Visible to the public Object Classification of Remote Sensing Images Based on Optimized Projection Supervised Discrete Hashing

TitleObject Classification of Remote Sensing Images Based on Optimized Projection Supervised Discrete Hashing
Publication TypeConference Paper
Year of Publication2021
AuthorsZhang, QianQian, Liu, Yazhou, Sun, Quansen
Conference Name2020 25th International Conference on Pattern Recognition (ICPR)
Date Publishedjan
KeywordsClassification algorithms, compositionality, hash algorithms, Hash functions, image processing, Learning systems, Memory management, object classification, optimized projection, Pattern recognition, pubcrawl, remote sensing, resilience, Resiliency, supervised discrete hashing, Task Analysis
AbstractRecently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications.
Citation Keyzhang_object_2021