Semi-supervision and classification method for hyper-spectral remote sensing images based on local stream type learning composition

Abstract

The invention discloses a semi-supervision and classification method for hyper-spectral remote sensing images based on local stream type learning composition. The method comprises the steps: (1) preparing a training sample set, including a small amount of marked data and a large number of non-marked data; (2) choosing k nearest neighbor points for each sample point in the training sample set based on the distance measurement method of spectral angle mapping; utilizing a local stream type learning algorithm to obtain weights among connecting points in a graphic structure and calculating a graphic adjacency matrix to obtain the corresponding graphic structure; classifying the non-marked data based on the graphic adjacency matrix and a GFHF algorithm; and (3) classifying other data points in the images by using a GFHF generalized algorithm. Two widely applied algorithms, including a local stream type learning dimension-reduction algorithm and a semi-supervision and classification algorithm, are contacted by a graph and are better applicable to classify a plurality of hyper-spectral remote sensing data, so that the classification precision of the hyper-spectral remote sensing images can be improved remarkably.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a semi-supervised classification method based on local manifold learning composition, which is used for hyperspectral remote sensing image classification. Background technique [0002] Classification of ground objects using remote sensing images is to identify the physical properties of ground objects based on the acquired remote sensing information. The classification results can clearly reflect the spatial distribution of ground objects for the production of thematic maps, and it is convenient for people to understand and discover laws from them. decision making. Land feature classification has important applications in agricultural monitoring, soil survey, mineral mapping, and urban environmental monitoring. [0003] Hyperspectral remote sensing is a remote sensing science and technology with high spectral resolution. It has the characteris...

AI Technical Summary

Problems solved by technology

[0005] In hyperspectral remote sensing image classification, the heat kernel function (Heat Kernel, HK) is currently used to calculate the image weight, but this weight can only reflect the relationship between two points, and does not make full use of the local area of ​​the neighborhood where the data point is located. structural information

In the field of machine learning, in 2008, some scholars proposed a graph weight calculation method based on non-negative Local Liner Reconstruction (LLR) (F.Wang and C.Zhang,"Label propagation through linear neighborhoods,"IEEE Transactions on Knowledge and Data Engineering, vol.20, no.1, pp.55-67, Jan.2008.), considering the local structure information of the data, but non-negative constraints on the weights may make the Dot refactoring doesn't work well

In 2009, Yan proposed a graph weight calculation method based on sparse representation (Sparse Representation, SR), but due to the need to solve the reconstruction weight globally, the amount of calculation is large (S.Yan and H.Wang, "Semi-supervised learning by sparse representation,"in Proceedings of SIAM International Conference on Data Mining,Nevada,USA,Apr.2009,pp.792-801.)

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