Construction method of threshold learnable local binary network based on texture description and deep learning and classification method of remote sensing images

[0043] This embodiment provides a method for constructing a threshold learnable local binary network based on texture description and deep learning, please refer to figure 1 , the method includes:

[0044] Step S1: Obtain a remote sensing image dataset, divide the dataset into two non-overlapping sub-datasets on average, and use them for training and cross-validation respectively.

[0045] Specifically, split the dataset D equally into two non-overlapping sub-datasets D t ,D v , used for training and cross-validation, respectively; the size of all dataset images is n×n pixels.

[0046] Step S2: Load the ResNet-50 network model pre-trained on the ImageNet data set, and modify the output dimension of the last fully connected layer of the ResNet-50 network model to the dimension corresponding to the image category, and use it in the remote sensing The stochastic gradient descent and backpropagation algorithms are used for fine-tuning on the image data set, and the ResNet-50 de...

[0077] Based on the same inventive concept, this embodiment provides a remote sensing image classification method, including: inputting the remote sensing image to be classified into the threshold value learnable local binary network TLBPNet based on texture description and deep learning constructed in Embodiment 1, and obtaining Image classification results.