Forest biomass-based remote sensing image feature selection method and apparatus

Abstract

The present invention provides a forest biomass-based remote sensing image feature selection method and a forest biomass-based remote sensing image feature selection apparatus. The method includes the following steps that: feature values are extracted from a forest remote sensing image, the feature values are preprocessed through an SR (stepwise regression) algorithm, and feature values corresponding to multicollinearity are removed from the preprocessed feature values, so that a feature set can be generated, the initial set of the feature set is a full set; the feature set is updated repeatedly according to the following processes: an SVM (support vector machine) algorithm is trained according to the initialization feature set, so that the weights of feature values in the initialization feature set are determined, an SVM-REF (support vector machine-recursive feature elimination) algorithm and the weights are adopted to construct the feature sequencing coefficient of the feature values in the feature set, and the feature values in the feature set are sequenced according to the feature sequencing coefficient, and the feature set is updated according to the sequence of the feature set; and update operation is carried out continuously until the number of feature values in the current feature set is equal to a preset number of feature values, and the current feature set is determined as the optimal feature set used for forest biomass. With the method and apparatus of the invention adopted, the effect of remote sensing image feature selection can be optimized.

Description

technical field [0001] The technical field of remote sensing images of the present invention, in particular, relates to a feature selection method and device for remote sensing images of forest biomass. Background technique [0002] Remote sensing image features are the most essential attributes unique to images and used to distinguish them from other images. There are many kinds of image features, including natural features such as terrain, vegetation, and hydrology, as well as human objects such as houses and roads, and the relationship between these features is also intricate. Therefore, in the analysis of remote sensing images Among them, remote sensing image feature selection technology plays an important role. [0003] Forest biomass accounts for about 90% of the global terrestrial vegetation biomass. It is not only an important indicator of forest carbon sequestration capacity, but also an important parameter for evaluating forest carbon budget. Therefore, the study...

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Problems solved by technology

However, the methods usually used at present will take a long time to select more features, and the results of the derived forest biomass optimization model have large errors, resulting in poor overall results.

[0004] Aiming at the problem that the remote sensing image features selected in the above method are not effective, no effective solution has been proposed so far.

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