This document compares hyperspectral and multispectral remote sensing data for land cover analysis and classification. Hyperspectral data has higher spatial, spectral, and radiometric resolution, allowing it to more accurately classify small features, but the data sets are larger and noisier. Multispectral data has lower resolution but can still classify large landscapes effectively. A supervised classification of Florida land covers using each data type found hyperspectral data achieved 99.5% accuracy while multispectral was 91.7% accurate. However, hyperspectral misclassified some areas due to noise in its high resolution data. The best data type depends on the project scope and scale.

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Jeffrey Schorsch
April 19, 2016
Remote Sensing Data Analysis:
Hyperspectral vs. Multispectral
Introduction
Choosing the most effective data for any given remote sensing project is not always the
simplest decision. The decision is made based off of many different factors depending on cost,
timeframe to collect data, the strengths and weaknesses of each sensor type, and so on. While
this report does not go into great detail about the cost and specific time to acquire datasets, these
factors will not be ignored. This report focuses on the strengths and weaknesses of two very
different types of data, hyperspectral and multispectral, in an attempt to answer one question:
why should we choose one over the other when analyzing land cover features? There is no
specific answer to this question, but strengths and weaknesses of each type will be weighed to
find out which one is more effective for the given project. The goal of this report is to inform
amateur researchers about the proper ways to utilize each form of data to reach the best results
possible for any given project.
Background
Multispectral and hyperspectral data was compared using classification and data analysis
methods in ENVI. These data sets differ in resolution spatially, spectrally, and radiometric. This
is what will inevitably affect how accurate each data set is at a large scale and small scale for
land cover classification and analysis. For example, Landsat 8 imagery (used in this project) has
a spatial resolution of 30x30 m. While it is effective at distinguishing various land covers at a
small scale, it is less effective at distinguishing smaller, individual features primarily identified at
a much larger scale (Qihao et al. 2008). Portable Remote Imaging Spectrometer, or PRISM,
(exclusively used in this project for hyperspectral data) has a spatial resolution of .9x.9 m.
Already a stark difference can be seen between each dataset’s spatial resolutions. The smaller
pixel size allows researchers to distinguish and analyze different features that would otherwise
be combined into one ambiguous pixel in multispectral data. The tradeoff for having such a high
spatial resolution means that it can never be used for small scale analysis that Landsat 8 is used

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for. This could only be achieved if the researcher is willing to acquire many datasets of
hyperspectral scenes which can be costly, timely, and difficult to work with.
Qihao et al. (2008) compared the spectral resolutions using both datasets to observe urban
and environmental landscapes. It was regarded that multispectral data did not have a high enough
spectral resolution to be reliable when observing urban features, and hyperspectral data was
recommended. Though multispectral data does not have a spectral resolution as fine as
hyperspectral does not mean it is not useful. Li et al. (2016) found success using multispectral
data when classifying tropical savannahs. Multispectral images are generally only comprised of 6
bands between 450-2350 nm wavelengths and an additional thermal band beyond that scope.
When coupled with various analyzing techniques like NDVI as well as a researcher’s knowledge
of the observed area, multispectral data is very useful and accurate at classifying large
landscapes more efficiently than hyperspectral. On the other hand, hyperspectral data has a fine
spectral resolution useful for classifying smaller features, especially urban. PRISM data consists
of 246 bands between 350-1045 nm that allow researchers to identify features without needing
prior knowledge of the given area. The disadvantage of this data is the weak differentiation of
low-albedo objects. Hundreds of detected wavelengths per .9x.9m pixel can lead to random noise
that may affect the measurability of a given pixel, which can be seen later in the results. This also
makes datasets massive in storage size (roughly 8 GB) and costly to acquire in quantities.
The last factor to compare is radiometric resolution. The Landsat 8 sensor collects data
around a 12-bit range. This means that each band is translated into 4,096 grey scale levels. This
is a greater bit depth in comparison to older Landsat products (Landsat, 2015). PRISM data
boasts a bit depth of 14 with over 16,000 gray scales levels (PRISM, 2015). In simpler terms, the
higher the bit depth a sensor has the wider the range of values each pixel has. This allows
observers to perceive greater differences in reflective values for each pixel; therefore containing
more information than one that has a lower bit depth. It would be easy to say that hyperspectral,
with a bit depth of 14, has a greater radiometric resolution than Landsat 8 with a bit depth of 12.
However, this is not entirely true considering that this resolution is affected by noise. As
discussed earlier, hyperspectral data’s small pixel size can make it easily affected by random
noise across 246 bands. This makes the two data products difficult to compare in terms of

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radiometric resolution and should be weighed by each dataset’s advantages and disadvantages
toward the given observed area.
Data Analyzed
The multispectral data analyzed in this project consisted of the southern coast of Florida
and almost the entirety of the Florida Keys. The hyperspectral data consisted of a narrow scene
stretching vertically across the island of Long Key in the Florida Keys. Both of the multispectral
and hyperspectral data were subset into a scene of Long Key roughly 1.1 square km
(24°48'45.2"N, 80°49'47.6"W). The multispectral data was set to red band = 865 nm, green band
= 655 nm, and blue band = 561 nm. The hyperspectral data was set to similar bands for the most
accurate comparison: red band = 860 nm, green band = 650 nm, and blue band = 562 nm. This
created two CIR subsets of Long Key.
Method
After this initial setup was completed there was a hyperspectral subset and multispectral
subset equal in both dimension and area. As discussed in the section titled “Background”, the
multispectral subset was very pixelated due to having a lower spatial resolution than
hyperspectral. The hyperspectral subset remained very clear and features remained fairly
distinguishable. The two subsets were set to nearly equal band combinations but came from
different sensors and therefore have differing spatial, spectral, and radiometric resolutions
naturally. This can result in slightly differing data values in each subset. This would lead to bias
in the final classification results unless corrected. Nevertheless, this bias was nearly impossible
to avoid. The multispectral subset went through a radiometric calibration to correct for this bias.
However, the hyperspectral subset was missing gain and offset values for the same calibration.
After a bit of research, the values could not be found, but there was a passage on the PRISM
website that explained that the data already went through radiometric calibration. This may lead
to a slight bias considering the process was not verified as going through the exact same
calibration as the multispectral subset to ensure a bias-free result.
Visually, the two subsets were set to Linear 2% and it seemed they portrayed the same
reflectance values overall disregarding the massive spatial resolution difference. Two samples of
the subsets can be seen immediately below (left: hyperspectral, right: multispectral).

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The two original subsets these samples were derived from were used to create classification
schemes to further compare their effectiveness in large scale use.
Supervised classification was performed on the multispectral subset. Training samples
were collected in three different classes: water, vegetation, and urban. Due to limited pixels to
classify, water training samples were limited to 73, vegetation to 40, and urban to 21. These low
sample sizes reluctantly did not seem to have any particular impact on the classification’s
accuracy. The resulting classification map can be seen immediately below.
Red: Water
Green: Vegetation
Blue: Urban

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After creating 3 ROIs to collect test samples for each class, an accuracy assessment was
produced. This will be addressed in the section titled “Results”.
The hyperspectral data underwent a similar, yet slightly different approach that happens
to be more accurate for this data type. The subset was classified using SAM (spectral angle
mapper) which compares spectral angles between training pixels and unidentified pixels. Smaller
angles show higher likelihood that it belongs in the same class. Greater angles show less
likelihood, and therefore the pixel is placed in a class with a reference pixel of a smaller angle.
The three classes remained the same as the previous classification. Many more pixels were able
to be sampled considering the pixel size is much smaller. The rule of thumb for an accurate
classification is to collect more pixels than there are bands (>246 pixels per class). This was
simple for water and vegetation which easily had over a 30,000 training pixels apiece. The urban
class had only about 800 pixels considering urban features are fairly small. The result can be
found immediately below.
Red: Water
Green: Vegetation
Blue: Urban

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Each class was separated and overlaid with its corresponding rule image to visually show how
accurate each class was. The darker the pixel is, the smaller the spectral angle is, showing it is
most likely to fall into that particular class (the colored pixels – red, green, blue – are the
classifications). The three images can be found immediately below.
Water

Vegetation


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Urban

It can be seen that the rule images and their corresponding classifications seem to match up well
portraying an accurate classification. Again, 3 ROIs were created to collect test samples. This
assessment will be addressed below in the section titled “Results”.
Results
The accuracy assessments were much more impressive than what was anticipated. For the
multispectral classification, there was an overall accuracy of 91.7910%. This was a successful
result considering the low
sample size of pixels and the
medium spatial resolution of
Landsat 8 data. The urban
class has the lowest
accuracy, 52.38%, but this
was expected. 30m x 30m
pixel size is not sufficient
enough to detect individual
urban features, and therefore
pixels project weighted
values of the features
contained within them – in
this case, both urban and
vegetation features. This low

