X-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data.

ABSTRACT: This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global urban scenes through remote sensing imagery. However, their ability in identifying materials (pixel-wise classification) remains limited, due to the noisy collection environment and poor discriminative information as well as limited number of well-annotated training images. To this end, we propose a novel cross-modal deep-learning framework, called X-ModalNet, with three well-designed modules: self-adversarial module, interactive learning module, and label propagation module, b

SUBMITTER: Hong D 

PROVIDER: S-EPMC7453915 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature