Cloud Detection From RGB Color Remote Sensing Images With Deep Pyramid Networks
This addresses a critical pre-processing step for remote sensing applications, but it is incremental as it adapts existing deep learning methods to a specific domain problem.
The paper tackles cloud detection in RGB remote sensing images by adapting a deep pyramid network with a pre-trained encoder, achieving accurate pixel-level segmentation and classification from noisy labeled data. The method outperforms baselines on a new dataset from Gokturk-2 and RASAT satellites, including challenging cases like snowy mountains.
Cloud detection from remotely observed data is a critical pre-processing step for various remote sensing applications. In particular, this problem becomes even harder for RGB color images, since there is no distinct spectral pattern for clouds, which is directly separable from the Earth surface. In this paper, we adapt a deep pyramid network (DPN) to tackle this problem. For this purpose, the network is enhanced with a pre-trained parameter model at the encoder layer. Moreover, the method is able to obtain accurate pixel-level segmentation and classification results from a set of noisy labeled RGB color images. In order to demonstrate the superiority of the method, we collect and label data with the corresponding cloud/non-cloudy masks acquired from low-orbit Gokturk-2 and RASAT satellites. The experimental results validates that the proposed method outperforms several baselines even for hard cases (e.g. snowy mountains) that are perceptually difficult to distinguish by human eyes.