Cloud classification of ground-based infrared images combining manifold and texture features
Abstract. Automatic cloud type recognition of ground-based infrared
images is still a challenging task. A novel cloud classification method is
proposed to group images into five cloud types based on manifold and texture
features. Compared with statistical features in Euclidean space, manifold
features extracted on symmetric positive definite (SPD) matrix space can
describe the non-Euclidean geometric characteristics of the infrared image
more effectively. The proposed method comprises three stages: pre-processing,
feature extraction and classification. Cloud classification is performed by a
support vector machine (SVM). The datasets are comprised of the zenithal and
whole-sky images taken by the Whole-Sky Infrared Cloud-Measuring System
(WSIRCMS). Benefiting from the joint features, compared to the recent two
models of cloud type recognition, the experimental results illustrate that
the proposed method acquires a higher recognition rate with an increase of
2 %–10 % on the ground-based infrared datasets.