Classification of mixed pixels and Spectral mixture analysis. #Geography #Remote_Sensing #GIS

Classification of mixed pixels and Spectral mixture analysis.

Spectral unmixing
Spectral unmixing = spectral mixture modelling = spectral mixture analysis Spectral unmixing is an alternative to soft classification for sub-pixel analysis.

Spectral unmixing is based on the assumption that spectral signature of satellite images results essentially from a mixture of a small number of pure components (endmembers) with characteristic spectra.

#Geography
#Cartography
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#Remote_Sensing
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Vineesh V,
Assistant Professor Geography,
Directorate of Collegiate Education,
Government of Kerala, India

[geethugs]

There are two major approaches to mixed pixel classification: the linear and the non-linear approach. The linear approach is applicable to macro-mixtures, while the non-linear approach is best suited for the unmixing of intimate mixtures. The linear approach has been used for the spectral unmixing of individual pixels, or sets of mixed pixels. A region of mixed pixels can be characterized through the probability density function of proportions of classes in the pixels.Remote sensor records the reflection factor of the pixels which leads to the confusion of the classification of pixels that are showing the more than one class. These pixels are known as mixed pixels. For example, in a a multispectral image from a satellite, a pixel that response to the signal vegetation, water, urban, rocky or forest at same time is a mixed pixel. Classifying mixed pixels to their appropriate class has been the major issue in remote sensing image processing.
So these kind of classification is done or mix up of pixels are done for getting better results and better analysis.

[susmithak]

Spectral Mixture Analysis (SMA) is a technique for estimating the proportion of each pixel that is covered by a series of known cover types - in other words, it seeks to determine the likely composition of each image pixel. Pixels that contain more than one cover type are called mixed pixels.

[jesnasherin]

In this class explained about the classification of mixed pixels spectral mixture analysis. Spectral mixture analysis is a technique for estimating the proportion of each pixel that is covered by a series of known cover types. Spectral unmixing is based on the assumption that spectral signature of satellite images results essentially from a mixture of a small number of pure components with characteristics spectra.

[vrindas]

Classification of mixed pixels. Spectral mixture analysis is a technique for estimating the propotion of each pixel that is covered by a series of known cover types. The two major approaches of mixed pixel are The Linear and The Non Linear approach

[sabarithasivan]

Classification of mixed pixels spectral mixture Mixture Analysis(SMA) is a technique for estimating the proportion of each pixel that is covered by a series of known covertypes - in other words, it seeks to determine the likely composition of each image pixel. Pixels that contain more than one cover type are calledmixed pixels.Pure” pixels contain only one feature or class. For example, a mixed pixel might contain vegetation, bare ground, and soil crust. A pure pixel would contain only one feature, such as vegetation. Mixed pixels can cause problems in traditional image classifications (e.g.,  supervised or unsupervised classification) because the pixel belongs to more than one class but can be assigned to only a single class. One way to address the problem of mixed pixels is to use SMA, (sometimes called subpixel analysis), and hyperspectral imagery.