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Multitemporal Relearning With Convolutional LSTM Models for Land Use Classification

Zhu, Yue and Geiß, Christian and So, Emily and Jin, Ying (2021) Multitemporal Relearning With Convolutional LSTM Models for Land Use Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp. 3251-3265. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2021.3055784. ISSN 1939-1404.

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Official URL: https://ieeexplore.ieee.org/document/9343734

Abstract

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-endfashion. 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.

Item URL in elib:https://elib.dlr.de/141669/
Document Type:Article
Title:Multitemporal Relearning With Convolutional LSTM Models for Land Use Classification
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Zhu, Yueyz591 (at) cam.ac.ukUNSPECIFIED
Geiß, Christianchristian.geiss (at) dlr.deUNSPECIFIED
So, Emilyekms2 (at) cam.ac.ukUNSPECIFIED
Jin, Yingyj242 (at) cam.ac.ukUNSPECIFIED
Date:February 2021
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:14
DOI :10.1109/JSTARS.2021.3055784
Page Range:pp. 3251-3265
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Classification postprocessing (CPP), convolutional neural networks (CNNs), deep learning (DL), multitemporal land use classification, relearning.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Geiß, Christian
Deposited On:19 Apr 2021 09:18
Last Modified:19 Apr 2021 09:18

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