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A Hyperspectral Remote Sensing Image Classification Method

A technology for hyperspectral remote sensing and image classification, applied in neural learning methods, instruments, scene recognition, etc., can solve difficult hyperspectral remote sensing image classification and other problems, achieve the effect of improving classification performance and generalization ability, and expanding data sets

Active Publication Date: 2022-03-04
ANHUI UNIVERSITY
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Problems solved by technology

[0008] The purpose of the present invention is to solve the defect that it is difficult to classify hyperspectral remote sensing images in the prior art, and provide a hyperspectral remote sensing image classification method to solve the above problems

Method used

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  • A Hyperspectral Remote Sensing Image Classification Method
  • A Hyperspectral Remote Sensing Image Classification Method
  • A Hyperspectral Remote Sensing Image Classification Method

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Experimental program
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Embodiment 1

[0154] In order to verify the influence of different parameter settings in the method proposed by the present invention on the classification accuracy, several factors that affect the classification effect of the model in the MSRA-G method will be analyzed, mainly including dimensionality reduction d, input sample window size w, and learning rate lr. The batch size is unified to 64, the iteration is 200 times, and the average value of the classification accuracy of 10 experiments is used as the experimental result.

[0155] Figure 5 It shows the impact of the classification method MSRA-G on the classification accuracy under different dimensions d in the present invention. From Figure 5 It can be observed that different dimensionality reduction d makes OA perform differently, basically showing a trend of first increasing and then decreasing. For the IP dataset, when d is 14, OA reaches the highest value, and then the OA value is basically stable. While the UP and SA data ...

Embodiment 2

[0158] Embodiment 2: In order to further verify the performance of the algorithm in this paper, this embodiment uses three data sets of IP, UP, and SV for verification, and randomly selects 5%, 1%, and 0.5% from each type of ground object as a training sample set, The rest are used as the test sample set. And use REF-SVM, 3D-CNN, MSDN, HybridSN, SSRN and R-HybridSN six hyperspectral image classification methods as the comparative experimental objects, the classification results take the mean of ten experimental results, and record the standard deviation, so as to verify MSRA - Classification performance of the G method.

[0159] Table 2 Comparison table of classification accuracy of different classification methods on IP dataset

[0160]

[0161]

[0162] right figure 2 The IP hyperspectral images shown are classified, and the MSRA-G classification method proposed in the present invention uses GANs to generate synthetic samples to achieve the purpose of expanding the ...

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Abstract

The invention relates to a hyperspectral remote sensing image classification method, which overcomes the problems of insufficient feature extraction and unsatisfactory classification accuracy of the hyperspectral remote sensing image under limited sample size compared with the prior art. The invention includes the following steps: acquisition and preprocessing of hyperspectral remote sensing images; construction and training of generative confrontation networks; acquisition of expanded training samples; construction of multi-scale residual attention networks; training of multi-scale residual attention networks; Acquisition of classified hyperspectral remote sensing images; acquisition of classification results of hyperspectral remote sensing images. In the case of insufficient training samples, the present invention can also obtain ideal hyperspectral remote sensing image classification results.

Description

technical field [0001] The invention relates to the technical field of hyperspectral remote sensing images, in particular to a hyperspectral remote sensing image classification method. Background technique [0002] As an important means of earth observation, hyperspectral remote sensing can obtain fine ground object attribute information, and has received enough attention in recent years. Due to the rapid development of satellite sensor technology, a large number of hyperspectral remote sensing images have been captured, which have rich spectral information and spatial information, which brings new opportunities for the application of hyperspectral remote sensing technology. Hyperspectral classification aims to train a classifier based on some labeled pixel samples, and then predict the labels corresponding to other pixel samples in the image to obtain the spatial distribution of different objects in the image. However, in the process of hyperspectral image classification, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/13G06V10/46G06V10/77G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/048G06N3/045G06F18/2135G06F18/2415
Inventor 赵晋陵胡磊黄林生梁栋徐超黄文江
Owner ANHUI UNIVERSITY
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