Abstract

Effective feature extraction plays an important role in the classification of very high resolution (VHR) remote sensing (RS) images. Current researches mainly focus on individual shallow or deep feature extraction methods, remarkable representatives of which include Morphological Attribute Profiles (APs) and Convolutional Neural Networks (CNNs). Actually, to combine low-level and high-level features may take advantages of each approach and fully exploit the description capability. In this paper, APs and CNNs are integrated to characterize VHR RS images in order to improve the pixel description. Moreover, during the training of CNNs, regularization, dropout and fine-tuning strategies are all utilized to mitigate over-fitting problems due to insufficient samples in RS applications. Evaluations using QuickBird datasets demonstrate that our proposed method leads to a higher classification accuracy compared to individual method for VHR images.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.