1.  Field of the Invention
The present invention relates to a remote image processing technology, and more particularly, to a decomposition apparatus for refining composition of mixed pixels and a decomposition method for refining composition of mixed pixels in remote sensing images, to perform analysis and processing of remote sensing images. 
2.  Description of the Related Art
The remote sensing technology has been developing since its introduction in 1960s until today in general technologies involving modern physics, space science, computer technology, mathematical methods, and earth science theories.  The remote sensing technology plays more and more important role in the study of the Earth's surface resources. 
With the development of the image forming technology, the application in the fields of multi-frequency remote sensing images has been increasing.  Due to the optical and physical characteristics of the imaging sensor as well as the complexity of the Earth's surface to be monitored, a plurality of land cover types are often included in one pixel (which is called a mixed pixel) in the obtained remote sensing image.  In order to improve the accuracy of land cover analysis, it is necessary to extract the composition ratio of each type of land cover from the mixed pixel based on the spectral characteristics of the land cover. 
Currently, such a mixed pixel decomposition method for extracting the composition ratio of each type of land cover from the mixed pixel is the spectrum mixed model for mainly analyzing one pixel.  The spectrum mixed model performs analysis according to different mixing methods for each pixel.  For example, Patent document 1 (Chinese patent CN 101221243 A) discloses a decomposition method of mixed pixels of remote sensing images based on non-negative matrix factorization.  Further, Patent document 2 (Chinese patent CN 102054273 A) discloses a decomposition method of mixed pixels of hyperspectral remote sensing images based on monomer triangle decomposition.  More specifically, in Patent documents 1 and 2, the decomposition method assumes that one mixed pixel is a spectral linear mixture of a plurality of end members, extracting the end members to obtain the components of each land cover type in the mixed pixel by the non-negative matrix factorization iterative calculation method or the triangle decomposition method.  Here, the end member means each type of pure substance appearing in the image.  It appears as a pure pixel having only a single substance in the image.  Further, the end member vector is a spectral vector that maps a certain kind of pure substance into multi-dimensional spectral space.  When the end member vector is selected, each type of substance selects only one pure pixel. 
Further, Patent document 3 (Chinese patent CN 101221662 A) discloses a decomposition method of mixed pixels of remote sensing images based on self-organization mapping neutral network.  The decomposition method also assumes that the spectral mixing method is a linear form, and calculates the component information after decomposition by the self-organization mapping neutral network combined with the fussy membership of fussy theory.  In Patent document 3, monitoring training is first performed for the self-organization mapping neutral network, and then the mixed pixel is decomposed based on the fussy model. 
Further, Non-patent document 1 (“Unsupervised study of hyperspectral unmixing methods”, doctoral dissertation presented by Sen Jia in the College of Computer Science of Zhejiang University) discloses a mixed pixel unmixing method for directly extracting the end member spectrum and its composition information from the remote sensing image, under the condition that the end member information is completely unknown, by using the unsupervised signal processing (independent components analysis (ICA)) method, based on the constraints such as non-negative conditions of the spectrum mixed model and the end member in the mixed pixel.  This study performs the unsupervised signal unmixing by combining Markov Random Field (MFR) with ICA, while taking into account the continuity of the signal in space to achieve better unmixing effect than fast ICA.  However, the prerequisite of the unsupervised ICA unmixing is the complete independence of the signal as well as the stability of the data statistical characteristics.  It is difficult to satisfy the condition in the process of remote sensing images in multiple frequency bands.  For this reason, this method is not suitable for the decomposition of mixed pixels of remote sensing images. 
In the related art described above, all the decomposition models involved can perform independent decomposition for a single mixed pixel.  However, the individual pixels in the image are not completely independent of each other due to the continuity of the landscape in the image or other reasons.  Thus, an inconsistency may exist in the decomposition results of neighboring pixels, and a problem may occur due to the situation that the decomposition results have nothing to do with the spatial relations. 
Also in the relation art described above, the decomposition results may not show the correlation between land cover types with respect to the decomposition of the pixel in the boundary of the other land cover type.  Thus, the decomposition results are not appropriate, and noise is also present.  In particular, isolation may occur in the decomposition results.  In other words, a certain type, which is decomposed from a certain pixel, is not present in other pixels in the local area, or the appearance of the type in the particular local area is not appropriate.