An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring
Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of h...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/10/2572 |
| _version_ | 1797476033496088576 |
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| author | Soyeon Park No-Wook Park Sang-il Na |
| author_facet | Soyeon Park No-Wook Park Sang-il Na |
| author_sort | Soyeon Park |
| collection | DOAJ |
| description | Continuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of high spatial resolution images, it is necessary to develop a new STIF model that can effectively reflect the properties of high spatial resolution satellite images for small-scale crop field monitoring. This paper proposes an advanced STIF model using a single image pair, called high spatial resolution image fusion using object-based weighting (HIFOW), for blending high spatial resolution satellite images. The four-step weighted-function approach of HIFOW includes (1) temporal relationship modeling, (2) object extraction using image segmentation, (3) weighting based on object information, and (4) residual correction to quantify temporal variability between the base and prediction dates and also represent both spectral patterns at the prediction date and spatial details of fine-scale images. The specific procedures tailored for blending fine-scale images are the extraction of object-based change and structural information and their application to weight determination. The potential of HIFOW was evaluated from the experiments on agricultural sites using Sentinel-2 and RapidEye images. HIFOW was compared with three existing STIF models, including the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. Experimental results revealed that the HIFOW prediction could restore detailed spatial patterns within crop fields and clear crop boundaries with less spectral distortion, which was not represented in the prediction results of the other three models. Consequently, HIFOW achieved the best prediction performance in terms of accuracy and structural similarity for all the spectral bands. Other than the reflectance prediction, HIFOW also yielded superior prediction performance for blending normalized difference vegetation index images. These findings indicate that HIFOW could be a potential solution for constructing high spatial resolution time-series images in small-scale croplands. |
| first_indexed | 2024-03-09T20:52:12Z |
| format | Article |
| id | doaj.art-08d79d5e27e047e9b699fcba81cddebe |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T20:52:12Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-08d79d5e27e047e9b699fcba81cddebe2023-11-23T22:29:29ZengMDPI AGAgronomy2073-43952022-10-011210257210.3390/agronomy12102572An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland MonitoringSoyeon Park0No-Wook Park1Sang-il Na2Department of Geoinformatic Engineering, Inha University, Incheon 22212, KoreaDepartment of Geoinformatic Engineering, Inha University, Incheon 22212, KoreaNational Institute of Agricultural Sciences, Rural Development Administration, Wanju 55365, KoreaContinuous crop monitoring often requires a time-series set of satellite images. Since satellite images have a trade-off in spatial and temporal resolution, spatiotemporal image fusion (STIF) has been applied to construct time-series images at a consistent scale. With the increased availability of high spatial resolution images, it is necessary to develop a new STIF model that can effectively reflect the properties of high spatial resolution satellite images for small-scale crop field monitoring. This paper proposes an advanced STIF model using a single image pair, called high spatial resolution image fusion using object-based weighting (HIFOW), for blending high spatial resolution satellite images. The four-step weighted-function approach of HIFOW includes (1) temporal relationship modeling, (2) object extraction using image segmentation, (3) weighting based on object information, and (4) residual correction to quantify temporal variability between the base and prediction dates and also represent both spectral patterns at the prediction date and spatial details of fine-scale images. The specific procedures tailored for blending fine-scale images are the extraction of object-based change and structural information and their application to weight determination. The potential of HIFOW was evaluated from the experiments on agricultural sites using Sentinel-2 and RapidEye images. HIFOW was compared with three existing STIF models, including the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC. Experimental results revealed that the HIFOW prediction could restore detailed spatial patterns within crop fields and clear crop boundaries with less spectral distortion, which was not represented in the prediction results of the other three models. Consequently, HIFOW achieved the best prediction performance in terms of accuracy and structural similarity for all the spectral bands. Other than the reflectance prediction, HIFOW also yielded superior prediction performance for blending normalized difference vegetation index images. These findings indicate that HIFOW could be a potential solution for constructing high spatial resolution time-series images in small-scale croplands.https://www.mdpi.com/2073-4395/12/10/2572multi-sensor imagesresolutionimage segmentationcrop monitoring |
| spellingShingle | Soyeon Park No-Wook Park Sang-il Na An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring Agronomy multi-sensor images resolution image segmentation crop monitoring |
| title | An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring |
| title_full | An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring |
| title_fullStr | An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring |
| title_full_unstemmed | An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring |
| title_short | An Object-Based Weighting Approach to Spatiotemporal Fusion of High Spatial Resolution Satellite Images for Small-Scale Cropland Monitoring |
| title_sort | object based weighting approach to spatiotemporal fusion of high spatial resolution satellite images for small scale cropland monitoring |
| topic | multi-sensor images resolution image segmentation crop monitoring |
| url | https://www.mdpi.com/2073-4395/12/10/2572 |
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