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Yeseul Kim
Department of Geoinformatic Engineering, Inha University, Incheon 22212, Korea

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Journal article
Published: 13 May 2020 in Remote Sensing
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Dense time-series with coarse spatial resolution (DTCS) and sparse time-series with fine spatial resolution (STFS) data often provide complementary information. To make full use of this complementarity, this paper presents a novel spatiotemporal fusion model, the spatial time-series geostatistical deconvolution/fusion model (STGDFM), to generate synthesized dense time-series with fine spatial resolution (DTFS) data. Attributes from the DTCS and STFS data are decomposed into trend and residual components, and the spatiotemporal distributions of these components are predicted through novel schemes. The novelty of STGDFM lies in its ability to (1) consider temporal trend information using land-cover-specific temporal profiles from an entire DTCS dataset, (2) reflect local details of the STFS data using resolution matrix representation, and (3) use residual correction to account for temporary variations or abrupt changes that cannot be modeled from the trend components. The potential of STGDFM is evaluated by conducting extensive experiments that focus on different environments; spatially degraded datasets and real Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat images are employed. The prediction performance of STGDFM is compared with those of a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM). Experimental results indicate that STGDFM delivers the best prediction performance with respect to prediction errors and preservation of spatial structures as it captures temporal change information on the prediction date. The superiority of STGDFM is significant when the difference between pair dates and prediction dates increases. These results indicate that STGDFM can be effectively applied to predict DTFS data that are essential for various environmental monitoring tasks.

ACS Style

Yeseul Kim; Phaedon C. Kyriakidis; No-Wook Park. A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions. Remote Sensing 2020, 12, 1553 .

AMA Style

Yeseul Kim, Phaedon C. Kyriakidis, No-Wook Park. A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions. Remote Sensing. 2020; 12 (10):1553.

Chicago/Turabian Style

Yeseul Kim; Phaedon C. Kyriakidis; No-Wook Park. 2020. "A Cross-Resolution, Spatiotemporal Geostatistical Fusion Model for Combining Satellite Image Time-Series of Different Spatial and Temporal Resolutions." Remote Sensing 12, no. 10: 1553.

Journal article
Published: 02 September 2017 in Remote Sensing
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To improve the accuracy of classification with a small amount of training data, this paper presents a self-learning approach that defines class labels from sequential patterns using a series of past land-cover maps. By stacking past land-cover maps, unique sequence rule information from sequential change patterns of land-covers is first generated, and a rule-based class label image is then prepared for a given time. After the most informative pixels with high uncertainty are selected from the initial classification, rule-based class labels are assigned to the selected pixels. These newly labeled pixels are added to training data, which then undergo an iterative classification process until a stopping criterion is reached. Time-series MODIS NDVI data sets and cropland data layers (CDLs) from the past five years are used for the classification of various crop types in Kansas. From the experiment results, it is found that once the rule-based labels are derived from past CDLs, the labeled informative pixels could be properly defined without analyst intervention. Regardless of different combinations of past CDLs, adding these labeled informative pixels to training data increased classification accuracy and the maximum improvement of 8.34 percentage points in overall accuracy was achieved when using three CDLs, compared to the initial classification result using a small amount of training data. Using more than three consecutive CDLs showed slightly better classification accuracy than when using two CDLs (minimum and maximum increases were 1.56 and 2.82 percentage points, respectively). From a practical viewpoint, using three or four CDLs was the best choice for this study area. Based on these experiment results, the presented approach could be applied effectively to areas with insufficient training data but access to past land-cover maps. However, further consideration should be given to select the optimal number of past land-cover maps and reduce the impact of errors of rule-based labels.

ACS Style

Yeseul Kim; No-Wook Park; Kyung-Do Lee. Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps. Remote Sensing 2017, 9, 921 .

AMA Style

Yeseul Kim, No-Wook Park, Kyung-Do Lee. Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps. Remote Sensing. 2017; 9 (9):921.

Chicago/Turabian Style

Yeseul Kim; No-Wook Park; Kyung-Do Lee. 2017. "Self-Learning Based Land-Cover Classification Using Sequential Class Patterns from Past Land-Cover Maps." Remote Sensing 9, no. 9: 921.