Tree species classification of high resolution image combining with object-oriented and deep feature

doi:10.13474/j.cnki.11-2246.2019.0109

Abstract

Abstract:

A tree species classification of high resolution image combining with object-oriented and deep feature is proposed to overcome the problem that traditional manual extraction features need professional knowledge and difficult to extract high quality features.In order to obtain the precise boundary of tree species, the method firstly uses multiscale segmentation technology to segment the whole remote sensing image, and selects the training samples as the input of the deep convolution neural network.In order to avoid over-fitting caused by a small number of samples, transfer learning method is used to initialize the deep convolution neural network with the parameters of VGG16 model trained on ImageNet. Using global average pooling compression parameters, a 1024 nodes fully connected layer and 7 nodes Softmax classifier are added at the end of the network. The network is trained by back propagation and Adam optimization algorithm.Finally, the whole remote sensing image is classified and the tree thematic map is generated. The test site is located in the Huangfu Mountain National Forest Park in Anhui province.QuickBird high resolution image is the data source.The results show that the overall accuracy and Kappa coefficient of this method are 78.98% and 0.6850 respectively, which can ensure the accuracy of tree species and achieve end-to-end tree species classification.

Key words: high resolution image, tree species classification, convolutional neural network, transfer learning, multiscale segmentation

CLC Number:

P237

TENG Wenxiu, WANG Ni, SHI Huihui, XU Zhenyu. Tree species classification of high resolution image combining with object-oriented and deep feature[J]. 测绘通报, 2019, 0(4): 38-42.

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