RS_EE&ASE_3.pdf

REMOTE SENSING PART-3 (IMAGE AND RESOLUTION) Nature of the image: ❖ Pixel - picture element having both spatial and spectral properties. ❖ Spatial property - defines the "on ground" height and width. ❖ Spectral property - defines the intensity of spectral response for a...

REMOTE SENSING PART-3 (IMAGE AND RESOLUTION) Nature of the image: ❖ Pixel - picture element having both spatial and spectral properties. ❖ Spatial property - defines the "on ground" height and width. ❖ Spectral property - defines the intensity of spectral response for a cell in a particular band  Example of aof picture Composition a Pixel How will this pixel appear in the image? RS_Vis_8 Land Cover %age of pixel Reflectance Building 15 90 Sand 15 100 Trees 50 50 Water 20 10  Composition of a Pixel 55.5 RS_Vis_10 Nature of the Image:  Image Resolution ❖ Spatial Resolution -- what size we can resolve ❖ Spectral Resolution -- what wavelengths do we use ❖ Radiometric Resolution -- degree of detail observed ❖ Temporal Resolution -- how often do we observe  Spatial Resolution The fineness of detail visible in an image. – (coarse) Low resolution – smallest features not discernable – (fine) High resolution – small objects are discernable Factors affecting spatial resolution – Atmosphere, haze, smoke, low light, particles, blurred sensor systems, pixel size and Instantaneous field of view.  Instantaneous Field of View It is defined as the angle subtended by a single detector element on the axis of the optical system. IFOV has the following attributes: Solid angle through which a detector is sensitive to radiation. The IFOV and the distance from the target determines the spatial resolution. A low altitude imaging instrument will have a higher spatial resolution than a higher altitude instrument with the same IFOV.  Instantaneous Field of View Angular cone of visibility of the sensor Depends upon – Altitude of sensor – Viewing angle of the sensor  Instantaneous Field of View Angular cone of visibility of the sensor Depends upon – Altitude of sensor – Viewing angle of the sensor Focal length and scale Shorter focal lengths have wider field of views, while longer focal lengths have smaller field of views. Therefore sensors with a longer focal length will produce an image with a smaller footprint compared to that of a shorter focal length.  Spatial Resolution Target and background characteristics – Contrast and Shadows Image Scale – the distance on an image to the corresponding distance on the ground Large scale –objects seen better (1:50,000) Small scale –objects not clear (1:250,000) Image Scale A sensor with a 152 mm focal length takes an aerial photograph from an altitude of 2780m. What is the scale of the photograph? Elevation of ground = 500MSL. Q. The scale of an aerial photograph is 1:15,000. In the photo you measure the length of a bridge to be 0.25 inches, what is the length of the bridge in feet in real life?  Spatial Resolution A photographic image has a true scale – 1:500 – engineering & surveying – 1:12,000 – resource management – 1:50,000 – large-area assessments A photograph on film cannot be resampled for higher resolution. The image captured cannot be physically manipulated.  Spatial Resolution A digital image does not have a fixed scale. It’s the imaging instrument that has a fixed scale or Ground Sample Distance (GSD) in the original digital image – GSD is the distance between two consecutive pixel centers measured on the ground. The bigger the value of the image GSD, the lower the spatial resolution of the image and the less visible details. The GSD is related to the flight height: the higher the altitude of the flight, the bigger the GSD value. – Sometimes resampled where the pixels are modified to suit or change the image size  Spatial Resolution Interpretability for assessing spatial resolution – Detectability – the ability to record the presence or absence of an object, although the identity of the object may be unknown. An object may be detected even though it is smaller than the resolving power of the imaging system – Recognizability – the ability to identify an object from the image. Objects can be detected and resolved an yet not be recognizable. Example – roads, railroads, canals could all look linear and have been detected but which are they? – Identification – the ability to distinguish category features such as cars from trucks or species of trees.  Spatial Resolution  Spectral Resolution 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 to. The narrower the bands, the greater the spectral resolution.  Spectral Resolution  Temporal Resolution Remote Sensor Data Acquisition June 1, 2020 June 17, 2020 July 3, 2020 16 days  Temporal Resolution Depends on: The orbital parameters of the satellite Latitude of the target Swath width of the sensor Pointing ability of the sensor  Radiometric Resolution 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.  Radiometric Resolution 7-bit 0 (0 - 127) 8-bit 0 (0 - 255) 0 9-bit (0 - 511) 10-bit 0 (0 - 1023) Radiometric Resolution 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?  Radiometric Resolution The number of digital values possible in an image is equal to the number two (2 - for binary codings 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. 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.  