Fundamentals of Remote Sensing: Digital Image Analysis.

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Fundamentals of Remote Sensing: Digital Image Analysis Slide 2 Center for Remote Sensing and Spatial Analysis, Rutgers University Advanced Remote Sensing: Introduction to Digital Image Analysis Lecture 1 Prepared by R. Lathrop 10/99 updated 9/03 Readings: ERDAS Field Guide 5th ed ERDAS CH. 1: 1-15; 3: 52-77 Slide 3 Center for Remote Sensing and Spatial Analysis, Rutgers University Lecture Notes 1: Overview of Remote Sensing A number of these slides were originally produced by Scott Madry and Chuck Colvard with some subsequent modification by Rick Lathrop. Additional slides were produced by Rick Lathrop. Slide 4 Center for Remote Sensing and Spatial Analysis, Rutgers University Remote Sensing Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation.-Lillesand & Kiefer (1987) Slide 5 Center for Remote Sensing and Spatial Analysis, Rutgers University Radiation source/target/sensor Electromagnetic energy interacts with physical matter in different ways in different parts of the spectrum. Some energy is scattered, absorbed, etc. Slide 6 Center for Remote Sensing and Spatial Analysis, Rutgers University Digitization of analog aerial photography, can be very useful for historical studies and/or for high spatial resolution needs Direct acquisition using some form of digital imaging sensor Computerized image analysis can help to enhance and extract information content of imagery in a time-efficient, cost-effective manner Computers still can not replace the human image analyst Digital Image Acquisition & Analysis Slide 7 Aerial Cameras A large format oblique camera Keystones Wild RC- 10 mapping camera Slide 8 Center for Remote Sensing and Spatial Analysis, Rutgers University Aerial photos Black & White - single panchromatic layer Color: 3 layers B-G-R Color IR: 3 layers G-R- NIR Slide 9 Center for Remote Sensing and Spatial Analysis, Rutgers University Aerial photos The traditional form of remote sensing Pro: Can be easily customized to meet specific requirements Con: Can be expensive Need access to plane Time consuming interpretation Repeat coverage often infrequent Different sun angles Slide 10 Center for Remote Sensing and Spatial Analysis, Rutgers University Space-borne Remote Sensing Emerging Technology Pro: GIS ready faster turn around acquisition time of 5 minutes gives equal solar illumination, shadows, no clouds easy to repeat for change detection Con: Significant investment in computer hardware/software Less flexibility in acquisition Slide 11 Center for Remote Sensing and Spatial Analysis, Rutgers University Passive electro-optical systems Electronic sensors can acquire data outside the visible spectrum Elements sensitive to electro magnetic energy (EME) of certain wavelengths focus energy onto a sensor plane. A prism is used to divide the energy into specific wavelengths. The CCDs are stimulated and produce an electrical signal equal to the energy focused upon it. These data are recorded. Data are processed and displayed on computers-images are composed of pixels, whose brightness relates to the strength of the radiation received from an area on the surface. Digital processing of the data produces useful information Slide 12 Center for Remote Sensing and Spatial Analysis, Rutgers University Design of A Remote Sensing Effort Clear definition of the problem Evaluation of the potential of remote sensing techniques Identification of appropriate remote sensing data acquisition procedures Determination of the data interpretation procedures Identification of the criteria by which the quality of information can be evaluated Slide 13 Center for Remote Sensing and Spatial Analysis, Rutgers University Resolution Four kinds of resolution determined by user needs: Spatial Resolution: How small an object do you need to see (pixel size) and how large an area do you need to cover (swath width) Spectral Resolution: What part of the spectrum do you want to measure Radiometric Resolution: How finely do you need to quantify the data Temporal Resolution: How often do you need to look Slide 14 Center for Remote Sensing and Spatial Analysis, Rutgers University Spatial resolution Instantaneous Field of View (IFOV) determines the dimension, D, of the Ground Resolution Cell (GRC) imaged on the ground IFOV Slide 15 Center for Remote Sensing and Spatial Analysis, Rutgers University Spatial resolution keeps getting better... Slide 16 Center for Remote Sensing and Spatial Analysis, Rutgers University Spatial resolution Slide 17 Center for Remote Sensing and Spatial Analysis, Rutgers University 1, 3, and 10 meters Slide 18 Center for Remote Sensing and Spatial Analysis, Rutgers University ultra-high spatial resolution 24 inch (60 cm) 6 inches (15 cm) Slide 19 Center for Remote Sensing and Spatial Analysis, Rutgers University Swath width Landsat-185km (100 mi) 80 m = 40 Mb-4 bands (MSS) 30 m = 320 Mb-6 bands (TM) 10 m = 342.25 Mb-1band 5 m = 1.369 Gb -1 band 1 m = 34.225 Tb - 1 band How small do we need? How much data can we store and process? 