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Pixel-level Classification Method of Remote Sensing Image Based on Convolutional Neural Network with Adaptive Convolution Kernel

A technology of convolutional neural network and remote sensing image, applied in the field of pixel-level classification of remote sensing image based on adaptive convolution kernel convolutional neural network, can solve the problems of poor adaptability and achieve the effect of improving the classification effect

Active Publication Date: 2020-09-01
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0004] In order to overcome the shortcomings of poor adaptability of existing remote sensing image pixel-level classification methods, the present invention provides a remote sensing image pixel-level classification method based on adaptive convolution kernel convolutional neural network

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  • Pixel-level Classification Method of Remote Sensing Image Based on Convolutional Neural Network with Adaptive Convolution Kernel
  • Pixel-level Classification Method of Remote Sensing Image Based on Convolutional Neural Network with Adaptive Convolution Kernel
  • Pixel-level Classification Method of Remote Sensing Image Based on Convolutional Neural Network with Adaptive Convolution Kernel

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[0039] Reference figure 1 . The specific steps of the remote sensing image pixel-level classification method based on the adaptive convolution kernel convolution neural network of the present invention are as follows:

[0040] Step 1: Data preprocessing.

[0041] The remote sensing image R contains r×r pixels.

[0042] First, randomly select M image blocks centered on pixels from the remote sensing image R As a sample, the center pixel category represents the category of its image block, and the size of each pixel block is m×m. Where M is extracted from M samples T As training samples Where M T

[0043] Then, from M T Randomly select N image blocks of size n×n from the training samples As cluster data.

[0044] Step 2: Use the MCFSFDP clustering method to adaptively determine the convolution kernel.

[0045] Image block Pull into a column vector, as a data point in the clustered data set, the data point is represented as j, and the value range of j is [1, N]; jk =dist(j,k) re...

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Abstract

The invention discloses a remote sensing image pixel-level classification method based on an adaptive convolution kernel convolutional neural network, which is used to solve the technical problem of poor adaptability of the existing remote sensing image pixel-level classification method. The technical solution is to first calculate the density and distance value of the data points, then adaptively select the clustering center as the convolution kernel, and finally add the learned convolution kernel to CNN to train the softmax layer of the network, and perform remote sensing on the trained network Image pixel-level classification. The present invention uses the improved clustering algorithm MCFSFDP based on fast searching and finding density peaks, clusters to obtain adaptive convolution kernels, and substitutes them into the CNN structure based on pre-trained convolution kernels. Compared with the CNN structure based on the K-means clustering artificially setting the clustering category pre-learned convolution kernel, the adaptively learned convolution kernel can effectively represent the characteristics of the digital shorthand data information and improve the pixel-level classification effect of the remote sensing image .

Description

Technical field [0001] The invention relates to a remote sensing image pixel-level classification method, in particular to a remote sensing image pixel-level classification method based on Adaptive Kernels Based Convolutional Neural Network (Adaptive Kernels Based CNN). Background technique [0002] The existing pixel-level classification methods of remote sensing images mainly fall into two categories: one is based on artificially designed features; the other is based on deep learning features. [0003] literature" M, Kiselev A, Alirezaie M, Et al. Classification And Segmentation Of Satellite Orthoimagery Using Convolutional Neural Networks[J].Remote Sensing, 2016, 8(4): 329." discloses a remote sensing image classification method based on deep learning, The convolution kernel is obtained through pre-learning and does not need to be updated through error feedback. It is proposed that the K-means clustering algorithm is used to learn the convolutional neural network convolution k...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/04G06F18/24137
Inventor 张艳宁丁晨李映夏勇魏巍张磊
Owner NORTHWESTERN POLYTECHNICAL UNIV
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