Signal Strength Depends on Energy flux from the surface Altitude of the sensor Spectral bandwidth of the detector IFOV Dwell time  Signal to noise ratio Signal to noise ratio is defined as the ratio between the power of the signal and the background noise. Higher the SNR, the easier it is to differentiate between signal and noise Fine spatial resolution → small IFOV → less energy Difficult to detect fine energy differences → Poor radiometric resolution Poor spectral resolution Narrow spectral bands →High spectral resolution → Less energy Difficult to detect fine energy differences → Poor radiometric resolution Poor spatial resolution Wide spectral band → Poor spectral resolution→ more reflected energy Good spatial resolution Good radiometric resolution These three types of resolutions must be balanced against the desired capabilities and objectives of the sensor Images Monochromatic Images Panchromatic Images Multispectral Images ❖ Multispectral sensors detect light reflectance in more than one or two bands of the EM spectrum. ❖ These bands represent different data - when combined into the red, green, blue of a color monitor, they form different colors Nature of the Image: ❖ Multispectral image is composed of 'n' rows and 'n' columns of pixels in each of three or more spectral bands  Multispectral Images The images received from each of the spectral bands of a multispectral sensors can be viewed independently as greyscale images. Or they can also be combined to form one multispectral image called color composite images. They are of three kinds: »True Color composites »False color composites »Natural color composites  True Color Composites True color composite images are composed of three primary colors i.e., red, blue and green. If a multispectral sensor can detect the three visual color bands, then the three bands can be combined to give a true color composite.  False color composite The display color assignment can also be chosen arbitrarily when the multispectral sensor does not sense in the primary visual color band or in the visible range of the electromagnetic spectrum for that matter. Though the colors can be chosen arbitrarily, some sets of colors are used more because they help in distinguishing certain ground features.  Natural Composite Image When a multispectral scanner does not sense one or more of the primary colors, the spectral bands that it can sense can be combined to generate a image that closely resembles the visual color photograph.  Natural Composite Image Example: The SPOT HRV multispectral scanner does not have a blue band. The three bands it can sense are XS1, XS2 and XS3 which correspond to green, red and NIR bands. Reasonably good natural composite image can be obtained by combining the three spectral bands. R = XS2 G = (3 XS1 + XS3)/4 B = (3 XS1 - XS3)/4  Hyperspectral Sensors Acquire images in several, narrow, contiguous spectral bands in the visible, NIR, MIR, and thermal infrared regions of the EMR spectrum o Typically more than 100 bands are recorded o Enables the construction of a continuous reflectance spectrum for each pixel – Hyperspectral sensors are also known as imaging spectrometers – Hyperspectral scanners may be along-track or across-track Example: Hyperion sensor : 220 bands (from 400 -2.5 μm) AVIRIS sensor : 224 individual CCD detectors each with 10nm spectral resolution Hyperspectral Image Interpretation Spectral curves of the pixels are compared with the existing spectral library to identify the targets All pixels whose spectra match the target spectrum to a specified level of confidence are marked as potential targets Depending on whether the pixel is a pure feature class or the composition of more than one feature class, the resulting plot will be either a definitive curve of a "pure" feature or a composite curve containing contributions from the several features present  GEOLOGICAL APPLICATIONS ❖ Surficial deposit / bedrock mapping ❖ Lithological mapping ❖ Structural mapping ❖ Sand and gravel (aggregate) exploration/ exploitation ❖ Mineral exploration ❖ Hydrocarbon exploration ❖ Environmental geology ❖ Sedimentation mapping and monitoring ❖ Geo-hazard mapping STRUCTURAL MAPPING  HYDROLOGICAL APPLICATIONS ❖ Wetlands mapping and monitoring ❖ Soil moisture estimation ❖ Snow pack monitoring ❖ Measuring snow thickness ❖ River and lake ice monitoring ❖ Flood mapping and monitoring ❖ Glacier dynamics monitoring ❖ Drainage basin mapping and watershed modeling ❖ Irrigation mapping ❖ Groundwater exploration FLOODS AND DISASTER RESPONSE  DROUGHT MONITORING JULY 2001 JULY 2002 LANDSAT THEMATIC MAPPER (SOURCE: CCRS 2002)  LAND-USE LAND-COVER APPLICATIONS ❖ Natural resource management ❖ Wildlife habitat protection ❖ Urban expansion / encroachment ❖ Damage delineation (tornadoes, flooding, volcanic, seismic, fire) ❖ Legal boundaries for tax and property evaluation ❖ Target detection - identification of landing strips, roads, clearings, bridges, land/water interface Land Cover Classification  OCEANOGRAPHIC APPLICATIONS ❖Ocean pattern identification ❖Storm forecasting ❖Fish stock and marine mammal assessment ❖Water temperature monitoring ❖Water quality ❖Ocean productivity, phytoplankton concentration and drift ❖Mapping and predicting oilspill extent and drift ❖Strategic support for oil spill emergency response decisions ❖Shipping navigation routing ❖Mapping shoreline features / beach dynamics ❖Coastal vegetation mapping