I would like to understand if is there any difference or relation between spectral signature concept and features in remote sensing?
[GIS] what’s the difference between spectral signature and features in remote sensing
classificationfeaturesremote sensing
Related Solutions
The advantage of satellites is that they cover the entire globe. Sure Europe doesn't have the same data freedom as something sourced from NASA (the USA have the quaint notion that things paid for with public money should be public!), but those NASA satellites do still circle the entire planet.
There are a lot of possible sources of data, some free, some not:
- http://gcmd.gsfc.nasa.gov/
- http://glovis.usgs.gov/
- http://earthexplorer.usgs.gov/
- http://earthnow.usgs.gov/earthnow_app.html?sessionId=9a2c4eb76504bdf0e9361336ab4c9ee9117659
- http://www.ngdc.noaa.gov/mgg/topo/globe.html
- https://earth.esa.int/web/guest/data-access/catalogue-access/descw (Europe; ESA)
- http://www.esa.int/SPECIALS/Eduspace_EN/SEMX7BANJTF_0.html (If you're using it for teaching)
- http://library.oceanteacher.org/OTMediawiki/index.php/Remote_Sensing_Data_Gateway (a list of sites that contain Remote Sensing data)
It is sometimes difficult to distinguish calibration and correction in remote sensing, because we are not in a laboratory with full control on the measurement. Therefore the two are often mixed.
Sensu stricto, radiometric calibration is the conversion from the sensor measurement to a physical quantity. In remote sensing, the sensor is measuring a radiance from the top of the atmosphere. Therefore the image provider also provide calibration coefficients to convert from digit number (DN) to radiance. Because we can trust the amount of light energy that comes from the sun, the radiance is often normalized into a reflectance values (easier to work with because bounded by 0 and one), so this step can also be part of the calibration. So the calibration gives you a reflectance value, but it is the reflectance on top of the atmosphere (TOA).
Indeed, the proportion of the incident light that is really reflected by the observed object is effected by different factors (mainly topography and atmospheric thickness). The reflectances measured TOA therefore need to be corrected if you need absolute values. This does not depend on the sensor itself, so I would not talk about calibration in this case: you need to correct the values measured TOA in order to estimate the values top of canopy.
To answer your question, I would thus say that DOS is a correction method and DN to TOA reflectance is a calibration. DOS require a stable dark object where you can assume that variability is due to atmospheric noise, which is difficult to find.
EDIT: for more info on Landsat atmospheric correction, I recommend LEDAPS (Masek et al, 2013) For Sentinel-2, different algorithms have been proposed and I cannot give a definitive answer yet. SEN2COR is used a lot, and MAJA is great if you work with time series (also for Landsat, by the way).
Best Answer
The term spectral signature refers to the relationship between the wavelength (or frequency) of electromagnetic radiation and the reflectance of the surface. The signature is affected by several things including the material composition and structure. Some parts of the EMR spectrum, such as the microwave region, are more sensitive to surface structure than other regions. We use the spectral signature (or more often sampled parts of it--bands of satellite imagery) to infer things about the surface such as composition (e.g. vegetation, bare soil, etc.).
A feature on the other hand is simply an object in landscape. For example, a feature may be a field of uniform crop, a road, or building, or any other part of the landscape. We often try to identify features by using their spectral signatures, assuming uniformity, which is not always the case. Sometimes, rather than classifying pixels based on their spectral signatures alone, we also try to account for spatial relations such as the proximity of similar pixels. This is common for example with object-based image segmentation, which attempts to identify features using a combination of spectral and spatial characteristics.