are sequentially scanned with a help of a ro-tating mirror which alters the direction of observation. When scanning ofa row is completed the scanning is repeated but a new image row is ob-served because of the satellite motion. The electromagnetic energy is di-verted by means of varying refraction through a prism, towards separatesensors for each spectral band. A different possibility (fig. 3.10b) is thesimultaneous observation of a whole row at a single instant, by means ofa corresponding array of sensors, one for each pixel. This is the “push-broom” scanning system used in the HRV sensors aboard the SPOT sat-

Figure 3-9


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ellites. A more advanced arrangement (fig.3.10c) allowing the simultane-ous observation of a number of rows, by means of a matrix of sensors, isthe “stepstare” scanning system. A sort of combination of the two firstsystems is the “whiskbroom” system, used on the MSS sensor aboard thefirst five Landsat satellites. An array of six sensors is observing simulta-neously six pixels in the direction of satellite motion (fig.3.10d). A singlescanning in the perpendicular direction allows the coverage of six imagerows at the same time. Repetition of the scanning allows the observationof the next six rows, and so on, along the direction of satellite motion. Inolder data when calibration of the six sensors was not very successfuland their sensitivity varied, a “stripping effect” could be seen in raw un-processed data.

Figure 3-10: Various types of scanning sensors.

Other technical characteristics is the capability of diverting the imagingsensor in different directions providing nadir and off-nadir images. Off-nadir viewing is selectively used in the SPOT satellites to provide cover-age of the same area from two different directions during different satel-lite passes. This produces stereo images that can be used in digital pho-togrammetry for the determination of terrain elevations.


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Among the various sensors we present in more detail those on the Land-sat and SPOT satellite missions, because of their importance in remotesensing applications. Other satellite missions and sensors as well as air-borne sensors are only summarized in tables.

Figure 3-11: The Landsat 7 satellite with its solar panel (right) and the ETM+sensor (left).

The NASA Landsat missions were the first series of dedicated missionsthat operate continuously since 1972, despite the unfortunate loss ofLandsat 6 on launch. They have remained for years the essential sourceof widespread data, at least until the beginning of the SPOT series in1986, which constitute the main alternative source at present. The firstthree missions carried two sensors, the most important one being theMultispectral Scanner (MSS) with four bands (4 at green, 5 at red, 6 and7 in the near infrared) with pixel size of about 80 m and a swath of 185km. An additional band at the thermal infrared was included only on theLandsat 3 mission, with smaller spatial resolution (pixel size about 240m).

Table 3.1: The Landsat satellite missions and their sensors.

THE LANDSAT SATELLITES

Satellite in orbit since until sensorsLandsat 1 23 July 1972 6 Jan. 1978 MSS, RBVLandsat 2 22 Jan. 1975 25 Feb. 1982 MSS, RBVLandsat 3 5 March 1978 31 March 1983 MSS, RBVLandsat 4 16 July 1982 July 1995 MSS, TMLandsat 5 1 March 1984 … MSS, TMLandsat 6 5 Oct. 1993 lost on launch ETMLandsat 7 15 April 1999 … ETM+

The second sensor on Landsat 1, 2, and 3, was the Return Beam Vidicon(RBV). On Landsat 1 and 2 the RBV had three bands (1 at blue, 2 in red,3 in infrared) with the same spatial resolution as the MSS. On Landsat 3


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two identical RBV sensors were placed, each with a single panchromaticband. Their spatial resolution was 40 m and the swath 98 km, which forthe two sensors overlapped into a total swath of 185 km. Following thebands 1, 2, 3 of the RBV, the MSS bands were named 4, 5, 6, 7 (and 8) inthe first three Landsat missions. Since the Landsat 4 mission the The-matic Mapper (TM) and its successors Enhanced Thematic Mapper(ETM) on the lost Landsat 6 and ETM+ on Landsat 7 replaced the RBV.In the newer missions the MSS bands were renamed from 4, 5, 6, 7 to 1,2, 3, and 4. The MSS data gave rise to extensive research in many appli-cation areas. It is characteristic of this development that techniques suchas the vegetation index (see chapter 9) originally tailored for the MSSwere later modified to fit the TM and the SPOT sensors.

