An image is a two-dimensional representation of objects in a real scene. Remote sensing images are representations of parts of the earth surface as seen from space. The images may be analog or digital. Aerial photographs are examples of analog images while satellite images acquired using electronic sensors are examples of digital images.
|A digital image is a two-dimensional array of pixels. Each pixel has an intensity value (represented by a digital number) and a location address (referenced by its row and column numbers).|
The intensity value represents the measured physical quantity such as the solar radiance in a given wavelength band reflected from the ground, emitted infrared radiation or backscattered radar intensity. This value is normally the average value for the whole ground area covered by the pixel.
The intensity of a pixel is digitised and recorded as a digital number. Due to the finite storage capacity, a digital number is stored with a finite number of bits (binary digits). The number of bits determine the radiometric resolution of the image. For example, an 8-bit digital number ranges from 0 to 255 (i.e. 28 - 1), while a 11-bit digital number ranges from 0 to 2047. The detected intensity value needs to be scaled and quantized to fit within this range of value. In a Radiometrically Calibrated image, the actual intensity value can be derived from the pixel digital number.
The address of a pixel is denoted by its row and column coordinates in the two-dimensional image. There is a one-to-one correspondence between the column-row address of a pixel and the geographical coordinates (e.g. Longitude, latitude) of the imaged location. In order to be useful, the exact geographical location of each pixel on the ground must be derivable from its row and column indices, given the imaging geometry and the satellite orbit parameters.
|"A Push-Broom" Scanner: This type of imaging system is commonly used in optical remote sensing satellites such as SPOT. The imaging system has a linear detector array (usually of the CCD type) consisting of a number of detector elements (6000 elements in SPOT HRV). Each detector element projects an "instantaneous field of view (IFOV)" on the ground. The signal recorded by a detector element is proportional to the total radiation collected within its IFOV. At any instant, a row of pixels are formed. As the detector array flies along its track, the row of pixels sweeps along to generate a two-dimensional image.|
Multilayer images can also be formed by combining images obtained from different sensors, and other subsidiary data. For example, a multilayer image may consist of three layers from a SPOT multispectral image, a layer of ERS synthetic aperture radar image, and perhaps a layer consisting of the digital elevation map of the area being studied.
|An illustration of a multilayer image consisting of five component layers.|
A multispectral IKONOS image consists of four bands: Blue, Green, Red and Near Infrared, while a landsat TM multispectral image consists of seven bands: blue, green, red, near-IR bands, two SWIR bands, and a thermal IR band.
Currently, hyperspectral imagery is not commercially available from satellites. There are experimental satellite-sensors that acquire hyperspectral imagery for scientific investigation (e.g. NASA's Hyperion sensor on-board the EO1 satellite, CHRIS sensor onboard ESA's PRABO satellite).
An illustration of a hyperspectral image cube. The hyperspectral image data usually consists of over a hundred contiguous spectral bands, forming a three-dimensional (two spatial dimensions and one spectral dimension) image cube. Each pixel is associated with a complete spectrum of of the imaged area. The high spectral resolution of hyperspectral images enables better identificaiton of the land covers.
A "High Resolution" image refers to one with a small resolution size. Fine details can be seen in a high resolution image. On the other hand, a "Low Resolution" image is one with a large resolution size, i.e. only coarse features can be observed in the image.
A low resolution MODIS scene with a wide coverage. This image was received by CRISP's ground station on 3 March 2001. The intrinsic resolution of the image was approximately 1 km, but the image shown here has been resampled to a resolution of about 4 km. The coverage is more than 1000 km from east to west. A large part of Indochina, Peninsular Malaysia, Singapore and Sumatra can be seen in the image.
(Click on the image to display part of it at a resolution of 1 km.)
A browse image of a high resolution SPOT scene. The multispectral SPOT scene has a resolution of 20 m and covers an area of 60 km by 60 km. The browse image has been resampled to 120 m pixel size, and hence the resolution has been reduced. This scene shows Singapore and part of the Johor State of Malaysia.
