Research Collection
Educational Material
Short introduction to remote sensing and a comparison between airborne and spaceborne sensors
Author(s):
Baltsavias, Emmanuel P.
Publication Date:
2003
Permanent Link:
https://doi.org/10.3929/ethz-a-004658748
Rights / License:
In Copyright - Non-Commercial Use Permitted
This page was generated automatically upon download from the ETH Zurich Research Collection. For more information please consult the Terms of use.
ETH Library
1. Remote Sensing definition
"Feeling without touching"
CCRS
"Remote sensing is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information."
Here, only satellite-based RS, Earth Observing, mainly for land (other application areas: atmosphere, oceans, snow/ice)
2. Satellites
- Civilian from 1972
- Dozens launched and many of them still operating - Increasing number planned
- Responsible: Int'l agencies (ESA), governmental agencies (NASA, JAXA, CCRS, ISRO, DLR, INPE etc.), private firms (SPOT Image, Space Imaging, Digital Globe etc.)
- Orbit: usually sun-synchronous, near-polar, near-circular, 450 - 850 km altitude ; some geostationary/geosynchronous (e.g. meteorological satellites)
3. Satellite platforms
- mainly unmanned satellites (large, ì normalî , mini, micro, nano, pico satellites) - less, Space Shuttle and space stations (e.g. MIR)
- each platform can usually carry many instruments
4. Sensors (here, only imaging sensors) ; Classification possible according to different criteria:
- Mostly digital, few film (Russian) - Electromagnetic spectrum covered
- Visible (e.g. 0.4 - 0.7 microns), B (Blue): 0.45, G (Green): 0.55, R (Red): 0.65 - Infrared (NIR, SWIR, MIR, TIR)
- Microwaves (radar, SAR ...Synthetic Aperture Radar)
- Optical spectral bands selected in atmospheric windows and based on application (reflectance of relevant objects)
- Panchromatic (PAN): B/W in visible range ; Multispectral (MS): usually in RGB and NIR ;
Hyperspectral: > 10-15 bands, narrow bandwidth (few nm) ; Pan-Sharpened (fusion of PAN & MS) - Optical sensors ["normal" (CCD- and film-based cameras), less Laser (Lidar)], Microwave
- Most digital electro-optical sensors use linear CCDs (one or more), few use detectors - Active (Laser, Radar), passive
- Radar
- Complicated geometry and processing, difficult to interpret visually
- No illumination needed (can operate at night), can penetrate clouds, largely weather independent, can partly penetrate earth surface and tree canopies (for long wavelengths)
5. Resolution
- spatial: many definitions, often ground pixel size (pixel footprint, Ground Sampling Distance (GSD))
NOTE: ground pixel size is different than image quality and geometric accuracy!
- radiometric (number of bits per pixel): often 6-12 bit, usually 8
- spectral: number of bands and band characteristics (band center, band width, spectral curve of each band)
- temporal: repeat and revisit period (revisit < repeat, for high latitudes and with steerable sensors and agile satellites)
In reality multiple observations of same area much less frequent due to atmospheric and weather conditions, other suboptimal imaging conditions (long shadows), other imaging priorities etc.
6. Important satellites/sensors
- Landsat 1-5, 7 (USA) / MSS, TM, ETM+
- SPOT 1-5 (France) / HRV, HRVIR, HRG and HRS, Vegetation 1 and 2 - IRS 1C, 1D (India) / PAN, LISS, WiFS
- High-resolution ( < 3 m) : Ikonos 2 (Space Imaging), Quickbird (Digital Globe),
Orbview-3 (Orbimage), Spot 5 PAN (in supermode), EROS 1A (Imagesat Int'l), Russian film - Radar: ERS 1-2 (ESA)/SAR, Envisat (ESA) / ASAR, Radarsat 1 (Canada), Space Shuttle SRTM Mission (USA, Germany, Italy)
- Specific (meteorology), e.g. Meteosat, MSG, NOAA AVHRR
- Important new multisensor satellites (mostly experimental): Envisat (ESA), Terra (NASA) - Laser and hyperspectral sensors: relatively new, little done, mostly experimental
7. Examples of data
- See provided links on WEB (http://www.photogrammetry.ethz.ch/~manos/satfe/satfe.html) under "Data", "Satellites/Sensors", "Some companies"
- Important links under "Data":
- NPOC (National Point of Contact, CH) - Earth Observing System data gateway - Eurimage
- Visible earth
- Space Imaging (register gratis for Carterra -> online data browser) - SPOT Image
- ESA online catalogue
8. Products from satellite imagery
- Selection of sensor(s): based on experience, literature review, availability, price etc.
Other selection criteria: place, time (maybe difficult if multitemporal analysis required) etc.
- Sales: via NPOC, from int'l agencies (ESA), gov. Agencies (USGS, NASA etc.), private companies (Space Imaging, SPOT Image, Eurimage etc.)
- Prices: vary a lot even for same sensor: reduction for scientists/universities, reductions if many data, cheaper if in archive, cheaper if older, costly if satellite programming for new image acquisition, costly if license for more than 1 site, price dependence on size of image, processing level, distributor, cloud requirements, time delivery etc.
9. Product delivery (incl. different options for ordering)
- cartographic projection/ellipsoid for rectified images and orthoimages - data format, e.g. GeoTIFF, TIFF, NITF, HDF, proprietary etc.
