Abstract
In the context of carbon neutrality, forest cover change detection has become a key topic of global environmental monitoring. As a large-scale monitoring technique, remote sensing has received obvious attention in various land cover observation applications. With the rapid development of deep learning, remote sensing change detection combined with deep neural network has achieved high accuracy. In this paper, the deep neural network is used to study forest cover change with Landsat images. The main research ideas are as follows. (1) A Siamese detail difference neural network is proposed, which uses a combination of concatenate weight sharing mode and subtract weight sharing mode to improve the accuracy of forest cover change detection. (2) The self-inverse network is introduced to detect the change of forest increase by using the sample data set of forest decrease, which realizes the transfer learning of the sample data set and improves the utilization rate of the sample data set. The experimental results on Landsat 8 images show that the proposed method outperforms several Siamese neural network methods in forest cover change extraction.
Highlights
Changes in forest cover affect the delivery of important ecosystem services, including biodiversity richness climate regulation, carbon storage, and water supplies [1,2]
In order to solve this problem, the purpose of this paper is to introduce an application of Siamese neural network (SNN) to extract forest cover change
In order to make the images of different time comparable, the digital number (DN) value of the images used in the experiment are converted into surface reflectance
Summary
Changes in forest cover affect the delivery of important ecosystem services, including biodiversity richness climate regulation, carbon storage, and water supplies [1,2]. Forest cover change mapping has important research value and economic benefits according to climate and carbon-cycle modeling [4,5], hydrological studies [6], habitat analyses [7,8,9], biological conservation [10], and land-use planning [11,12]. Remote sensing observation is a timely and accurate means to detect forest cover change on a large scale [13,14]. In the field of hyperspectral image, Huang et al used 500 m MODIS time series images and a distance metric-based method to detect forest cover change in Pacific
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