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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: WaterGreen: VegetationBlue: 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: WaterGreen: VegetationBlue: Urban

Each class was

separated and overlaid with its corresponding rule image to visually show how

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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

Urban

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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

accuracy can be seen on the classification map where urban pixels seem to extend from the

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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.

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

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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

have almost no knowledge of the given area and still be able to classify it correctly. Multispectral

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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.

References

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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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