High spectral image classification method based on NSCT transformation and DCNN

Abstract

The invention discloses a high spectral image classification method based on NSCT transformation and DCNN. The objective of the invention is to solve the problem that texture details and directivity information of to-be-classified high spectral images cannot be sufficiently excavated in the prior art. The method comprises steps of inputting a high spectral image; carrying out NSCT transformation; carrying out normalization and block taking operation on the transformed stereo blocks; randomly selecting training, verification and test sample sets in a sample set; constructing a depth convolution neural network and setting network super-parameters; training the network; inputting the test samples into the network to obtain actual classification tags and drawing terrain classification result graph; and comparing the classification tags and the reference tags of the test samples, calculating classification evaluation indexes, drawing loss curve graphs of the training and verification samples of increasing along with the iteration times, thereby finishing the terrain classification. According to the invention, more texture details, directivity information and space information of the high spectral image are kept; the classification is quietly precise; and the method can be applied to meteorology and environmental monitoring, land utilization, urban planning and disaster prevention and reduction.

Description

Technical field [0001] The invention belongs to the technical field of image processing, and relates to hyperspectral image classification, in particular to a hyperspectral image classification method based on non-subsampled contour wave (NSCT) transformation and deep convolutional neural network (DCNN). It can be applied to weather monitoring, environmental monitoring, land use, urban planning, disaster prevention and mitigation, etc. Background technique [0002] Because hyperspectral remote sensing images continuously image objects in a wide range of wavelengths, they have the advantages of high spectral resolution and rich information. Therefore, hyperspectral images have been widely used in the field of remote sensing. Hyperspectral image classification has important applications in the fields of geological prospecting, food safety, environmental monitoring, etc. It is also the basis for other applications in the field of hyperspectral remote sensing. Its purpose is to divid...

AI Technical Summary

Problems solved by technology

However, these hyperspectral image classification methods are all carried out in the spatial and spectral domains, and do not mine the potential edge and texture details of the hyperspectral image, while the hyperspectral image will inevitably compress the spectral resolution while improving the spectral resolution. The spatial resolution of hyperspectral images leads to the need for certain techniques to mine the spatial texture details of hyperspectral images. Therefore, the above methods all have the problem of not being able to effectively extract hyperspectral image detail information.

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