Common raster data concepts
Remote sensing contains thousands of operations which can be performed on data. And this field changes on an almost daily basis as new satellites are put into space and computer power increases. Despite its decades long history, we haven't even scratched the surface of the knowledge this field can provide the human race. Once again, similar to the common GIS processes, this minimal list of operations gives you the basis to evaluate any technique used in remote sensing.
Band math
Band math is multidimensional array mathematics. In array math, arrays are treated as single units, which are added, subtracted, multiplied, and divided. But in an array the corresponding numbers in each row and column across multiple arrays are computed simultaneously.
Change detection
Change detection is the process of taking two images of the same location at different times and highlighting the changes. A change can do the addition of something on the ground, such as a new building or the loss of a feature, such as coastal erosion. There are many algorithms for detecting changes among images and also determining qualitative factors, such as how long ago the change took place. The following image from a research project by the US Oak Ridge National Laboratory shows rainforest deforestation between 1984 and 2000 in the state of Rondonia, Brazil. Colors are used to show how recently the forest was cut. Green represents virgin rain forest, white is forest cut within 2 years of the end of the date range, red within 22 years, and the other colors fall in between as described in the legend:
Histogram
A histogram is the statistical distribution of values in a data set. The horizontal axis represents a unique value in a data set while the vertical axis represents the frequency of that unique value within the raster. A histogram is a key operation in most raster processing. It can be used for everything from enhancing contrast in an image to serving as a basis for object classification and image comparison. The following example from NASA shows a histogram for a satellite image which has been classified into different categories representing the underlying surface feature:
Feature extraction
Feature extraction is the manual or automatic digitization of features in an image to points, lines, or polygons. This process serves as the basis for the vectorization of images in which a raster is converted to a vector data set. An example of feature extraction is extracting a coastline from a satellite image and saving it as a vector data set. If this extraction is performed over several years you could monitor the erosion or other changes along that coastline.
Supervised classification
Objects on the earth reflect different wavelengths of light depending on the material they are made of. In remote sensing, analysts collect wavelength signatures for specific types of land cover (for example, concrete) and build a library for a specific area. A computer can then use that library to automatically locate classes in that library in a new image of that same area.
Unsupervised classification
In an unsupervised classification a computer groups pixels with similar reflectance values in an image without any other reference information other than the histogram of the image.