Chapter 22

An hyperspectral image contains a collection of spectral pixels or equivalently, a collection of spectral bands.


Figure 22.1: Illustration of an hyperspectral cube, spectral pixel and a spectral layer.

An hyperspectral system 22.1 acquired radiance, each pixel contains fine spectral information fine that depends of:

Preliminary treatments allow to perform atmospheric correction for estimating a reflectance cube spectral by subtraction of information extrinsic of the scene (see also 12.2).

22.1 Unmixing

22.1.1 Linear mixing model

Reflectance information depends only of the materials spectral responses in the scene. When the mixture between materials is macroscopic, the linear mixing model of spectra is generally admitted. In this case, the image typically looks like this:


Figure 22.2: Zone which verify the LM model.

We notice 22.2 the presence of pure pixels, and pixel-blending. The LMM acknowledges that reflectance spectrum associated with each pixel is a linear combination of pure materials in the recovery area, commonly known as “”endmembers. This is illustrated in 22.3


Figure 22.3: Decomposition of a hyperspectral cube according to the LMM.

The “ left” term represents the different spectral bands of data cube. The “ right” term represents a “ product” between the reflectance spectra of endmembers and their respective abundances. Abundance band of endmembers is image grayscale between 0 and 1. The pixel i of the abundance band of endmember j is sji. This value is the abundance of endmember j in the pixel i. Under certain conditions  [62], this value can be interpreted as the ratio surface of the material in the overlap zone (22.2). In practice, one can reasonably expect that:

Many techniques of unmixing in hyperspectral image analysis are based on geometric approach where each pixel is seen as a spectral vector of L (number of spectral bands). The spectral bands can then be written as vectors.

SVG-Viewer needed.

Figure 22.4: “Vectorization” of hyperspectral cube. The spectral pixels are stored in the columns of the matrix R and, in equivalently, the spectral bands are assigned to the lines of R.

By deduction of 22.3 et de 22.4, the LMM needs to decompose R as:

R = A.S+N = X +N

J is the number of endmembers and the number of spectral pixels I:

The unmixing problem is to estimate matrices A and S from R or possibly of Ẍ, an estimate of the denoised matrix signal.

Several physical constraints can be taken into account to solve the unmixing problem:

22.1.2 Simplex

Recent unmixing algorithms based on the “property of simplex.” In a vector space of dimension J -1, we can associate to J vectors algebrically independent, J points which define the vertices of a J-simplex 22.5.


Figure 22.5: Illustration of a 2-simplex, a 3-simplex and a 4-simplex.

Hyperspectral cube of L bands, based on J endmembers, may be contained in a affine subspace of dimension J -1. A relevant subspace in the sense of signal-to-noise ratio is generally obtained by Principal Component Analysis (PCA) 14.7. In practice, the J -1 eigenvectors associated with highest values are the columns of a projection matrix V of dimension (Lx(J -1)). Reduced data Z, of dimensions ((J -1)xI) are obtained by the operation: Z = VT (R~)

where each column of ~
R where the average spectrum is subtracted, generally estimated under maximum likelihood. In the subspace carrying the column-vectors Z, endmembers spectra oare associated to the top of the simplex. If the noise is negligible, the simplex circumscribed reduced data.

This property shows that the endmembers research are the vertices of a simplex that circumscribes the data. However, an infinity of different simplices can identify the same data set. In fact, the problem of unmixing generally does not have a unique solution. This degeneration can also be demonstrated by the formalism of the non-negative matrices factorization [3].

It is therefore necessary to choose the most physically relevant solution. All unmixing techniques based on this simplex property admit that the best solution is defined by the allowable minimum volume simplex, or the notion of volume is extended to all finite dimensions (possibly different from 3).

22.1.3 State of the art unmixing algorithms selection

The more recent linear unmixing algorithms exploit the simplex property. It is possible to classify these methods into several families: Family 1

A first family of unmixing algorithms are based on research of the endmembers “among” data. This means that a minimum of one pure pixel must be associated with each endmembers. If this hypothesis is not verified, it will produce an estimation error of the endmembers spectra. The historical advantage of these algorithms are their low algorithmic complexity. The three best known are :

In addition to its success recognized by the community and very competitive algorithmic complexity, the endmembers estimation is unbiased in absence of noise (and when there are pure pixels).

VCA The VCA algorithm is systematically used to initialize various studied algorithms (except MVES, based on a different initialization).

Important elements on the operation of VCA: Family 2

A second family is composed of algorithms which are looking for the simplex of minimum volume circumscribing the data. Phase initialization consists in determining an initial simplex any circumscribing the data. Then, a numerical optimization scheme minimizes a functional, increasing function of the volume generalized, itself dependent of estimated endmembersin the current iteration. The optimization scheme is constrained by the fact that the data have remained on the simplex and possibly by constraints C1, C2 and C3.

Existing algorithms are:

Main differences between algorithms are:

These issues impact the computational complexity and the precision of the estimation.

MVSA [87] MVSA key points:

MVES [23] MVES key points:

SISAL [13] SISAL key points: Family 3

Non negative matrix factorization algorithms (NMF for Non-negative Matrix Factorization). The purpose of this branch of applied mathematics is to factor a non-negative matrix, X in our case, into a product of non negative matrices: AS by minimizing a distance between X and AS and with an adapted regularization to lift the degeneration in an appropriate manner adapted to the physical problems associated with unmixing.

