&lt;title&gt;Multi-spectral band selection for satellite-based systems&lt;/title&gt;

Clodius, William B.; Weber, P.; Borel, Christoph C.; Smith, Barham W.

doi:10.1117/12.319369

Cited by 13 publications

(8 citation statements)

References 8 publications

(3 reference statements)

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“…A multispectral sensor collects spectral data from multiple discrete bands of the electromagnetic spectrum. The flexibility of multispectral sensors arises from the user's ability to preselect and/or interchange the spectral filter elements within individual channels, thereby allowing for the strategic targeting of specific bands of the spectrum [18]. A wealth of literature has established the value of spectral indices derived from multispectral data for the extraction of physical or biophysical information from spectral data.…”

Section: Uav Multispectral Sensorsmentioning

confidence: 99%

Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing

2012

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Unmanned aerial vehicles (UAVs) represent a quickly evolving technology, broadening the availability of remote sensing tools to small-scale research groups across a variety of scientific fields. Development of UAV platforms requires broad technical skills covering platform development, data post-processing, and image analysis. UAV development is constrained by a need to balance technological accessibility, flexibility in application and quality in image data. In this study, the quality of UAV imagery acquired by a miniature 6-band multispectral imaging sensor was improved through the application of practical image-based sensor correction techniques. Three major components of sensor correction were focused upon: noise reduction, sensor-based modification of incoming radiance, and lens distortion. Sensor noise was reduced through the use of dark offset imagery. Sensor modifications through the effects of filter transmission rates, the relative monochromatic efficiency of the sensor and the effects of vignetting were removed through a combination of spatially/spectrally dependent correction factors. Lens distortion was reduced through the implementation of the Brown-Conrady model. Data post-processing serves dual roles in data quality improvement, and the identification of platform limitations and sensor idiosyncrasies. The proposed corrections improve the quality of the raw multispectral imagery, facilitating subsequent quantitative image analysis.

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Section: Uav Multispectral Sensorsmentioning

confidence: 99%

Sensor Correction of a 6-Band Multispectral Imaging Sensor for UAV Remote Sensing

2012

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“…Since the presence of water vapor can effect the ground temperature determination from spacecraft imagery, we are interested in developing reliable methods for determining water vapor column abundances for later removal. Figure 1 shows the relative positions of the MTI spectral bands E, F and G. These bands were chosen to allow easy water vapor calculations to be made (see Clodius et al, 1998 3 for a further description of MTI spectral band selections.) We will henceforce use band F to describe the 940 nm water vapor absorption feature and bands E and G to refer to the continuum around the 940 nm absorption feature.…”

Section: Introductionmentioning

confidence: 99%

<title>Comparison of four methods for determining precipitable water vapor content from multispectral data</title>

et al. 2001

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Determining columnar water vapor is a fundamental problem in remote sensing. This measurement is important both for understanding atmospheric variability and also fro removing atmospheric effects from remotely sensed data. Therefore, discovering a reliable, and if possible, automated method for determining water vapor column abundance is important. There are two standard methods for determining precipitable water vapor during the daytime from multi-spectral data. The first method is the Continuum Interpolated Band Ratio (CIBR) (see for example King, et al., 1996 1 ). This method assumes a baseline and measures the depth of a water vapor feature as compared to this baseline. The second method is the Atmospheric Pre-corrected Differential Absorption technique (APDA) (see Schläpfer, et al., 1998 2 ); this method accounts for the path radiance contribution to the top of atmosphere radiance measurement, which is increasingly important at lower and lower reflectance values. We have also developed two methods of modifying CIBR. We use a simple curve fitting procedure to account for and remove any systematic errors due to low reflectance while still preserving the random spread of the CIBR values as a function of surface reflectance. We also have developed a two-dimensional look-up table for CIBR; CIBR, using this technique, is a function of both water vapor (as with all CIBR techniques) and surface reflectance. Here we use data recently acquired with the Multi-spectral Thermal Imager spacecraft (MTI) to compare these four methods of determining columnar water vapor content.

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“…These considerations gave a first guide to spectral band selection, with a sense that five spectral bands may be enough to provide the needed data, and a sense those additional bands may be redundant. Much detailed modeling then refined the band choices [32] and confined our initial estimates of the needed information.…”

Section: Application To Imaging Spectroscopymentioning

confidence: 99%

“…Not all is positive, however, since the solar spectrum adds significantly to the short wavelength end of the MWIR region, thus contaminating the shortest MWIR band beyond usefulness. Again one iterates from this starting point toward definition of the best choices in spectral bands (see Figure 2, adapted from Clodius, et al [32]). The figure of merit here is to obtain spectral bands with maximum sensitivity for each of the desired data products ("answers" in the nomenclature of Figure 1), and with low sensitivity to contamination by other effects.…”

Section: Application To Imaging Spectroscopymentioning

confidence: 99%