Figure 3-12: The spectral regions of the 7 bands of the Landsat TM and the 4bands of the SPOT HRVIR sensor.

The Thematic Mapper aboard Landsat 4 and 5, has 7 bands (1 at blue, 2at green, 3 at red, 4, 5, at near infrared, 7 at mid infrared and 6 at thethermal infrared), a spatial resolution of 30 m (except for the thermalband 6 which has a lower resolution of 120 m) and a swath of 185 km.Note that band 7 is out of its natural (increasing wavelength) order be-cause it was added later in the original sensor design. The ETM+ oper-ating on Landsat 7 has more or less the same characteristics as the TM,with the spatial resolution of band 6 improving from 120 to 60 m and anadditional panchromatic band with 15 m spatial resolution. The TM andETM+ have a radiometric resolution of 8 bits (0-255), double the 7 bit(0-127) resolution of MSS (6 bits for band 4, formerly 7). Among variouswidely used sensors the TM and ETM+ are the ones that cover the threebasic colors (RGB) in the visible and the relevant bands can be used toproduce “true color” imagery. Another difference between the first 3Landsat missions and the following ones concerns the orbit design: Low-ering the satellite altitude from 920 to 705 km resulted in a little smallerperiod and a small improvement of the temporal resolution from a repeatcycle of 18 to one of 16 days.


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Table 3.2: Characteristics of the Multispectral Scanner.

MSS (Multispectral Scanner)Landsat 1, 2, 3, 4, 5

Spectral resolution:Band wavelength

Landsat1,2,3

Landsat4,5 (µm)

4 1 0.5 − 0.6 green5 2 0.6 − 0.7 red6 3 0.7 − 0.8 near infrared7 4 0.8 − 1.1 near infrared

8 (Landsat 3) 10.4 − 12.6 thermal infraredSpatial resolution: 79×79 m (1, 2, 3)

81.5×81.5 m (4), 82.5×82.5 m (5)Band 8: 237×237 m

Swath: 185 kmRadiometric resolution: 128 values (7 bits)

Band 8: 64 values (6 bits)Temporal resolution: 18 days (Landsat 1,2,3),

16 days (Landsat 4,5)

Table 3.3: Characteristics of the RBV sensor.

RBV (Return Beam Vidicon)Landsat 1, 2, 3 (pan.)


(µm)1 0.475 − 0.575 blue2 0.580 − 0.680 red3 0.689 − 0.830 near infrared

Pan 0.505 − 0.750 panchromatic(Landsat 3)

Spatial resolution: 79×79 mPan, Landsat 3: 40×40 m

Swath: 185 kmPan: (2×) 98 km

Temporal resolution: 18 days


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Table 3.4: Characteristics of the Thematic Mapper.

TM (Thematic Mapper)Landsat 4, 5


(µm)1 0.45 − 0.52 blue2 0.52 − 0.60 green3 0.63 − 0.69 red4 0.76 − 0.90 near infrared5 1.55 − 1.75 mid infrared7 2.08 − 2.35 mid infrared6 10.4 − 12.5 thermal infrared

Spatial resolution: 30×30 mBand 6: 120×120 m

Swath: 185 kmRadiometric resolution: 256 values (8 bits)Temporal resolution: 16 days

Table 3.5: Characteristics of the ETM+ sensor.

ETM+ (Enhanced Thematic Mapper)Landsat 7


(µm)Pan 0.52 − 0.90 panchromatic

1 0.45 − 0.515 blue2 0.525 − 0.605 green3 0.63 − 0.690 red4 0.75 − 0.90 near infrared5 1.55 − 1.75 mid infrared7 2.09 − 2.35 mid infrared6 10.40 − 12.50 thermal infrared

Spatial resolution: 30×30 mBand 6: 60×60 mPan: 15×15 m


The series of the French satellites SPOT (Système Probatoire d’ Obser-vation de la Terre) began in 1986 with 4 satellites flown up to now and afifth planned for 2002. The sensor on SPOT 1, 2 and 3 is the High Reso-lution Visible (HRV) with three bands (1 at green, 2 at red and 3 at nearinfrared), with 20 m spatial resolution, 60 km swath (per sensor) and 8bit radiometric resolution. An additional panchromatic band has a spatial


