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.
Highlights
Reliable information concerning a forest is required to perform extensive forest management, as well as for planning purposes to maintain sustainable forestry
There is widespread interest among many researchers regarding the detection of individual trees and gathering forest information from digital aerial photographs or high-resolution remote-sensing images, and several researchers proposed automatic or semi-automatic single-tree detection methods
To reduce the dependence of the detection method on prior knowledge and improve the generalization performance of the classification model for different scenes, this paper presents a single-tree detection method for high-resolution remote-sensing images based on a cascade neural network
Summary
Reliable information concerning a forest is required to perform extensive forest management, as well as for planning purposes to maintain sustainable forestry. With the increasing availability of high-spatial-resolution data and computational power, a growing amount of remote-sensing research on forestry focused on detecting and measuring individual trees as opposed to obtaining stand-level statistics. High-resolution satellite remote-sensing imagery is currently one of the most widely used types of data in forestry applications [1]. Many remote-sensing satellites can obtain sub-meter remote-sensing images; these satellites include Orbview, WorldView, and QuickBird-2 of the United States, EROS-B and EROS-C of Israel, and Gaofen-2 of China. The color and contour features of trees, which cannot be observed in low-resolution remote-sensing images, can be observed in the high-resolution remote-sensing images. There is widespread interest among many researchers regarding the detection of individual trees and gathering forest information from digital aerial photographs or high-resolution remote-sensing images, and several researchers proposed automatic or semi-automatic single-tree detection methods. The conventional methods of tree detection can be mainly divided into two categories
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