A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles

Martı́nez, Pablo; Pérez, R.; Plaza, Javier

doi:10.1016/j.patcog.2004.01.006

Cited by 117 publications

(64 citation statements)

References 44 publications

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“…Mathematical approaches, such as morphological profiles, have been developed to analyze urban hyperspectral data [145,146]. In these approaches, spatial organization is explicitly taken into account.…”

Section: Hyperspectral Approachesmentioning

confidence: 99%

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

et al. 2011

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With urban populations and their footprints growing globally, the need to assess the dynamics of the urban environment increases. Remote sensing is one approach that can analyze these developments quantitatively with respect to spatially and temporally large scale changes. With the 2015 launch of the spaceborne EnMAP mission, a new hyperspectral sensor with high signal-to-noise ratio at medium spatial resolution, and a 21 day global revisit capability will become available. This paper presents the results of a literature survey on existing applications and image analysis techniques in the context of urban remote sensing in order to identify and outline potential contributions of the future EnMAP mission. Regarding urban applications, four frequently addressed topics have been identified: urban development and planning, urban growth assessment, risk and vulnerability assessment and urban climate. The requirements of four application fields and associated image processing techniques used to retrieve desired parameters and create geo-information products have been reviewed. As a result, we identified promising research directions enabling the use of EnMAP for urban studies. First and foremost, research is required to analyze the spectral information content of an EnMAP pixel used to support material-based land cover mapping approaches. This information can subsequently be used to improve urban indicators, such as imperviousness. Second, we identified the global monitoring of urban areas as a promising field of investigation taking advantage of EnMAP's spatial coverage and revisit capability.Remote Sens. 2011, 3 1818However, owing to the limitations of EnMAPs spatial resolution for urban applications, research should also focus on hyperspectral resolution enhancement to enable retrieving material information on sub-pixel level.

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Section: Hyperspectral Approachesmentioning

confidence: 99%

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

et al. 2011

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“…For instance, if used in the RGB colour space, the red channel would be inevitably prioritised, whereas it would be sufficient to permute the bands as GRB to shift this priority to the green channel (figure 1). In the case of multispectral or even hyperspectral images, where usually no a priori priority order exists among the tens or even hundreds of bands, the use of lexicographical ordering is practically justified only after the application of a proper transform (e. g. maximum noise fraction transform, principal components analysis, discrete cosine transform, etc) which will redistribute the total variational information contained within the bands with a monotonic order, and thus artificially create the prioritised band environment [4,20]. Hence making it possible to lexicographically exploit the information concentrated on the first few bands.…”

Section: Lexicographical Orderingmentioning

confidence: 99%

On lexicographical ordering in multivariate mathematical morphology

Aptoula

Lefèvre

2008

Pattern Recognition Letters

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Since mathematical morphology is based on complete lattice theory, a vector ordering method becomes indispensable for its extension to multivariate images. Among the several approaches developed with this purpose, lexicographical orderings are by far the most frequent, as they possess certain desirable theoretical properties. However, their main drawback consists of the excessive priority attributed to the first vector dimension. In this paper, the existing solutions to solving this problem are recalled and two new approaches are presented. First, a generalisation of α-modulus lexicographical ordering is introduced, making it possible to accommodate any quantisation function. Additionally, an input specific method is suggested, based on the use of a marker image. Comparative application results on colour noise reduction and texture classification are also provided.

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“…A similar approach was applied by Pesaresi and Benediktsson [11], who used a composition of mono-channel morphological operations based on SEs of different sizes in order to characterize image structures in high-resolution grayscale urban satellite data. In this work, we use the concept of multichannel morphological profile, defined as a vector where a measure of the spectral variation of the result of pseudo-morphological transformations is stored for every step of an increasing SE series [12]. Following previous work by Benediktsson et al [13], we use an artificial neural network-based approach for the classification of the resulting morphological features.…”

Section: Morphological Profile-based Classification Algorithmmentioning

confidence: 99%

Morphological Hyperspectral Image Classification: A Parallel Processing Perspective

Plaza

2006

Hyperspectral Data Exploitation

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Mathematical morphology (MM) is a theory for spatial structure analysis that was established by introducing fundamental operators applied to two sets [1]. A set is processed by another one having a carefully selected shape and size, known as the structuring element (SE). In the context of image processing, the SE acts as a probe for extracting or suppressing specific structures of the image objects, checking that each position of the SE fits within those objects. Based on these ideas, two fundamental operators are defined in MM, namely erosion and dilation. The application of the erosion operator to an image yields an output image, which shows where the SE fits the objects in the image. On the other hand, the application of the dilation operator to an image produces an output image, which shows where the SE hits the objects in the image. All other MM operations can be expressed in terms of erosion and dilation [2]. For instance, the notion behind the opening operator is to dilate an eroded image in order to recover as much as possible of the eroded image. In contrast, the closing operator erodes a dilated image so as to recover the initial shape of image structures that have been dilated. The filtering properties of the opening and closing are based on the fact that, depending on the size and shape of the considered SE, not all structures from the original image will be recovered when these operators are applied. Because of the nonlinear properties of MM filters, their application generally results in an irreversible, though controlled, loss of information.Although MM operators were originally defined for binary images, they were soon extended to gray-tone (mono-channel) images by viewing these data as an imaginary topographic relief in which the brighter the gray tone, the higher the corresponding elevation [3]. Here, morphological operations can be graphically 353interpreted as the result of sliding a flat SE over the topographical relief, so that the SE defines the new (dilated or eroded) scene based on its spatial properties such as height or width (see Figure 13.1). However, extension of MM operators to multichannel data such as hyperspectral imagery with hundreds of spectral channels is not straightforward. A simple approach consists in applying grayscale MM techniques to each channel separately, an approach that has been called marginal MM in the literature [4]. However, the marginal approach is often unacceptable in remote sensing applications because, when MM techniques are applied independently to each image channel, analysis techniques are subject to the well-known problem of ''false colors''; that is, it is very likely that new spectral constituents not present in the original image may be created as a result of processing the channels separately [5]. An alternative way to approach the problem of multichannel MM is to treat the data at each pixel as a vector. Unfortunately, there is no unambiguous means of defining the minimum and maximum values between two vectors of more than one dimension, and th...

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A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles

Cited by 117 publications

References 44 publications

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

On lexicographical ordering in multivariate mathematical morphology

Morphological Hyperspectral Image Classification: A Parallel Processing Perspective

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