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商务合作：From Wikipedia, the free encyclopedia
For Operations under false , see .
A mosaic constructed from a series of 53 images taken through three
by Galileo’s imaging system as it flew over the northern regions of the
in December 1992.
False color (or false colour) refers to a group of
used to display images in color which were recorded in the
or non-visible parts of the . A false-color image is an image that depicts an object in
that differ from those a
(a "true-color" image) would show.
In addition variants of false color such as pseudocolor (), density slicing (), and choropleths () are used for
of either data gathered by a single grayscale channel or data not depicting parts of the electromagnetic spectrum (e.g. elevation in relief maps or tissue types in ).
"True-color" redirects here. For the 24-bit color depth of screens for computers, mobile phones, etc., see .
To understand false color, a look at the concept behind true color is helpful. An image is called a "true-color" image when it offers a natural
rendition, or when it comes close to it. This means that the colors of an object in an image
the same way as if this observer were to directly view the object: A green tree appears green in the image, a red apple red, a blue sky blue, and so on. When applied to black-and-white images, true-color means that the perceived lightness of a subject is preserved in its depiction.
Two exemplary
satellite images showing the same region:
and the city of
The "true-color" image shows the area in actual colors, e.g., the vegetation appears in green. It covers the full
using the red, green and blue / green spectral bands of the satellite mapped to the
of the image.
The same area as a "false-color" image using the , red and green spectral bands mapped to RGB – this image shows vegetation in a red tone, as vegetation reflects much light in the near infrared.
Burns Cliff inside of
on . The color is approximate true color because, instead of the red spectral band, the infrared was used. The result is a metameric failure in the color of the sky, which is slightly green in the image – had a
observer been present, then that person would have perceived the actual sky color to have a bit more orange in it.
The Opportunity rover which captured this image does have a red filter, but it is often not used, due to the higher scientific value of images captured using the infrared band and the constraints of data transmission.
Absolute true-color rendering is impossible. There are three major sources of color error (" failure"):
of the human eye and of an image capture device (e.g. a "").
Different spectral emissions / reflections of the object and of the image render process (e.g. a "" or "").
Differences in spectral irradiance in the case of reflective images (e.g. photo prints) or reflective objects – see
(CRI) for details.
The result of a metameric failure would be for example an image of a green tree which shows a different shade of green than the tree itself, a different shade of red for a red apple, a different shade of blue for the blue sky, and so on.
(e.g. with ) can be used to mitigate this problem within the physical constraints.
Approximate true-color images gathered by spacecraft are an example where images have a certain amount of metameric failure, as the spectral bands of a spacecraft's camera are chosen to gather information on the physical properties of the object under investigation, and are not chosen to capture true-color images.
This approximate true-color
shows the impact crater
on . It was taken by the panoramic camera on the
and is a composite of a total of 258 images taken in the 480, 530 and 750
spectral bands (blue / green, green and near infrared).
A traditional false-color satellite image of Las Vegas. Grass-covered land (e.g. a golf course) appears in red.
A false-color image sacrifices natural color rendition (in contrast to a true-color image) in order to ease the detection of features that are not readily discernible otherwise – for example the use of near infrared for the detection of vegetation in satellite images. While a false-color image can be created using solely the visual spectrum (e.g. to accentuate color differences), typically some or all data used is from
(EM) outside the
or ). The choice of spectral bands is governed by the physical properties of the object under investigation.
As the human eye uses three "spectral bands" (see
for details), three spectral bands are commonly combined into a false-color image. At least two spectral bands are needed for a false-color encoding, and it is possible to combine more bands into the three visual RGB bands – with the eye's ability to discern three channels being the limiting factor. In contrast, a "color" image made from one spectral band, or an image made from data consisting of non-EM data (e.g. elevation, temperature, tissue type) is a pseudocolor image (see below).
For true color, the
channels (red "R", green "G" and blue "B") from the camera are mapped to the corresponding RGB channels of the image, yielding a "RGB→RGB" mapping. For false color this relationship is changed. The simplest false-color encoding is to take an RGB image in the visible spectrum, but map it differently, e.g. "GBR→RGB". For "traditional false-color" satellite images of
a "NRG→RGB" mapping is used, with "N" being the near-infrared spectral band (and the blue spectral band being unused) – this yields the typical "vegetation in red" false-color images.
False color is used (among others) for satellite and space images: Examples are
satellites (e.g. , see example above),
(e.g. the ) or
(e.g. ). Some spacecraft, with
"Curiosity") being the most prominent examples, have the ability to capture approximate true-color images as well.
produce, in contrast the spacecrafts mentioned previously, grayscale images from the visible or infrared spectrum.
Examples for the application of false color:
These three false-color images demonstrate the application of remote sensing in : The left image shows vegetation density and the middle image presence of water (greens / blue for wet soil and red for dry soil). The right image shows where crops are under stress, as is particularly the case in fields 120 and 119 (indicated by red and yellow pixels). These fields were due to be irrigated the following day.
This false-color composite image of the spiral galaxy
is combining four infrared spectral bands from 3.6 to 8.0 . The contribution from starlight (measured at 3.6 micrometers) has been subtracted from the 5.8 and 8 micrometer band to enhance the visibility of the polycyclic aromatic hydrocarbon emissions.