- number of bits, e.g. 8 or 11(16) for Ikonos - storage media
- film or digital (rare case, usually digital)
- flexibility in area definition (via polygon, e.g. Ikonos) or predefined sizes (e.g. Landsat) - variable number of channels that can be ordered, sometimes pan-sharpened, e.g. Ikonos - clear tendency for value added products [DEMs (only from stereo-capable satellites),
orthoimages, land use/cover, orthoimage maps, mapping (general or of specific objects like streets) etc.], espec. for direct use in GIS
- processing level: radiometric correction, geometric correction at various levels (only
correction of systematic errors (e.g. earth's rotation and curvature, panoramic distortion, variations of altitude and attitude) without rectification, rectification on ellipsoid (often UTM and WGS84; with or without GCPs, usually without DEM except e.g. SPOT 5), orthoimages
[with DEM of variable accuracy, usually with GCPs or without (IKONOS)]
10. Data processing
- Manual, semi-automated (usual case), automated - Important processing steps
- preprocessing (improvement (radiometric, geometric) for subsequent main processing)
- sensor calibration (geometric, radiometric), sensor and orbit modeling (use of GPS/INS/stellar cameras), sensor orientation/triangulation and bundle adjustment (as for aerial images)
- georeferencing (relations between image and ground coordinate systems, solved by sensor orientation above)
- geocoding (image transformation to a specific ground coordinate system)
- on ellipsoid at a certain height (rectification, image-to-map registration), usually with help of GCPs
- orthoimage generation (orthorectification), using a DTM and usually also GCPs
- extraction of geometric information: mapping (line maps or orthoimage maps, mapping of general or specific objects); DSM/DTM generation; measurement of point coordinates,
distances, areas, volumes
- extraction of thematic (semantic) information: land cover / land use, thematic maps for specific objects (e.g. geology, crop types, forest types, water quality types)
- important image analysis operation: classification
Done based on use of: spectral info, geometry (size, shape, 3D info), texture, context, shadows, multitemporal changes, motion etc. ; Tendency to go from pixel-based to object-based classification
- multitemporal applications (changes over time of geometry and/or semantics; important for agriculture, landuse / landcover, monitoring and environmental applications, disaster and hazard mapping etc.)
Remarks:
- Usually extraction of geometric and thematic info is simultaneous
- Processing, especially thematic, needs ground truth (field data) for sensor calibration, establishment of relations between sensor data and information needed (espec. because
information needed is only indirectly determined), product validation/editing and correction - Often integration/combination/fusion of data from multiple sensors or information sources
(for complementarity, redundancy)
- Processing and especially analysis of data often within a GIS (with raster data capabilities), for integrated processing/analysis with other data (raster, vector, attribute)
11. Processing systems - RS systems,
e.g. Erdas, PCI, ENVI, ER Mapper
- Usually with a lot of image processing and GIS capabilities, some weak in sensor modelling and geometric info extraction),
- GIS
- Often limited image processing capabilities (some exceptions, e.g. IDRISI), few have add-on third party modules, e.g. Erdas Image Analyst for ESRI
- Digital photogrammetric systems,
e.g. SOCET SET (BAE Systems), ImageStation (Z/I Imaging), LPS (Leica), VirtuoZo (Supresoft) - Support of only few sensors
- Weak in semi-automated/automated thematic information extraction and image processing - Digital Image Processing systems (are slowly disappearing)
- Specific systems, e.g. for visualisation
-> Convergence between the first 3 system types but differences will remain
12. Applications
General remarks:
- Many applications mentioned but most in research stage, few really operational, potential for more applications exist (see critical remarks of Ehlers presentation on WEB under "Gemischtes
Material")
- RS mostly technology driven, not user/application driven
- Problems related also to data availability and costs, proper area coverage, at proper time, delivery time, image quality, geometric resolution etc.
- Additional problems in CH: complex terrain, many clouds, high accuracy etc. requirements, many existing data/good mapping
- Most applications within GIS ("Integration of RS and GIS") Application examples:
- Many examples in tutorials on WEB (e.g. from CCRS, Applications of Imaging Radar) and at WEB pages of data providers and RS firms
- See also examples of large EU projects (partly operational, e.g. MARS for agriculture) - Meteorology (weather prediction, climatology, NWM): THE operational application - Mapping (espec. with orthoimage maps, espec. for developing countries)
- DSM/DTM generation (from optical data (SPOT, high-resolution sats, some Russian imagery), stereo SAR, interferometric SAR)
- Telecommunications (placement of antennas, signal propagation), see link to WEB site of ISTAR
- Agriculture (crop area estimation, crop monitoring and yield etc.)
- Geology/Soil (mapping, mineral exploration, soil moisture, soil salinity)
- Forest mapping (tree types, diseases, wood yield, biomass, tree cutting)
- Urban and rural planning (urban area change, land cover/ land use, transportation planning, infrastructure management)
- Ecosystem and natural resources (mapping, management) - Atmospheric applications (aerosol concentrations, ozon etc.) - Water (mapping, efficient use and management, water quality) - Coastal zones (mapping, management, protection)
- Snow/ice (glacier mapping, multitemporal changes, snow runoff) - Oceanography (waves, winds, temperature, water quality)
- Natural disasters (prediction and mitigation, monitoring, damage evaluation): floods, earthquakes, forest fires, storm damages, volcanoes, icebergs etc.
- Global changes (land cover, vegetation, soil, water, global warming, ozon hole etc.) - Military (spionage, site monitoring etc.)
13. Differences to aerial sensors, e.g. which deliver the "better" data for a given application?
see Table on WEB page under "Gemischtes Material"
