High-resolution remote sensing image scene classification method for small data set

Abstract

The embodiment of the invention discloses a high-resolution remote sensing image scene classification method for a small data set; and a three-scale three-channel end-to-end multi-scale convolution joint neural network model is established. The method comprises three steps: step one, random sub region extraction is carried out on a remote sensing image with an image size of N*N according three dimensions, wherein the dimensions of the sub regions of the image after extraction are N / 2*N / 2, N / 4*N / 4, and N / 8*N / 8 and inputs of a multi-channel convolution feature extractor; step two, a feature joint network based on multi-scale and multi-channel fusion is established and two-two fusion enhancement is carried out on features with different dimensions to realize a high-layer-feature joint enhancement expression; and step three, on the basis of a joint loss function, high-layer features of joint enhancement of the scene are classified. Therefore, high-precision classification of a small sample training set is realized; and the high-layer feature expression capability in the small sample data set and the computing speed are improved.

Description

technical field [0001] The invention relates to the technical field of high-resolution remote sensing image scene classification, in particular to a method for high-resolution remote sensing image scene classification based on a multi-scale convolution joint neural network model. Background technique [0002] With the launch of high-resolution remote sensing satellites such as IKONOS and QuickBird, the resolution of images acquired by remote sensing satellites has been continuously improved. These high-resolution images contain more information than the original low- and medium-resolution images, and because of the variety, variability and distribution complexity of objects in remote sensing image scenes, the scene semantic information contained in them is very large. Difficult to obtain from it. In recent years, deep learning has achieved high recognition accuracy in remote sensing scene classification due to its good representation of high-level features. However, it nee...

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Problems solved by technology

[0003] However, in the remote sensing image scene recognition technology based on deep learning methods, good recognition accuracy often depends on a large amount of training data, which is not suitable for classification on simple small data sets.

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