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ISPRS Int. J. Geo-Inf. 2018, 7(9), 367; https://doi.org/10.3390/ijgi7090367

Single-Tree Detection in High-Resolution Remote-Sensing Images Based on a Cascade Neural Network

College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
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Received: 17 July 2018 / Revised: 27 August 2018 / Accepted: 31 August 2018 / Published: 6 September 2018
(This article belongs to the Special Issue Geographic Information Science in Forestry)
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Abstract

Traditional single-tree detection methods usually need to set different thresholds and parameters manually according to different forest conditions. As a solution to the complicated detection process for non-professionals, this paper presents a single-tree detection method for high-resolution remote-sensing images based on a cascade neural network. In this method, we firstly calibrated the tree and non-tree samples in high-resolution remote-sensing images to train a classifier with the backpropagation (BP) neural network. Then, we analyzed the differences in the first-order statistic features, such as energy, entropy, mean, skewness, and kurtosis of the tree and non-tree samples. Finally, we used these features to correct the BP neural network model and build a cascade neural network classifier to detect a single tree. To verify the validity and practicability of the proposed method, six forestlands including two areas of oil palm in Thailand, and four areas of small seedlings, red maples, or longan trees in China were selected as test areas. The results from different methods, such as the region-growing method, template-matching method, BP neural network, and proposed cascade-neural-network method were compared considering these test areas. The experimental results show that the single-tree detection method based on the cascade neural network exhibited the highest root mean square of the matching rate (RMS_Rmat = 90%) and matching score (RMS_M = 68) in all the considered test areas. View Full-Text
Keywords: single-tree detection; high-resolution; remote-sensing images; backpropagation network; cascade neural network single-tree detection; high-resolution; remote-sensing images; backpropagation network; cascade neural network
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Tianyang, D.; Jian, Z.; Sibin, G.; Ying, S.; Jing, F. Single-Tree Detection in High-Resolution Remote-Sensing Images Based on a Cascade Neural Network. ISPRS Int. J. Geo-Inf. 2018, 7, 367.

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