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Figure 3-13: The SPOT 4 satellite with two HRVIR sensors (left)

resolution of 10 m. On SPOT 4 (fig. 3.13) an improved version wasplaced, the High Resolution Visible InfraRed (HRVIR). The most notablechange was the addition of a fourth band at the mid infrared region. Anew sensor on SPOT 4 is the Vegetation (VGT), with low spatialresolution (1 or 4 km) but wider field of view (FOV = 101°), whichallows the fast collection (1 to 2 day resolution depending on latitude) ofaveraged global data (40° S – 60° N). The VGT has four bands (1 atblue, 2 at red, 3 at near infrared and 4 at mid infrared), a swath of 2250km and operates at two modes, the “direct” or “regional” (pixel size 1km) and the “recording” or “world-wide” mode (pixel size 4 km). On theplanned SPOT 5 mission the High Resolution Geometry (HRG) will havethe same characteristics as HRVIR, but an expected higher spatialresolution of 10 m and 3 or 5 m in panchromatic band. This will becomparable to the already available 4 m resolution multispectral imagesof the Ikonos satellite launched on September 24, 1999, at an altitude of680 km. Ikonos also provides panchromatic images of 1 m resolution inaddition to the 4 bands of multispectral data, which are identical to thebands 1, 2 and 4 of the Landsat Thematic Mapper.Each SPOT satellite carries two identical HRV or HRVIR sensors (fig.3.14), so that the individual swaths of 60 km are combined with a 3 kmoverlap into a total swath of 117 km, when both sensor observe in thenadir direction mode. The characteristic difference of the sensors is thepossibility to change the direction of view from ground control “oncommand”, with the help of a steerable mirror (fig. 3.15). In addition tothe usual nadir mode used for “thematic” mapping, oblique views (at 0.6°intervals up to ±27° away from nadir) are used to cover the same areafrom two (or more) different directions taken at different satellite passes(fig. 3.16). These converging views are necessary for the geometricmapping of the area, using digital photogrammetric tech niques.

Figure 3-14

Figure 3-15


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A typical product is a Digital Terrain Model (DTM), i.e. a file of eleva-tions in a grid over the earth surface.

Figure 3-16: Stereometric view from different passes of the SPOT satellite forthe photogrametric determination of the terrain anaglyph.

Table 3.6: The SPOT satellite missions and their sensors.

THE SPOT SATELLITES(Système Probatoire d’Observation de la Terre)

Satellite in orbit since until sensorsSPOT 1 22 Feb. 1986 1994 HRV (×2)SPOT 2 22 Jan. 1990 … HRV (×2)SPOT 3 26 Sept. 1993 14 Nov. 1997 HRV (×2)SPOT 4 24 March 1998 … HRVIR (×2), VGTSPOT 5 planned 2002 HRG (×2)


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Table 3.7: Characteristics of the HRV sensor.

HRV (High Resolution Visible)SPOT 1, 2, 3



1 0.50 − 0.59 green2 0.61 − 0.68 red3 0.79 − 0.89 near infrared

Spatial resolution: 20×20 mPan 10×10 m


Table 3.8: Characteristics of the HRVIR sensor.

HRVIR(High Resolution Visible InfraRed)

SPOT 4



1 0.50 − 0.59 green2 0.61 − 0.68 red3 0.79 − 0.89 near infrared4 1.58 − 1.75 mid infrared

Spatial resolution: 20×20 m(Band 2 also 10×10 m)

Pan 10×10 mSwath: 60 kmRadiometric resolution: 256 values (8 bits)Temporal resolution: 26 days

Table 3.9: Characteristics of the Vegetation sensor.

