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MODIS mixed pixels decomposition forest information extraction method

A technology of mixed pixel decomposition and information extraction, which is applied in the direction of instruments, character and pattern recognition, computer parts, etc., can solve the problems of poor forest type recognition ability and classification accuracy that cannot meet production needs, etc.

Inactive Publication Date: 2015-11-11
CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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Problems solved by technology

[0003] In order to overcome the shortcomings of the existing MODIS image forest information extraction method that the classification accuracy cannot meet the production needs and the forest type recognition ability is poor, the present invention provides a forest information extraction method, which can effectively use the MODIS data spectral radiation with excellent accuracy It can also improve the lack of spatial resolution of MODIS, and can conveniently and quickly use the MOD09A1 surface reflectance products and MOD13Q1 vegetation index products provided by MODIS images to effectively improve the accuracy of forest coverage mapping and forest type identification

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  • MODIS mixed pixels decomposition forest information extraction method
  • MODIS mixed pixels decomposition forest information extraction method
  • MODIS mixed pixels decomposition forest information extraction method

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Embodiment Construction

[0034] The present invention comprises the following steps:

[0035] Step 1: data acquisition step, obtaining forest remote sensing images;

[0036] Step 2: The preprocessing step, preprocessing the remote sensing image obtained in step 1, and obtaining the preprocessed remote sensing image data, the preprocessing includes: performing projection conversion on the remote sensing data, atmospheric correction, image stitching, eliminating black bands, geometric Calibration, study area extraction;

[0037] Step 3: Analysis of phenological differences of forest types, find out the period of vegetation index that can clearly distinguish each forest type; specifically, first use GPS to obtain (coniferous forest, broad-leaved forest, bamboo forest, shrub forest, water area, cultivated land, Construction land) coordinate information, use GIS software to obtain the gray value of typical sample points in the remote sensing image of coniferous forest, broad-leaved forest, bamboo forest, ...

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Abstract

The invention provides an MODIS mixed pixels decomposition forest information extraction method, being integrated with the standard images of an MOD09A1 surface reflectance product and the standard images of an MOD13Q1 vegetation index product. The MODIS mixed pixels decomposition forest information extraction method is characterized in that the images of different MODIS products are provided with different forest information; and however, as the resolution ratio is low and large amount of mixed pixels exist in the images, the forest information is extracted through an improved end-member purification method and adoption of a linear mixed pixel decomposition model. The MODIS mixed pixels decomposition forest information extraction method of the invention has the advantages of being able to quickly extract the forest cover information in wide range, improving the forest mapping precision and the forest type identification precision, wherein only the MOD09A1 surface reflectance product and the MOD13Q1 vegetation index product are used during the information extraction process, thus obtaining ideal effect and being simple and practicable.

Description

Technical field [0001] The invention relates to a technique for extracting forest information from MODIS remote sensing images, in particular to a method for extracting remote sensing information that can improve the accuracy of forest coverage mapping and forest type identification by using mixed pixel decomposition. Background technique [0002] At present, although the measurement accuracy of medium and high-resolution remote sensing images is relatively high, the price of data is relatively expensive and it is not easy to obtain; low-resolution remote sensing images cover a wide range and are relatively cheap, such as MODIS images, due to their highest spatial resolution The distance is 250m, and MODIS remote sensing images have high spectral radiometric accuracy and can be obtained for free. However, there are a large number of mixed pixels, which often cause certain errors in the classification of ground objects. It is difficult to obtain ideal classification accuracy b...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 林辉陈利孙华
Owner CENTRAL SOUTH UNIVERSITY OF FORESTRY AND TECHNOLOGY
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