A pseudocolor image (sometimes styled pseudo-color or pseudo color) is derived from a
by mapping each
to a color according to a table or function. Pseudo color is typically used when a single channel of data is available (e.g. temperature, elevation, soil composition, tissue type, and so on), in contrast to false color which is commonly used to display three channels of data.
A typical example for the use of pseudo color is
("thermal imaging"), where
feature only one spectral band and show their grayscale images in pseudo color.
Examples of encoding temperature with pseudo color:
Thermogram of a "" in the foreground and a traditional building in the background. Note the color to temperature key on the right.
Thermal image of a
using pseudocolor encoding – yellow/white indicates hot and red/violet indicates cool.
This pseudocolor image shows the results of a computer simulation of temperatures during
reentry. Areas reaching 3,000 °F (1,650 °C) can be seen in yellow.
Another familiar example of pseudo color is the encoding of
in physical , where negative values (below ) are usually represented by shades of blue, and positive values by greens and browns.
Examples of encoding elevation with pseudo color:
An elevation map of the , showing ocean floor in shades of blue and land in greens and browns.
This color-coded elevation relief map indicates the result of floods on . Please note the color to elevation key on the bottom.
with hypsometric tints of red for the highest points and purple for the lowest.
Pseudocoloring can make some details more visible, as the
is bigger than between successive gray levels alone.
Depending on the table or function used and the choice of data sources, pseudocoloring may increase the information contents of the original image, for example adding geographic information, combining information obtained from infrared or ultra-violet light, or other sources like
Examples of overlaying additional information with pseudo color:
This image shows compositional variations of the Moon overlaid as pseudo color.
A grayscale MRI of a knee – different gray levels indicate different tissue types, requiring a trained eye.
A pseudocolor MRI of a knee created using three different grayscale scans – tissue types are easier to discern through pseudo color.
A further application of pseudocoloring is to store the results
that is, changing the colors in order to ease understanding an image.
An image of
and surrounding waters using density slicing to show
concentration. The ocean color as captured by the satellite image is mapped to seven colors: Yellow, orange and red indicate more phytoplankton, while light green, dark green, light blue and dark blue indicat land and clouds are depicted in different colors.
Density slicing, a variation of pseudo color, divides an image into a few colored bands and is (among others) used in the analysis of
images. For density slicing the range of grayscale levels is divided into intervals, with each interval assigned to one of a few discrete colors – this is in contrast to pseudo color, which uses a continuous color scale. For example, in a grayscale
the temperature values in the image can be split into bands of 2 °C, and each band represented by one color – as a result the temperature of one spot in the thermograph can be easier acquired by the user, because the discernible differences between the discrete colors are greater than those of images with continuous grayscale or continuous pseudo color.
Main article:
The , visualised using a choropleth map.
A choropleth is an
in which areas are colored or patterned proportionally to the
of one or more
being represented. The variables are ma each area contributes one data point and receives one color from these selected colors. Basically it is density splicing applied to a pseudocolor overlay. A choropleth map of a
area is thus an extreme form of false color.
This section requires . (August 2012)
While artistic rendition lends to subjective expression of color,
() has become a culturally significant figure of the
movement by creating false color paintings with
techniques. Some of Warhol's most recognizable prints include a replication of , her image based on a
from the movie . The subject was a
starlet whose death in 1962 influenced the artist. A series of prints were made with endearment but expose her persona as an illusion through his
style of art production which are non-erotic and slightly grotesque. Using various ink color palettes, Warhol immersed himself in a process of repetition that serves to compare personas and every day objects to the qualities of
and . The colors of ink were selected through experimentation of
and do not correlate to false color rendering of the
employed in
image processing. For years the artist continued
false color images of , perhaps his most referenced work being
which was bought in May 2007 by a private collector for 80 million US Dollars.
uses several false-color satellite image layers
, points in a color space that correspond to a color perception that cannot be produced by any physical (non-negative) light spectrum.
. www.crisp.nus.edu.sg.
. landsat.gsfc.nasa.gov. .
Nancy Atkinson (). . .
. www.nasaimages.org. .
GDSC, Nationaal Lucht- en Ruimtevaartlaboratorium (National Laboratory of Air and Space Transport), Netherlands. . GDSC, Nationaal Lucht- en Ruimtevaartlaboratorium (National Laboratory of Air and Space Transport), Netherlands.
. Neuron2.net.
Leonid I. Dimitrov (1995). . Institute of Information Processing, Austrian Academy of Sciences.
C J S N W Campbell (July 1999). . 7th. International Conference on Image Processing and its Applications. University of Bristol.
John Alan Richards and Xiuping Jia (2006).
(4th ed.). Birkh?user. pp. 102–104.  .
J. B. Campbell, "Introduction to Remote Sensing", 3rd ed., Taylor & Francis, p. 153
Wood, Paul (2004). . London, United Kingdom: Yale University Press. pp. 339–341, 354.   2014.
. www.MoMa.org 2014.
Fallon, Michael (2011). . North Mankato, Minnesota, United States of America: ABDO Publishing Company. pp. 44–46.   2014.
Vogel, Carol. . The New York Times 2014.
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