VGT (Vegetation)SPOT 4


(µm)1 0.43 − 0.47 blue2 0.61 − 0.68 red3 0.78 − 0.89 near infrared4 1.58 − 1.75 mid infrared

Spatial resolution: 1×1 km(also 4×4 km mode)



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Table 3.10: Characteristics of remote sensing satellites.(np = near polar, gs = geostationary)

REMOTE SENSING SATELLITES AND SENSORS

Satellite from until sensorsaltitude

h(km)

inclin.i

(deg)

periodT

(min)

repeatcycle(days)

Landsat 1, 2, 3 1972 (1) 1983 (3) MSS, RBV 920 99 103 18Landsat 4, 5 1982 (4) ... MSS, TM 705 np 98.9 16Landsat 7 1999 ... ETM+ 705 np 98.9 16SPOT 1, 2, 3 1986 (1) HRV 832 np 101 26SPOT 4 1998 HRVIR, VGT 832 np 101 26NOAA 10 - 14 1986 ... AVHRR 861 / 845 np 1ADEOS Aug 1996 July 1997 AVNIR, OCTS 797 np 101 41Nimbus 7 Dec 1986 CZCS 1000IRS-1A Mar 1988 LISS-1 904 101 22IRS-1B Aug 1991 LISS-2 904 101 22IRS-P2 Oct 1994 LISS-2 101 24IRS-1C 1995 LISS-3, WIFS 817 101 24IRS-1D Sep 1997 LISS-3, WIFS 736 / 825 101MOS-1 Feb 1987 MESSR, VTIR 908 99.1 17MOS-1b Feb 1990 MESSR, VTIR 908 99.1 17RESURS-01 1985 MSU-SK 678 98 21Orb View-2 SeaWiFS 705 np 98.9 1GMS VISSR 35900 0 gsGOES GOES Imager 35900 0 gsSpace Shuttle 1983 MOMS-01Space Shuttle 1993 MOMS-02Mir Space Station 1996 MOMS-02PERS-1 July 1991 ATSRERS-2 Apr. 1995 ATSR-2ENVISAT-1 1999 AATSREOS-AM-1 1999 MODISIkonos Sept 1999 681 98.1 98


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Table 3.11: Characteristics of multispectral satellite sensors(Resolutions and swath)

SENSOR No. ofbandsIFOV(m)

swath(km)

repeatcycle(days)

dynamicrange(bits)

MSS 4 79 185 18 / 16 7 / 6RBV 3 79 185 18TM 7 30 185 16 8ETM+ 7 30 185 16 8HRV 3 20 60 26 8HRVIR 4 20 60 26 8VGT 4 1000 2250 26 8ATSR 4ATSR-2 7 1000 500AATSR 7 500

1000500

AVHRR 5 1100 2394 10AVNIR 4

pan168

80 41 87

OCTS 12 700 1400 41 10CZCS 6 825 1566 6 8VISSR 2 1250

500068

GOESImager

5 100040008000

10

MESSR 4 50 100 17 8VTIR 4 (900)

27001500 17 8

LISS-1 4 73 146 22 7LISS-2 4 36 146 22 7LISS-3 4

pan2310

142-14670

24 7

WIFS 2 188 774 24 7MSU-SK 5 160 600 21SeaWiFS 8 1100 2800 1 10Ikonos 4 11 11


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Table 3.12: Spectral characteristics of satellite multispectral sensors.

MSS RBV TM ETM+ HRV HRVIR VGT ATSRBlue 0.475 − 0.575 0.45 − 0.52 0.45 − 0.515 0.43 − 0.47Green 0.5 − 0.6 0.52 − 0.60 0.525 − 0.605 0.50 − 0.59 0.50 − 0.59Red 0.6 − 0.7 0.580 − 0.680 0.63 − 0.69 0.63 − 0.690 0.61 − 0.68 0.61 − 0.68 0.61 − 0.68

Near IR 0.7 − 0.80.8 − 1.1

0.689 − 0.830 0.76 − 0.90 0.75 − 0.90 0.79 − 0.89 0.79 − 0.89 0.78 − 0.89

Mid IR 1.55 − 1.752.08 − 2.35

1.55 − 1.752.09 − 2.35

1.58 − 1.75 1.58 − 1.75 1.6

ThermalIR

10.4 − 12.6(Landsat

3)10.4 − 12.5 10.40 − 12.5

3.710.812

Pan 0.505 − 0.750(Landsat 3)