Part of a high resolution SPOT scene shown at the full resolution of 20 m. The image shown here covers an area of approximately 4.8 km by 3.6 km. At this resolution, roads, vegetation and blocks of buildings can be seen.
Part of a very high resolution image acquired by the IKONOS satellite. This true-colour image was obtained by merging a 4-m multispectral image with a 1-m panchromatic image of the same area acquired simultaneously. The effective resolution of the image is 1 m. At this resolution, individual trees, vehicles, details of buildings, shadows and roads can be seen. The image shown here covers an area of about 400 m by 400 m. A very high spatial resolution image usually has a smaller area of coverage. A full scene of an IKONOS image has a coverage area of about 10 km by 10 km.
|10 m resolution, 10 m pixel size||30 m resolution, 10 m pixel size||80 m resolution, 10 m pixel size|
The following images illustrate the effect of pixel size on the visual appearance of an area. The first image is a SPOT image of 10 m pixel size derived by merging a SPOT panchromatic image with a SPOT multispectral image. The subsequent images show the effects of digitizing the same area with larger pixel sizes.
|Pixel Size = 10 m
Image Width = 160 pixels, Height = 160 pixels
|Pixel Size = 20 m
Image Width = 80 pixels, Height = 80 pixels
|Pixel Size = 40 m
Image Width = 40 pixels, Height = 40 pixels
|Pixel Size = 80 m
Image Width = 20 pixels, Height = 20 pixels
The following images illustrate the effects of the number of quantization levels on the digital image. The first image is a SPOT panchromatic image quantized at 8 bits (i.e. 256 levels) per pixel. The subsequent images show the effects of degrading the radiometric resolution by using fewer quantization levels.
|8-bit quantization (256 levels)||6-bit quantization (64 levels)|
|4-bit quantization (16 levels)||3-bit quantization (8 levels)|
|2-bit quantization (4 levels)||1-bit quantization (2 levels)|
Digitization using a small number of quantization levels does not affect very much the visual quality of the image. Even 4-bit quantization (16 levels) seems acceptable in the examples shown. However, if the image is to be subjected to numerical analysis, the accuracy of analysis will be compromised if few quantization levels are used.
|Part of the running track in this IKONOS image is under cloud shadow. The IKONOS uses 11-bit digitization during image acquisition. The high radiometric resolution enables features under shadow to be recovered.|
|The features under cloud shadow are recovered by applying a simple contrast and brightness enhancement technique.|
In comparison, the panchromatic data has only one band. Thus, panchromatic systems are normally designed to give a higher spatial resolution than the multispectral system. For example, a SPOT panchromatic scene has the same coverage of about 60 x 60 km2 but the pixel size is 10 m, giving about 6000 x 6000 pixels and a total of about 36 million bytes per image. If a multispectral SPOT scene is digitized also at 10 m pixel size, the data volume will be 108 million bytes.
For very high spatial resolution imagery, such as the one acquired by the IKONOS satellite, the data volume is even more significant. For example, an IKONOS 4-band multispectral image at 4-m pixel size covering an area of 10 km by 10 km, digitized at 11 bits (stored at 16 bits), has a data volume of 4 x 2500 x 2500 x 2 bytes, or 50 million bytes per image. A 1-m resolution panchromatic image covering the same area would have a data volume of 200 million bytes per image.
The images taken by a remote sensing satellite is transmitted to Earth through telecommunication. The bandwidth of the telecommunication channel sets a limit to the data volume for a scene taken by the imaging system. Ideally, it is desirable to have a high spatial resolution image with many spectral bands covering a wide area. In reality, depending on the intended application, spatial resolution may have to be compromised to accommodate a larger number of spectral bands, or a wide area coverage. A small number of spectral bands or a smaller area of coverage may be accepted to allow high spatial resolution imaging.
Source : http://www.crisp.nus.edu.sg