1 research outputs found
Super-pixel cloud detection using Hierarchical Fusion CNN
Cloud detection plays a very important role in the process of remote sensing
images. This paper designs a super-pixel level cloud detection method based on
convolutional neural network (CNN) and deep forest. Firstly, remote sensing
images are segmented into super-pixels through the combination of SLIC and
SEEDS. Structured forests is carried out to compute edge probability of each
pixel, based on which super-pixels are segmented more precisely. Segmented
super-pixels compose a super-pixel level remote sensing database. Though cloud
detection is essentially a binary classification problem, our database is
labeled into four categories: thick cloud, cirrus cloud, building and other
culture, to improve the generalization ability of our proposed models.
Secondly, super-pixel level database is used to train our cloud detection
models based on CNN and deep forest. Considering super-pixel level remote
sensing images contain less semantic information compared with general object
classification database, we propose a Hierarchical Fusion CNN (HFCNN). It takes
full advantage of low-level features like color and texture information and is
more applicable to cloud detection task. In test phase, every super-pixel in
remote sensing images is classified by our proposed models and then combined to
recover final binary mask by our proposed distance metric, which is used to
determine ambiguous super-pixels. Experimental results show that, compared with
conventional methods, HFCNN can achieve better precision and recall