0.52 − 0.90 0.51 − 0.73 0.61 − 0.68

ATSR-2 AATSR AVHRR AVNIR OCTS CZCS VISSR GOES Im.Blue 0.42 − 0.50 0.490 ± 0.01 0.433 − 0.453

Green 0.555 0.55 0.58 −0.52 − 0.60 0.520 ± 0.01

0.565 ± 0.010.510 − 0.5300.540 − 0.560 0.55 − 0.55 −

Red 0.659 0.67 − 0.68 0.61 − 0.69 0.670 ± 0.01 0.660 − 0.680

Near IR 0.865 0.86 0.725 − 1.10 0.76 − 0.89 0.765 ± 0.020.865 ± 0.02

0.700 − 0.800 − 0.75 − 0.75

Mid IR 1.6 1.6

ThermalIR

3.710.812

3.71112

3.55 − 3.9310.3 − 11.311.4 − 12.4

3.55 − 3.888.25 − 8.8010.3 − 11.410.4 − 12.7

10.5 − 12.5 10.5 − 12.53.80 − 4.006.50 − 7.0010.2 − 11.210.5 − 12.5

Pan 0.52 − 0.72

MESSR VTIR LISS-1,-2 LISS-3 WIFS MSU-SK SeaWiFS MODIS

Blue0.45 −

0.62 − 0.680.402 − 0.4220.433 − 0.4530.480 − 0.500

36bands:

0.4 –

Green 0.51 − 0.59 0.5 − − 0.520.52 − 0.59 0.52 − 0.590.5 − 0.6 0.500 − 0.520

0.545 − 0.565Red 0.61 − 0.69 − 0.7 0.62 − 0.68 0.62 − 0.68 0.6 − 0.7 0.660 − 0.680

Near IR 0.73 − 0.800.80 − 1.10

0.77 − 0.86 0.77 − 0.86 0.77 − 0.86 0.7 − 0.80.8 − 1.1

0.745 − 0.7850.845 − 0.885

Mid IR

ThermalIR

6.0 − 7.010.5 − 11.511.5 − 12.5

1.55 − 1.70 10.4 −12.6 – 14.5

Pan 0.50 − 0.75


23

3.3. Airborne sensors

Although remote sensing from sensors aboard airplanes has been usedlong before the era of satellite missions, today their use is relatively lim-ited. Satellite sensors are typically flown on airplanes for testing beforebeen placed on satellites. The cost of airborne remote sensing is its maindisadvantage, because the data produced are of use to a limited numberof users, unlike the satellite data which are addressed to a worldwide anddiverse user community. Another disadvantage is the large image distor-tions resulting from the instability of the sensor platform during flight, aswell as to the very large field of view, which produces larger pixels awayfrom the nadir direction. The large FOV is necessary to achieve a reason-able swath from the relatively small flight height. The spatial resolutiondirectly depends on the fixed IFOV and the variable flight height.Smaller heights give smaller pixel values (higher spectral resolution) butat the same time smaller swath and therefore longer flight missions tocover the same land area, i.e. higher cost. Pixel size at nadir is approxi-mately the product of IFOV and height. Expressing IFOV in mrad andheight in kilometers, their product gives pixel size in meters. For exam-ple IFOV = 2.5 mrad corresponds to a 2.5×2.5 m pixel with a height of 1km. For the same IFOV a satellite-like resolution of 25×25 m corre-sponds to a height of 10 km. The advantage of airborne missions is thepossibility to produce data, which fit very well the particular needs of aspecific user, especially with sensors where the available band can bepreset to cover particular spectral regions. In addition they provide theonly means to record with high temporal resolution sudden events, suchas volcanic eruptions or spread of oil spills. Therefore they play an im-portant role in hazard assessment through remote sensing.The characteristics of three airborne sensors are presented in table 3.13:the Daedalus 1240/1260 Multispectral Line Scanner, the Airborne The-matic Mapper (ATM) with bands similar to those of the Landsat TM andthe Thermal Infrared Multispectral Scanner (TIMS).A number of airborne sensors with a large number of bands are designedto provide hyperspectral data; they will be examined in the next para-graph.Airborne sensors are also the radar sensors of active remote sensing,which are not discussed in this book.


