Cloud detection in satellite images is an important first-step in many remote sensing applications.
Ranked #2 on Semantic Segmentation on 38-Cloud
Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis.
Ranked #1 on Semantic Segmentation on 38-Cloud
Different empirical models have been developed for cloud detection.
Moreover, the method is able to obtain accurate pixel-level segmentation and classification results from a set of noisy labeled RGB color images.
In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications.
These satellites have different vantage points above the earth and different spectral imaging bands resulting in inconsistent imagery from one to another.
In addition, the training of the proposed adversarial domain adaptation model can be modified to improve the performance in a specific remote sensing application, such as cloud detection, by including a dedicated term in the cost function.
In the encoder, three input branches are designed to handle spectral bands at their native resolution and extract multiscale spectral features.