Dual-Branch Remote Sensing Spatiotemporal Fusion Network Based on Selection Kernel Mechanism

Li, Weisheng; Wu, Fangming; Cao, Dongwen

doi:10.3390/rs14174282

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…These models contributed to building a common framework for spationtemporal fusion algorithms that employs the use of two streams and the stepwise modeling of spatial, sensor, and temporal differences. In recent works [8][9][10][11][12][13][14][15], multiscale learning, spatial channel attention mechanisms, and edge reservation have been introduced into CNNs for the extraction and integration of features.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Study of the Accuracy and Change Detection Potential of Blending Time Series Remote Sensing Images with Spatiotemporal Fusion

et al. 2023

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Over one hundred spatiotemporal fusion algorithms have been proposed, but convolutional neural networks trained with large amounts of data for spatiotemporal fusion have not shown significant advantages. In addition, no attention has been paid to whether fused images can be used for change detection. These two issues are addressed in this work. A new dataset consisting of nine pairs of images is designed to benchmark the accuracy of neural networks using one-pair spatiotemporal fusion with neural-network-based models. Notably, the size of each image is significantly larger compared to other datasets used to train neural networks. A comprehensive comparison of the radiometric, spectral, and structural losses is made using fourteen fusion algorithms and five datasets to illustrate the differences in the performance of spatiotemporal fusion algorithms with regard to various sensors and image sizes. A change detection experiment is conducted to test if it is feasible to detect changes in specific land covers using the fusion results. The experiment shows that convolutional neural networks can be used for one-pair spatiotemporal fusion if the sizes of individual images are adequately large. It also confirms that the spatiotemporally fused images can be used for change detection in certain scenes.

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Section: Introductionmentioning

confidence: 99%

An Experimental Study of the Accuracy and Change Detection Potential of Blending Time Series Remote Sensing Images with Spatiotemporal Fusion

et al. 2023

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Deep Learning-Based Spatiotemporal Fusion of Unmanned Aerial Vehicle and Satellite Reflectance Images for Crop Monitoring

et al. 2023

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Tree Species Classification over Cloudy Mountainous Regions by Spatiotemporal Fusion and Ensemble Classifier

Chen

et al. 2023

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Accurate mapping of tree species is critical for the sustainable development of the forestry industry. However, the lack of cloud-free optical images makes it challenging to map tree species accurately in cloudy mountainous regions. In order to improve tree species identification in this context, a classification method using spatiotemporal fusion and ensemble classifier is proposed. The applicability of three spatiotemporal fusion methods, i.e., the spatial and temporal adaptive reflectance fusion model (STARFM), the flexible spatiotemporal data fusion (FSDAF), and the spatial and temporal nonlocal filter-based fusion model (STNLFFM), in fusing MODIS and Landsat 8 images was investigated. The fusion results in Helong City show that the STNLFFM algorithm generated the best fused images. The correlation coefficients between the fusion images and actual Landsat images on May 28 and October 19 were 0.9746 and 0.9226, respectively, with an average of 0.9486. Dense Landsat-like time series at 8-day time intervals were generated using this method. This time series imagery and topography-derived features were used as predictor variables. Four machine learning methods, i.e., K-nearest neighbors (KNN), random forest (RF), artificial neural networks (ANNs), and light gradient boosting machine (LightGBM), were selected for tree species classification in Helong City, Jilin Province. An ensemble classifier combining these classifiers was constructed to further improve the accuracy. The ensemble classifier consistently achieved the highest accuracy in almost all classification scenarios, with a maximum overall accuracy improvement of approximately 3.4% compared to the best base classifier. Compared to only using a single temporal image, utilizing dense time series and the ensemble classifier can improve the classification accuracy by about 20%, and the overall accuracy reaches 84.32%. In conclusion, using spatiotemporal fusion and the ensemble classifier can significantly enhance tree species identification in cloudy mountainous areas with poor data availability.

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Dual-Branch Remote Sensing Spatiotemporal Fusion Network Based on Selection Kernel Mechanism

Cited by 3 publications

References 42 publications

An Experimental Study of the Accuracy and Change Detection Potential of Blending Time Series Remote Sensing Images with Spatiotemporal Fusion

An Experimental Study of the Accuracy and Change Detection Potential of Blending Time Series Remote Sensing Images with Spatiotemporal Fusion

Deep Learning-Based Spatiotemporal Fusion of Unmanned Aerial Vehicle and Satellite Reflectance Images for Crop Monitoring

Tree Species Classification over Cloudy Mountainous Regions by Spatiotemporal Fusion and Ensemble Classifier

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