Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion

Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster assessment... (read more)

PDF Abstract

Datasets


Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK BENCHMARK
Cloud Removal SEN12MS-CR DSen2-CR MAE 0.029 # 1
RMSE 0.036 # 1
PSNR 28.7 # 1
SAM 8.15 # 1
SSIM 0.875 # 1

Methods used in the Paper


METHOD TYPE
ReLU
Activation Functions
Batch Normalization
Normalization
Residual Connection
Skip Connections
Residual Block
Skip Connection Blocks
Convolution
Convolutions
ResNet
Convolutional Neural Networks