24

Table 3.13: Characteristics of three airborne sensors.

Airborne Sensors

Daedalus ATMS TIMS

bands 0 0.32 – 0.381 0.38 – 0.422 0.42 – 0.453 0.45 – 0.504 0.50 – 0.555 0.55 – 0.606 0.60 – 0.657 0.65 – 0.698 0.70 – 0.799 0.80 – 0.89

10 0.92 – 1.1011 3.0 – 5.012 8.0 –14.0

1 0.42 – 0.452 0.45 – 0.523 0.52 – 0.604 0.605 – 0.6255 0.63 – 0.696 0.695 – 0.757 0.76 – 0.908 0.91 – 1.059 1.55 – 1.75

10 2.08 – 2.3511 8.5 – 13.0

1 8.2 – 8.62 8.6 – 9.03 9.0 – 9.44 9.5 – 10.25 10.2 – 11.26 11.2 – 12.2

FOV (degrees) 86 86 76IFOV (mrad) 2.5 2.5 2.5dyn. range (bits) 8 8 8

3.4. Hyperspectral sensors

Imaging spectrometers with many more bands than the multispectral sen-sors are characterized as hyperspectral. The high spectral resolution al-lows a detailed recording of the form of the spectral signature of eachimaged pixel, though with some distortion due to the atmospheric effectsof radiation absorption and scattering. The main difference in this case isnot in the sensor technology but rather in the data processing. The prob-lem of classification of the pixels in different classes of land cover needsa fundamentally different approach, where classification is rather re-placed by identification of the pixel class by means of comparison withan available library of spectral firms. This problem is discussed more inchapter 12.Most hyperspectral sensors are airborne. The characteristics of several ofthem are shortly presented in table 3.14.. These are the Geophysical En-vironmental Research Imaging Spectrometer (GERIS) with 63 bands de-veloped by the Geophysical Environmental Research Corporation, theCompact Airborne Spectrographic Imager (CASI) with 288 bands fromITRES Research - Canada, the Airborne Visible and Infrared ImagingSpectrometer (AVIRIS) with 224 bands from NASA’s Jet PropulsionLaboratories, MIVIS with 102 bands from Daedalus Enterprises, theChinese MAIS with 71 bands, the Hyperspectral Digital Image Collec-tion Experiment (HYDICE) with 206 bands from US Naval ResearchLaboratories and HYMAP with 128 bands from Integrated Spectronics.A satellite hyperspectral sensor is the Moderate Resolution ImagingSpectrometer (MODIS) with 36 bands, aboard the EOS (Earth Observing


25

System) satellites. It has variable spatial resolution with pixel size of 250m, 500 m and 1 km.

Table 3.14: Characteristics of hyperspectral sensors(imaging spectrometers).

spectralregion

No. ofbands

spectralresolution

(nm)

IFOV(mrad)

pixelsperline

dynamicrange(bits)

GERIS 0.40 – 1.081.0 – 2.02.0 – 2.5

247

32-------63

25.412016.5

2.5, 3.3, 4.6 512 or1024

16

CASI 0.4 – 0.9 288 1.8 1.02 – 1.53 512 12AVIRIS 0.4 – 0.72

0.69 – 1.301.25 – 1.871.84 – 2.45

31636363-------

224

9.79.68.8

11.6

1 550 12

MIVIS 0.433 – 0.8331.15 – 1.552.00 – 2.50

8.20 – 12.70

208

6410-------

102

2050

≤500

2 765 12

MAIS 0.45 – 1.11.40 – 2.508.2 – 12.2

32327

71

2030

400-800

varies 8

HYDICE 0.4 – 2.5 206 7.6-14.9 0.5 320 12HYMAP 0.44 – 0.88

0.881 – 1.3351.4 – 1.81

1.95 – 2.94

total128

16131216

2.5×2.0 512 12

MODIS 0.4 – 14.5 36

3.1. Digital image storage and display3.2. Satellites sensors3.3. Airborne sensors3.4. Hyperspectral sensors

chapter 3 remote sensing satellites, sensors and datader.topo.auth.gr/rs/tel_eng_03_satellites and...

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