The most prominent properties of digital remote sensing imagery include the number of spectral bands, spatial resolution, radiometric resolution and the image dimension.
The memory needed to store an image can be calculated as follows:
Data types specify the values digital imagery may contain and constrain the values that an expression, such as a variable or a function, may take. Common data types of digital remote sensing imagery are illustrated in the table below.
|Data type||Typical bit width||Value range|
|byte||1 byte = 8 bit||-127 to 127 (signed); 0 to 255 (unsigned)|
|integer||2 byte = 16 bit||-32768 to 32768 (signed); 0 to 65535 (unsigned)|
|float||4 byte = 32 bit||-Inf to +Inf, ‘single precision’; floating point number according to IEEE 754|
|double||8 byte = 64 bit||-Inf to +Inf, ‘double precision’|
Band interleaved by pixel (BIP), band interleaved by line (BIL) and band sequential (BSQ) are common approaches to organize multiband images. BIP, BIL, and BSQ are not image formats themselves but structures for storing the gray values of an image in memory or on disk.
There are hundreds of different raster image formats (see gdal.org), most common image formats regarding digital remote sensing imagery are illustrated in the table below.
|GeoTiff||.tif, .tiff, .gtiff||TIFF + geospatial reference|
|ENVI||generic, often used in imaging spectroscopy community; Header file (.hdr) with meta data!|
|JPEG2000||.jp2, .j2k||used by many data providers; usually for integer values only|
|HDF4, HDF5||.hdf, .h4, .hdf4, .h5, .hdf5||hierarchical data format, version 4 or 5; multi-resolution raster|
|netCDF||Network Common Data Forat; multi-resolution raster|
|SAVE||Standard Archive Format for Europe e.g. Sentinel-1 and Sentinel-2|
Metadata are information about the data and are commonly automatically recorded during data acquisition or added to the data during pre-processing. Metadata are either stored as part of an integrated data format, i.e., stored in the same file, or as an additional file accompanying the image data.
Example GeoTIFF: metadata stored within .tif image. Displaying metadata possible with specific metadata reader (e.g. via QGIS Layer properties).
Example ENVI format: metadata stored in separate header file (.hdr). Displaying and editing metadata possible with text editor.
HyMap (Hyperspectral Mapper) is an airborne imaging spectrometer covering the spectral range from 450 to 2500 nm in 128 bands with spectral resolutions between 10 and 20 nm. The spatial resolution varies with operating flight altitude (i.e., 2000 – 5000 m above ground level) and is usually in the range between 2 and 10 m.
HyMap imagery are used in various application fields, including detailed mapping of urban land cover. The high spectral (hyperspectral) resolution of HyMap data enables the differentiation of surface cover types which are not distinguishable in broadband multispectral data. Due to the relatively high spatial resolution, HyMap imagery can be used as alternative source to lab- and field-spectroscopy for developing spectral libraries of different materials and natural surface cover types.
The EnMAP-Box is developed at the Humboldt-Universität zu Berlin and the Universität Greifswald as a free and open source plug-in for QGIS. The EnMAP-Box is designed to process imaging spectroscopy data and particularly developed to handle data from the upcoming hyperspectral satellite mission Environmental Mapping and Analysis Program (EnMAP). The EnMAP Box enables you to:
More information about the EnMAP-Box, including installation guidelines, user manuals or different application tutorials can be found on the EnMAP-Box project page.
This video (includes sound) illustrates the basic functionalities for visualizing remote sensing imagery in the EnMAP-Box.
The aim of this assignment is to develop a spectral library based the HyMap data from Berlin and the spectral viewer functionality of the EnMAP-Box.
How to develop a spectral library in the EnMAP-Box
Create a zip-archive containing the image of your spectral library as well as the saved spectral library (.sli + .hdr) and upload the zip-file on Moodle.
General submission notes: Submission deadline for the weekly assignment is always the following Monday at 10am. Please use the naming convention indicating session number and family name of all students in the respective team, e.g. ‘s01_surname1_surname2_surname3_surname4.pdf’. Each team member has to upload the assignment individually. Provide single file submissions, in case you have to submit multiple files, create a *.zip archive.
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