MDMD-NMF [63] Key points of MDMD-NMF: Further remarks

Algorithms of families 1 and 2 estimate “ only” the spectra of endmembers. The estimated abundance maps held a posteri and requires the application of an algorithm like Fully Constrained Least Square (FCLS) otb::FCLSUnmixingImageFilter [57]. VSS includes an specific algorithm for the estimation of the abundances. The unmixing is a general non-convex problem, which explains the importance of the initialization of algorithms.

Overview of algorithms and related physical constraints:






C1 (A > 0)






C2 (S> 0)






C3 (additivity)

Mute (FCLS)






Endmembers in the data

Circumscribed to data

Circumscribed to data

Circumscribed to data

Indirectly by “space” regularization Basic hyperspectral unmixing example

The source code for this example can be found in the file

This example illustrates the use of the otb::VcaImageFilter and otb::UnConstrainedLeastSquareImageFilter . The VCA filter computes endmembers using the Vertex Component Analysis and UCLS performs unmixing on the input image using these endmembers.

The first step required to use these filters is to include its header files.

#include "otbVcaImageFilter.h" 
#include "otbUnConstrainedLeastSquareImageFilter.h"

We start by defining the types for the images and the reader and the writer. We choose to work with a otb::VectorImage , since we will produce a multi-channel image (the principal components) from a multi-channel input image.

  typedef otb::VectorImage<PixelType, Dimension>  ImageType; 
  typedef otb::ImageFileReader<ImageType>         ReaderType; 
  typedef otb::ImageFileWriter<ImageType>         WriterType;

We instantiate now the image reader and we set the image file name.

  ReaderType::Pointer reader = ReaderType::New(); 

For now we need to rescale the input image between 0 and 1 to perform the unmixing algorithm. We use the otb::VectorRescaleIntensityImageFilter .

  RescalerType::Pointer         rescaler = RescalerType::New(); 
  ImageType::PixelType minPixel, maxPixel; 

We define the type for the VCA filter. It is templated over the input image type. The only parameter is the number of endmembers which needs to be estimated. We can now the instantiate the filter.

  typedef otb::VCAImageFilter<ImageType>                       VCAFilterType; 
  VCAFilterType::Pointer vca = VCAFilterType::New(); 

We transform the output estimate endmembers to a matrix representation

    endMember2Matrix = VectorImageToMatrixImageFilterType::New(); 

We can now procedd to the unmixing algorithm.Parameters needed are the input image and the endmembers matrix.

  typedef otb::UnConstrainedLeastSquareImageFilter<ImageType, ImageType, double> UCLSUnmixingFilterType; 
    unmixer = UCLSUnmixingFilterType::New(); 

We now instantiate the writer and set the file name for the output image and trigger the unmixing computation with the Update() of the writer.

  WriterType::Pointer  writer = WriterType::New(); 

Figure 22.6 shows the result of applying unmixing to an AVIRIS image (220 bands). This dataset is freely available at


Figure 22.6: Result of applying the otb::VcaImageFilter and otb::UnConstrainedLeastSquareImageFilter to an image. From left to right and top to bottom: first abundance map band, second abundance map band, third abundance map band.

22.2 Dimensionality reduction

Please refer to chapter 18, page 823 for a presentation of dimension reduction methods available in OTB.

22.3 Anomaly detection

By definition, an anomaly in a scene is an element that does not expect to find. The unusual element is likely different from its environment and its presence is in the minority scene. Typically, a vehicle in natural environment, a rock in a field, a wooden hut in a forest are anomalies that can be desired to detect using a hyperspectral imaging. This implies that the spectral response of the anomaly can be discriminated in the spectral response of “background clutter”. This also implies that the pixels associated to anomalies, the anomalous pixels are sufficiently rare and/or punctual to be considered as such. These properties can be viewed as spectral and spatial hypotheses on which the techniques of detection anomalies in hyperspectral images rely on.

Literature on hyperspectral imaging generally distinguishes target detection and detection anomalies:


Figure 22.7: Notions on detection: ground truth mask, map detection, face detection.

In 22.7, we introduce some notions that will be useful later. Anomaly detection algorithms have an image as input and consider a map serving as a detection tool for making a decision. A adaptive thresholding provides a estimated mask detection that we hope is the most similar possible as the ground truth mask, unknown in practice.

Two approaches dominate the state of the art in anomaly detection in hyperspectral images. Methods which use Pursuit Projection (PP) and methods based on a probabilistic modelization of the background and possibly of the target class with statistical hypothesis tests. The PP consists in projecting linearly spectral pixels on vectors wi which optimizes a criterion sensitive to the presence of anomalies (like Kurtosis). This gives a series of maps of projections where anomalies contrast very strongly with the background. But the automatic estimation of map detection have also major difficulties, including:

Algorithms described here are RX (presented in the first version in [115] and GMRF [123]. They are based on probability models, statistics and hypothesis tests and sliding window. These approaches consist in answering the following question : “The pixel (or set of neighboring pixels) tests looks like background pixels?”, by a process of test statistics. More fully, this approach requires:

Principle of RX and GMRF can be resume with 22.8.


Figure 22.8: Basic workflow of anomaly detection in hyperspectral images.

An optional first step is to reduce the size of spectral data while maintaining information related to anomalies. Then, the spectral pixels are tested one by oneturn (parallelizable task). The pixel test works on a sliding window (see 22.9). This window consists of two sub windows, centered on the pixel test of dimensions L and LL, with L < LL.

Once all the parameters are estimated, a statistical test is performed and assigns a value Λ(i) to the tested pixel i. A map of detection is thus formed.


Figure 22.9: Principle of the sliding window and definitions of parameters L and LL of the sliding window. RX algorithms and GMRF, taken herein in the following sections.

The RX algorithm is available in OTB through the otb::LocalRxDetectorFilter class.