Abstract
Land-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timely manner. However, conventional approaches always have the limitations of large amounts of sample collection and exploitation of relational knowledge between multi-modality remote sensing datasets. With some global land-cover products being available, it is important to produce new land-cover maps based on the existing land-cover products and time series images. To this end, a novel transfer learning based automatic approach was proposed for updating land cover maps of rapidly urbanizing regions. In detail, the proposed method is composed of the following three steps. The first is to design a strategy to extract reliable land-cover information from the historical land-cover map for one of the images (source domain). Then, a novel relational knowledge transfer technique is applied to transfer label information. Finally, classifiers are trained on the transferred samples with spatio-spectral features. The experimental results show that aforementioned steps can select sufficient effective samples for target images, and for the main land-cover classes in a rapidly urbanizing region; the results of an updated map show good performance in both precision and vision. Therefore, the proposed approach provides an automatic solution for urban land-cover mapping with a high degree of accuracy.
Highlights
Over past decades, rapid population growth and urbanization have taken place at an unprecedented rate all over the world
Most of them derived by using satellite images at 300–1000 m spatial resolution, such as University of Maryland land-cover dataset (UMD) [16], Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover product (MOD12Q1 and MCD12Q1) [17] and Climate Change Initiative land-cover product (CCL-LC) from European space agency (ESA), did not provide sufficient thematic detail or change information [14], especially in urban areas
In this approach, training samples from source domain are used for initializing the learning task and the data of target domain is applied to adapting the model by a series of methods and strategies, such as Domain Adaptation Support Vector Machine (DASVM) [36], Active Learning (AL) technique [37], Change-Detection-driven Transfer Learning (CDTL) [38], Geodesic Flow Kernel Support Vector Machine (GFKSVM) [39] and iterative source samples reweighting strategy [40]
Summary
Rapid population growth and urbanization have taken place at an unprecedented rate all over the world. A widely used approach is based on adaptation of classifier with source domain samples and labeled/unlabeled target-domain samples In this approach, training samples from source domain are used for initializing the learning task and the data of target domain is applied to adapting the model by a series of methods and strategies, such as Domain Adaptation Support Vector Machine (DASVM) [36], Active Learning (AL) technique [37], Change-Detection-driven Transfer Learning (CDTL) [38], Geodesic Flow Kernel Support Vector Machine (GFKSVM) [39] and iterative source samples reweighting strategy [40].
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