185 by 185 km Slide 20 Center for Remote Sensing and Spatial Analysis, Rutgers University Spectral Resolution: slicing up the electromagnetic spectrum Slide 21 Center for Remote Sensing and Spatial Analysis, Rutgers University The electromagnetic spectrum Comparative Sizes: from subatomic to human scales Atom Nucleus Atom Molecule Bacteria Pinhead Honeybee Human & larger From NY Times graphic 4/8/2003 Slide 22 Center for Remote Sensing and Spatial Analysis, Rutgers University Spectral wavebands of Landsat TM Slide 23 Center for Remote Sensing and Spatial Analysis, Rutgers University Landsat TM-7 bands-8 bit data Spectral (where we look) Radiometric (how finely can we measure the return) 0-63, 0-255, 0-1023 Landsat TM BAND 1 2 3 4 5 7 6 Slide 24 Center for Remote Sensing and Spatial Analysis, Rutgers University Landsat TM: each waveband provides different information about earth surface features Slide 25 Center for Remote Sensing and Spatial Analysis, Rutgers University Radiometric resolution Dark Bright Determined by the A-to-D quantization 6 bit = 0-63, 8 bit = 0-255, 10 bit = 0-1023 Sensitivity of the detector to differences in EMR signal strength determines the smallest difference in brightness value that can be distinguished Slide 26 Center for Remote Sensing and Spatial Analysis, Rutgers University Radiometric resolution Higher radiometric resolution is especially important for quantitative applications such as sea- surface temperature mapping where the user wants to distinguish small differences in temperature Slide 27 Center for Remote Sensing and Spatial Analysis, Rutgers University Satellite remote sensing orbits give repeat coverage Geostationary Polar Sun-synchronous Constant view of hemisphere Covers entire Earth 35,800 km 700-900 km Slide 28 Center for Remote Sensing and Spatial Analysis, Rutgers University SPOT has steerable mirror to increase overpass frequency Slide 29 Center for Remote Sensing and Spatial Analysis, Rutgers University Change Detection The ability to monitor change is one of the benefits of remote sensing We can monitor human and natural changes in the landscape Slide 30 Center for Remote Sensing and Spatial Analysis, Rutgers University Hurricane Andrew takes on Florida Slide 31 Center for Remote Sensing and Spatial Analysis, Rutgers University Spatial and temporal resolution Ground resolution Repeat timeRepeat time.1 m 1 m 5 m 10-20m 30m 1 km 5 km 5 years 3 years 28 days 17 days 12 hours 30 min. 50 years Ground Surveys Aerial Photography Space photography SPOT Landsat-TM NOAA AVHRR Meteosats As spatial resolution increases, the revisit time is also increased, as are the applications that are appropriate and the cost Slide 32 Center for Remote Sensing and Spatial Analysis, Rutgers University Many different systems. Which to choose? Slide 33 Center for Remote Sensing and Spatial Analysis, Rutgers University Different sensors and resolutions sensor spatial spectral radiometric temporal ---------------------------------------------------------------------------------------------------------------- AVHRR 1.1 and 4 KM 4 or 5 bands 10 bit 12 hours 2400 Km.58-.68,.725-1.1, 3.55-3.93 (0-1023) (1 day, 1 night) 10.3-11.3, 11.5-12.5 (micrometers) Landsat MSS 80 meters 4 bands 6 bit 16 days 185 Km.5-.6,.6-.7,.7-.8,.8-1.1 (0-63) Landsat TM 30 meters 7 bands 8 bit 14 days 185 Km.45-.52,.52-.6,.63-.69, (0-255).76-.9, 1.55-1.75, 10.4-12.5, 2.08-2.3 um SPOT 10m P / 20m X P -1 band X- 3 bands 8 bit 26 days 60 Km P -.51-.73 um (0-255) (2 out of 5) X -.5-.59,.61-.68,.79-.89 um IRS15.8 meters1 band6 bit 22 days 70 km.5-.75(0-63) IKONOS1 meter1 band8 bit 3 days 11 km.45-.9 (0-255) (1.5 out of 3) Slide 34 Center for Remote Sensing and Spatial Analysis, Rutgers University Remote sensing : acquiring and analyzing data using distant devices recording electromagnetic energy Digital scanning devices or analog photography Airborne, terrestrial, marine, or orbiting platforms Can be passive, sensing existing radiation, or active, sensing radiation bounced off the surface, as with radar. Remote sensing usually involves digital data, but also photography Usually means both the capture and computerized image analysis of data. Quantitative, easily incorporated into GIS Design of a remote sensing effort must clearly define information needs and consider the 4 types of remote sensing resolution: radiometric, spatial, spectral, temporal when considering the types of imagery to use Remote Sensing - Summary