Repository logo

Multitemporal Relearning with Convolutional LSTM Models for Land Use Classification

Published version

Change log


In this article, we present a novel hybrid framework, which integrates spatial–temporal semantic segmentation with postclassification relearning, for multitemporal land use and land cover (LULC) classification based on very high resolution (VHR) satellite imagery. To efficiently obtain optimal multitemporal LULC classification maps, the hybrid framework utilizes a spatial–temporal semantic segmentation model to harness temporal dependency for extracting high-level spatial–temporal features. In addition, the principle of postclassification relearning is adopted to efficiently optimize model output. Thereby, the initial outcome of a semantic segmentation model is provided to a subsequent model via an extended input space to guide the learning of discriminative feature representations in an end-to-end fashion. Last, object-based voting is coupled with postclassification relearning for coping with the high intraclass and low interclass variances. The framework was tested with two different postclassification relearning strategies (i.e., pixel-based relearning and object-based relearning) and three convolutional neural network models, i.e., UNet, a simple Convolutional LSTM, and a UNet Convolutional-LSTM. The experiments were conducted on two datasets with LULC labels that contain rich semantic information and variant building morphologic features (e.g., informal settlements). Each dataset contains four time steps from WorldView-2 and Quickbird imagery. The experimental results unambiguously underline that the proposed framework is efficient in terms of classifying complex LULC maps with multitemporal VHR images.



Remote sensing, Image segmentation, Feature extraction, Semantics, Training, Deep learning, Task analysis, Classification postprocessing (CPP), con, volutional neural networks (CNNs), deep learning (DL), multi, temporal land use classification, relearning

Journal Title

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Conference Name

Journal ISSN


Volume Title



Institute of Electrical and Electronics Engineers (IEEE)
Engineering and Physical Sciences Research Council (EP/N021614/1)
Engineering and Physical Sciences Research Council (EP/L010917/1)
Engineering and Physical Sciences Research Council (EP/N010221/1)
Engineering and Physical Sciences Research Council (EP/P025234/1)