Figure 1 - uploaded by Jiale Jiang
Content may be subject to copyright.
Contexts in source publication
Context 1
... main soil type is loam. We selected 35 winter wheat sample fields (Figure 1) that had all been sown via low mechanization in 2018. A series of 1 m × 1 m sample plots were selected in the center of these fields. ...
Context 2
... main soil type is loam. We selected 35 winter wheat sample fields (Figure 1) that had all been sown via low mechanization in 2018. A series of 1 m × 1 m sample plots were selected in the center of these fields. ...
Context 3
... Planet captures daily global images at high resolution, it can acquire data in tandem with Sentinel-2 on the same day and can further be used to implement image fusion between spatial and spectral dimensions. Correlation of VNIR between Planet and Sentinel-2 images is shown in Figure 10; these images show similar variation (R 2 = 0.90) when monitoring different objects via either satellite source even though the VNIR linear relationship between them is not close to y = x. This result suggests that it is not feasible to use Planet VNIR as a substitute for Sentinel-2 data when downscaling other bands via the SupReME algorithm because of the presence of different sensors with varied spectral responses. ...
Context 4
... Planet captures daily global images at high resolution, it can acquire data in tandem with Sentinel-2 on the same day and can further be used to implement image fusion between spatial and spectral dimensions. Correlation of VNIR between Planet and Sentinel-2 images is shown in Figure 10; these images show similar variation (R 2 = 0.90) when monitoring different objects via either satellite source even though the VNIR linear relationship between them is not close to y = x. This result suggests that it is not feasible to use Planet VNIR as a substitute for Sentinel-2 data when downscaling other bands via the SupReME algorithm because of the presence of different sensors with varied spectral responses. ...
Context 5
... main soil type is loam. We selected 35 winter wheat sample fields (Figure 1) that had all been sown via low mechanization in 2018. A series of 1 m × 1 m sample plots were selected in the center of these fields. ...
Context 6
... main soil type is loam. We selected 35 winter wheat sample fields (Figure 1) that had all been sown via low mechanization in 2018. A series of 1 m × 1 m sample plots were selected in the center of these fields. ...
Context 7
... Planet captures daily global images at high resolution, it can acquire data in tandem with Sentinel-2 on the same day and can further be used to implement image fusion between spatial and spectral dimensions. Correlation of VNIR between Planet and Sentinel-2 images is shown in Figure 10; these images show similar variation (R 2 = 0.90) when monitoring different objects via either satellite source even though the VNIR linear relationship between them is not close to y = x. This result suggests that it is not feasible to use Planet VNIR as a substitute for Sentinel-2 data when downscaling other bands via the SupReME algorithm because of the presence of different sensors with varied spectral responses. ...
Context 8
... Planet captures daily global images at high resolution, it can acquire data in tandem with Sentinel-2 on the same day and can further be used to implement image fusion between spatial and spectral dimensions. Correlation of VNIR between Planet and Sentinel-2 images is shown in Figure 10; these images show similar variation (R 2 = 0.90) when monitoring different objects via either satellite source even though the VNIR linear relationship between them is not close to y = x. This result suggests that it is not feasible to use Planet VNIR as a substitute for Sentinel-2 data when downscaling other bands via the SupReME algorithm because of the presence of different sensors with varied spectral responses. ...
Similar publications
The automatic segmentation of lesions from brain MR images is critical in diagnosing and treating diseases of the brain. Compared with laborious and time-consuming manual segmentation, computer-aided segmentation provides more efficient and reliable predictions. Recently, Deep Convolutional Neural Networks were proposed to show state-of-the-art per...
Building extraction from high spatial resolution remote sensing images is a hot spot in the field of remote sensing applications and computer vision. This paper presents a semantic segmentation model, which is a supervised method, named Pyramid Self-Attention Network (PISANet). Its structure is simple, because it contains only two parts: one is the...
The aim of this study is to determine firstly, the changes in the Haut-Sassandra classified forest during the decade of conflict and, secondly, the optimal spatial resolution for analysis of this forest. The dynamic of this forest were shown by the transition matrix processing on two satellite images, covering the forest with a resolution of 30 m....
We introduce a comprehensive method for space-borne 3D volumetric scattering-tomography of cloud micro-physics, developed for the CloudCT mission. The retrieved micro-physical properties are the liquid-water-content and effective droplet radius within a cloud. We include a model for a perspective polarization imager, and an assumption of 3D variati...