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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Spatio-temporal spectral unmixing of time-series images
AU - Wang, Q.
AU - Ding, X.
AU - Tong, X.
AU - Atkinson, P.M.
PY - 2021/6/15
Y1 - 2021/6/15
N2 - Mixed pixels exist widely in remotely sensed images. To obtain more reliable land cover information than traditional hard classification, spectral unmixing methods have been developed to estimate the composition of the mixed pixels, in terms of the proportions of land cover classes. The existing spectral unmixing methods usually require pure spectra (i.e., endmembers) of each land cover class. However, in areas dominated by mixed pixels (e.g., highly heterogeneous areas), it can be a great challenge to extract a large number of pure endmembers, especially for long time-series data. Meanwhile, intra-class spectral variation remains a long-standing issue in spectral unmixing. In this paper, we propose a spatio-temporal spectral unmixing (STSU) approach to address these issues. The proposed method extends spectral unmixing from the traditional spatial domain to the spatio-temporal domain. It exploits fully the multi-scale spatio-temporal information, by using temporally neighboring fine spatial resolution images to detect land cover changes and, further, extracts the proportion information of unchanged mixed pixels required for training. The STSU method is free of the need for endmember extraction, using directly the extracted mixed training samples to construct a learning model, and it accounts for intra-class spectral variation. Therefore, it is a fully automatic method suitable for dynamic monitoring of land cover changes. The effectiveness of the STSU method was validated through experiments on Moderate Resolution Imaging Spectroradiometer (MODIS) data in five different areas. The proposed STSU method provides a new solution for spectral unmixing of time-series data based on the goal of continuous monitoring at the global scale.
AB - Mixed pixels exist widely in remotely sensed images. To obtain more reliable land cover information than traditional hard classification, spectral unmixing methods have been developed to estimate the composition of the mixed pixels, in terms of the proportions of land cover classes. The existing spectral unmixing methods usually require pure spectra (i.e., endmembers) of each land cover class. However, in areas dominated by mixed pixels (e.g., highly heterogeneous areas), it can be a great challenge to extract a large number of pure endmembers, especially for long time-series data. Meanwhile, intra-class spectral variation remains a long-standing issue in spectral unmixing. In this paper, we propose a spatio-temporal spectral unmixing (STSU) approach to address these issues. The proposed method extends spectral unmixing from the traditional spatial domain to the spatio-temporal domain. It exploits fully the multi-scale spatio-temporal information, by using temporally neighboring fine spatial resolution images to detect land cover changes and, further, extracts the proportion information of unchanged mixed pixels required for training. The STSU method is free of the need for endmember extraction, using directly the extracted mixed training samples to construct a learning model, and it accounts for intra-class spectral variation. Therefore, it is a fully automatic method suitable for dynamic monitoring of land cover changes. The effectiveness of the STSU method was validated through experiments on Moderate Resolution Imaging Spectroradiometer (MODIS) data in five different areas. The proposed STSU method provides a new solution for spectral unmixing of time-series data based on the goal of continuous monitoring at the global scale.
KW - Change detection
KW - Spatio-temporal domain
KW - Spectral unmixing
KW - Support vector machines (SVM)
KW - Time-series
KW - Classification (of information)
KW - Data mining
KW - Extraction
KW - Pixels
KW - Radiometers
KW - Support vector machines
KW - Endmembers
KW - Land cover
KW - Mixed pixel
KW - Spatio-temporal
KW - Spatio-temporal domains
KW - Support vector machine
KW - Time-series data
KW - Times series
KW - Time series
KW - detection method
KW - image analysis
KW - land cover
KW - MODIS
KW - pixel
KW - remote sensing
KW - satellite data
KW - spatial resolution
KW - time series analysis
U2 - 10.1016/j.rse.2021.112407
DO - 10.1016/j.rse.2021.112407
M3 - Journal article
VL - 259
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
SN - 0034-4257
M1 - 112407
ER -