Farmland Scene Classification Based on Convolutional Neural Network

This paper proposed a farmland scene classification method based on CNN (Convolutional neural network). The farmland image datasets are divided into 4 types, namely, Crops_field, House_field, Not_farming_field and Woods_field. There are 100 pictures in each type, 80 images in each type are used as t...

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Bibliographic Details
Published in2016 International Conference on Cyberworlds (CW) pp. 159 - 162
Main Authors Zhu Deli, Chen Bingqi, Yang Yunong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
CNN
Convolution
deep learning
deep neural network
Feature extraction
Kernel
Machine learning
Neural networks
Neurons
Training
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Summary:This paper proposed a farmland scene classification method based on CNN (Convolutional neural network). The farmland image datasets are divided into 4 types, namely, Crops_field, House_field, Not_farming_field and Woods_field. There are 100 pictures in each type, 80 images in each type are used as training sets, and the remaining 20 images are processed as test sets. Design a CNN with 2 convolution layers and 2 sub sample layers.In the training process, input images are restricted to 64*64, and the convolutional kernel is 5*5. Use the opensource toolkit of deep learning namely Tensorflow as the realization platform. After 700 times trainings, we validated the effects on the dataset, The corresponding correct rates of the four scenes are 79%, 82%, 76% and 75%. The result show that this method can achieve satisfactory effect.
DOI:10.1109/CW.2016.33