IEEE Geoscience and Remote Sensing Magazine - June 2019 - 142

and urban landscape and sprawl analysis [16]-[18]. Past
CD research has focused mainly on the design of CD techniques in multitemporal MS and SAR images (with both
moderate and high spatial resolutions) due to their large
availability. Only a few studies have addressed CD in HS
images. Due to the expected increase of multitemporal HS
images, CD in multitemporal HS images will become one
of the most interesting research and application directions
in the future.
Most HS optical passive sensors measure the reflectance
of a given object in the range from the visible (0.4- 0.7 nm)
to the short-wave IR (i.e., up to approximately 2.5 nm) .
A given sensor may work only in a certain portion of this
wavelength range. The sensor samples the reflected radiance at a given high spectral resolution (e.g., 10 nm). This
dense sampling of the spectrum allows for a precise representation of the reflectance of each pixel, resulting in a
fine measure of the spectral signature. An example of the

HS cube and pixel spectral signatures associated with different land-cover materials is shown in Figure 1. The spectral signatures of different materials have distinct spectral
shapes that can be used to discriminate among them, even
in terms of the subclasses within a general class (i.e., vegetation types).
Due to the coarse spectral sampling in a few discrete
spectral bands of MS sensors, the early stage of the development of CD techniques for these images focuses mainly
on the identification of strong, abrupt changes. Such landcover changes present a significant variation of the spectral signatures (e.g., vegetation to built-up areas, water to
soil). Taking advantage of the detailed spectral sampling
of HS sensors, the aim of CD in HS images is to detect
changes associated with large spectral variations as well
as those related to minor variations of the spectrum (usually not detectable from MS images). These changes most
often affect only some portions of the spectral signatures.

Different Vegetation

n

io
ns

e

r

ct

e
Sp

0.8
Reflectance

im

D
al

0.6
0.4
0.2
0
500

1,000
1,500
2,000
Wavelength (nm)
Building

Reflectance

0.8
0.6
0.4
0.2
0
500

1,000
1,500
2,000
Wavelength (nm)

Spatial Dimension

Water
Reflectance

0.8
0.6
0.4
0.2
0
500

1,000
2,000
1,500
Wavelength (nm)

Spatial Dimension
FIGURE 1. An example of an HS image cube for an agricultural area in Benton County, Washington (true color composite of

EO1H0440282007128110PY Hyperion product, courtesy of the U.S. Geological Survey), and the pixel spectral signatures associated with different land-cover materials.
142

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

JUNE 2019



IEEE Geoscience and Remote Sensing Magazine - June 2019

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