Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

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.

Multi-Temporal Land Cover Classification with Sequential Recurrent Encoders

“…However, for sequential tasks, recurrent network architectures, which provide an iterative framework to process sequential information, are generally better suited. Recent approaches utilize recurrent architectures for change detection [23][24][25], identification of sea level anomalies [26] and land cover classification [27]. For long-term dependencies, Jia et al [24] proposed a new cell architecture, which maintains two separate cell states for single-and multi-seasonal long-term dependencies.…”

Review of machine learning methods for Big satellite Data classification

“…Методи глибинного навчання підтвердили свою ефективність для оброб-лення як оптичних (гіперспектральних та мультиспектральних), так і радар-них зображень, побудови різних типів земної поверхні: ідентифікації доріг, будинків [55,[63][64][65]. Найбільш поширені моделі в глибинному навчанні для аналізу геопросторових даних -це згорткові нейронні мережі, глибинні автокодувальники (Deep Auto-encoders (DAE)), глибинні мережі переконань та рекурентні мережі з моделлю тривалої короткочасної пам'яті [55,[65][66][67][68].…”

Multi-Temporal Land Cover Classification With Long Short-Term Memory Neural Networks

“…While these approaches follow a generic feature extraction and classification pipeline, Siachalou et al (2015) utilize a hidden markov model (HMM) approach which retains sequential consistency of multi-temporal observations on four LANDSAT-7 and one RAPIDEYE observation of Thessaloníki, Greece in 2010. Methodically similar to ours, Lyu et al (2016) LSTM Cell utilize RNNs and LSTM architectures to multispectral LANDSAT-7 and hyperspectral EO-1 HYPERION images, but-in contrast to our approach-for the purpose of change detection.…”