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accuracy can be seen on the classification map where urban pixels seem to extend from the
inland lake where most likely a stream with vegetation – not urban features – is present. The
Kappa Coefficient .8587 illustrates that the confusion matrix itself, and its comparison of each
class, is roughly 86% reliable.
After assessing the SAM classification of the PRISM data using test sample ROIs, the
confusion matrix shows incredible accuracy of 99.5266%. The Kappa Coefficient is also much
higher than the previous assessment, but this comes as no surprise considering hyperspectral
data’s reliability spatially
and spectrally at large
scales. However, this does
not mean that there is an
absence of error. First of
all, .02% of all pixels were
left unclassified. This
compares to 0% in the
previous assessment. Also,
several pixels within both
the water and vegetation
classes were falsely
classified. On the other
hand, it accounts for less
than a .7% error in each of
these two classes making
it very accurate
nonetheless. Surprisingly,
it is reported that 0% of urban pixels were misinterpreted. Conversely, the user accuracy is much
less than desirable for this classification. This error can be seen in the original classification map.
An area bordering the north side of the inland body of water is largely misclassified as an urban
area.

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This error can be explained by hyperspectral data’s noise interference discussed in the
section titled “Background”. Both urban and shallow water features in some cases have similar
reflectance values making it difficult for SAM to pick up these subtle differences. The sensor is
detecting 246 wavelengths per pixel, and therefore random noise can slightly affect the data
value of the pixels. For example, sand on the road is reflecting values similar to that of sand
detected in shallow water. The 246 bands are detecting urban signatures along with sand, just as
it is detecting water signatures along with sand. The sand in this situation can be seen as the
noise that affects the value of the pixel. The only way to adjust for this issue would be to use
more classes. Depending on the heterogonous spectral variability of the landscape, more or less
classes should be used. In this situation at least 6 classes could have been used; it was fixed at 3
just to show its effectiveness compared to 3 classes in multispectral classification. Multispectral
data only detects 8 bands, so the pixels are not projecting this noise at nearly the same
magnitude. It is only detecting the dominate feature in that wavelength – which in this case is the
road or the water. The multispectral classification still confused several pixels for urban features,
but this is most likely due to the low spatial resolution leading to pixels seeming too ambiguous
to classify.
Conclusion
The choice of whether to use hyperspectral or multispectral data (or even something
between the two) depends on the landscape and its scale, and the judgment of the researcher.
Hyperspectral data is efficient at classifying and distinguishing features at a large scale. Its pixel
size is small, whereas multispectral data’s spatial resolution often combines multiple features
into one pixel. A SAM classification – like this one – is quite smooth on a large scale over the
coarseness of the multispectral classification. At a small scale, hyperspectral is nearly useless.
Imagine observing the entire Florida Keys (instead of a section of one island), it would take tens
maybe hundreds of scenes to complete. This would inevitably be too costly, too timely to work
with, too massive for hard-drive space, and all the data may not even be available for download.
Also, at a larger scale, the landscape becomes much more homogenous making it more effective
for satellite data like Landsat 8 to be utilized. Urban areas would be almost undetectable, while
vegetation, shorelines, and water bodies become easier to differentiate. In many ways, the scale
of the landscape helps decide which sensor type is most effective. Lastly, one advantage
hyperspectral has is the amount of data stored within the scene. This allows for the researcher to

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have almost no knowledge of the given area and still be able to classify it correctly. Multispectral
takes a bit more knowledge to distinguish minute features amongst the broad landscapes (ie.
various kinds of tree covers, or urban areas) that may not be so clear.
While it may seem this report was strictly trying to weigh the advantages and
disadvantages of each data type to help guide decisions; this is not entirely the point. Much of
these decisions come down to common sense depending on the landscape scale and the scope of
the project. The scale of each data type is so immensely different that usually there is not much
need for a decision at all. The true purpose was to analyze each data type in order to illustrate its
effectiveness overall so that amateur researchers, like myself, understand the different data
sources available for use, how radiometric, spatial, and spectral resolutions affect research
results, and by what methods to utilize this data properly. These kinds of questions can all be
answered by presenting the information as a decision for the researcher, rather than a bulk of
directionless information.

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References
Landsat 8. (2015). Retrieved April 17, 2016, from http://landsat.usgs.gov/landsat8.php
Li, Z., & Guo, X. (2016). Remote sensing of terrestrial non-photosynthetic vegetation using
hyperspectral, multispectral, SAR, and LiDAR data. Progress In Physical
Geography, 40(2), 276-304. doi:10.1177/0309133315582005.
PRISM website: Instrument. (2015). Retrieved April 17, 2016, from http://prism.jpl.nasa.gov
/instrument.html.
Qihao, W., Xuefei, H., & Dengsheng, L. (2008). Extracting impervious surfaces from spatial
resolution multispectral and hyperspectral imagery: a comparison. International Journal
Of Remote Sensing, 29(11), 3209-3232. doi:10.1080/01431160701469024medium.
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