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ENVI (The Environment for Visualizing Images) – Envi is one of several commonly used image commercial processing packages. ENVI’s complete image-processing package includes advanced, yet easy-to-use, spectral tools, geometric correction, terrain analysis, radar analysis, raster and vector GIS capabilities, and extensive support for images from a wide variety of sources. In addition, it is built upon the IDL (Interactive Data Language) platform which enables ENVI functionality to be available in IDL and IDL functionality in ENVI.
The easiest way to learn about ENVI is to work your way through some of the tutorials that have been produced:
Landsat 8 - Landsat 8 (formerly called the Landsat Data Continuity Mission or LDCM) is NASA’s eighth satellite in the Landsat series:
MODIS – MODIS (also known as the Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites:
IKONOS - IKONOS provides imagery beginning January 1, 2000 and is a high resolution satellite operated by GeoEye:
In geospatial analyses, data can be categorized into Raster and Vector data. Raster data consist of a regular grid (matrix) of numbers that populate cells (pixels) of known size and shape oriented in a regular pattern of rows and columns. Vector data utilize a coordinate-based system to represent and locate physical elements such as points, lines, and areas (polygons).
Image division or spectral band ratioing is one of the most common mathematical operations applied to multi-spectral images. Ratio images are calculated as the division of digital number (DN) values in one spectral band(s) by the corresponding pixel value in another band(s).
Band ratioing operations can reduce the environmentally induced variations in the DN values of a single band, such as brightness variations caused by topographic slope and aspect, shadows or seasonal changes in sunlight illumination angle and intensity. Therefore, band ratioing tends to emphasize and highlight subtle variations in the actual spectral responses of various surface covers.
Georeferencing - One of the most important, yet hardest to grasp, concepts in remote sensing and GIS is how to correctly georeference (raster or vector data) so that it can be viewed or analyzed along with other georeferenced information. The key point in regards to georeferencing or coordinate transformations is the necessity to locate positions or features in a vector or raster coverage whose real world coordinates are known. The requisite georeferencing information can be collected GPS points in the field of known locations, using points of known latitude/longitude or other coordinates, and using a previously georeferenced image.
Calibration -The primary objective for applying radiometric corrections to a remotely-sensed data set is to reduce the influence of errors or inconsistencies in image brightness values that may limit one's ability to interpret or quantitatively process and analyze the digital data. Radiometric “noise” may be partially a function of sensor characteristics but also environmental factors such as illumination quantity and quality, atmospheric optical properties, and surface properties. Performing radiometric pre-processing is a technique which changes the brightness values of an image to correct for sensor error (e.g. degradation, malfunction) and also for environmental variations on a scene.
Preprocessing – Geometric Correction
The basic premise of a classification is that pixels from the same cover type should be close together in the spectral measurement space (i.e. have similar digital numbers), whereas pixels from different cover types should be comparatively well separated in spectral space (i.e. have very different digital numbers).
Change detection is the process of identifying differences in the state of a feature or phenomenon by observing it at different times. In remote sensing, change detection is useful in land use/land cover change analysis such as monitoring deforestation, vegetation phenology, or urban growth. However, there are many remote sensor systems and environmental parameters that must be considered whenever performing change detection. Failure to understand the impact of the various parameters on the change detection process can lead to inaccurate results.
Many multispectral systems sense radiation in the thermal infrared as well as the visible and reflected infrared portions of the spectrum. However, remote sensing of energy emitted from the Earth's surface in the thermal infrared (3μm to 15μm) is different from the sensing of reflected energy. Multispectral scanning allows the possibility to acquire, display and interpret thermal properties of the Earth's surface.
The main objective of imaging spectroscopy (e.g. hyperspectral imaging) is to measure the spectral signatures of all features within the sensor's field of view. To accomplish this goal, pixels are sampled across many narrowband images at a particular spatial location within the "spectral cube", resulting in a one-dimensional spectrum. The spectrum is a plot of wavelength versus radiance or reflectance. The spectrum can be used to identify and characterize a particular feature within the scene, based on unique spectral signatures or "fingerprints". Spectral data can be obtained using either space-based or airborne platforms, and typically involves scanning many narrowband images simultaneously, while using some type of dispersion grating to produce the spectrum.
Source: Elowitz, Mark R. “What is Imaging Spectroscopy (Hyperspectral Imaging). www.markelowitz.com/Hyperspectral.html