Abstract
When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM) model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1) the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2) the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3) to the authors’ best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection.
Highlights
With the development of remote sensing, the dynamic observation of the Earth has led to a great deal of available, detailed, accurate and up-to-date change information for use in learning about and monitoring our planet [1]
We propose a new change detection method named REFEREE
REFEREE is applied to the transfer experiments, and the results show that REFEREE has remarkable transferability
Summary
With the development of remote sensing, the dynamic observation of the Earth has led to a great deal of available, detailed, accurate and up-to-date change information for use in learning about and monitoring our planet [1]. 2016, 8, 506 monitoring [4], land cover map updating [5], forest degradation survey [6] and glacier melting [7]. In this context, various types of multi-temporal images are exploited to resolve the above problems. Many algorithms have been designed for detecting changes, each with different advantages. These methods can be divided into four categories as follows:
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