Skip to content

Hyperspectral remote sensing image classification based on deep learning

Notifications You must be signed in to change notification settings

amilyxy/Hyperspctral_classification

Repository files navigation

a multi-scale 3D convolutional neural network is used in hyperspectral image classification.

Overview:

this is my graduation project that classify indian_pines、Pavia_university and Salinas dataset based on deep_learning. download the dataset

Language:

Python3.x

Tools:

Tensorflow

Describe:

The sensor source of image does not require much pre-processing, in order to reduce the burden on the computer, the input data is compressed using PCA and 96% effective spectral information is extracted. The data is normalized and input into a three-dimensional convolutional network to get feature of empty and spectrum. The three-dimensional convolutional network uses multi-scale convolution kernels to extract multi-scale spatial features, then the joint feature maps of the spectral and spatial properties of the hyperspectral image fed through a fully connected layer, which finally predicts each pixel label through the Softmax classifier.

About

Hyperspectral remote sensing image classification based on deep learning

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages