Semantic annotation method for hyperspectral remote sensing image

Abstract

The invention discloses a semantic annotation method for a hyperspectral remote sensing image. The semantic annotation method comprises the following steps of: I, acquiring training data and test data of the hyperspectral remote sensing image through spectral information and an annotated truth value of the hyperspectral remote sensing image; II, constructing a convolutional neural network according to the number of bands of the hyperspectral remote sensing image; III, training the convolutional neural network through the training data to obtain a convolutional neural network model; IV, classifying the test data through the convolutional neural network model to obtain a semantic annotation result; V, constructing a unary potential-energy function of a conditional random field model according to the semantic annotation result; VI, constructing a binary potential-energy function of the conditional random field model in a neighborhood by using an edge constraint model based on an improved mahalanobis distance; VII, carrying out weight adjustment of the unary potential-energy function and the binary potential-energy function on the conditional random field model; VIII, solving the conditional random field model to obtain the semantic annotation result. Through the above steps, the semantic annotation method for the hyperspectral remote sensing image is realized.

Description

technical field [0001] The invention relates to a semantic labeling method for hyperspectral remote sensing images, belonging to the technical field of image processing. Background technique [0002] With the rapid development of hyperspectral remote sensing imaging technology, semantic annotation of hyperspectral remote sensing images, as an important means of remote sensing image information extraction, plays an extremely important role in many fields such as disaster monitoring, agricultural survey, urban planning and military detection. However, hyperspectral remote sensing images contain a huge amount of information, high spectral feature dimensions, complex textures, rich structure and edge details, and an imbalance in the number of training samples between different categories. Therefore, it is of great theoretical and research value to study efficient and feasible semantic annotation algorithms for hyperspectral remote sensing images. [0003] Domestic and foreign s...

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Problems solved by technology

[0004] (1) Purpose of the invention: In view of this, the embodiment of the present invention expects to provide a semantic annotation method for hyperspectral remote sensing images, which can a

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