Abstract
Manually annotating remote sensing images is laborious work, especially on large-scale datasets. To improve the efficiency of this work, we propose an automatic annotation method for remote sensing images. The proposed method formulates the multi-label annotation task as a recommended problem, based on non-negative matrix tri-factorization (NMTF). The labels of remote sensing images can be recommended directly by recovering the image–label matrix. To learn more efficient latent feature matrices, two graph regularization terms are added to NMTF that explore the affiliated relationships on the image graph and label graph simultaneously. In order to reduce the gap between semantic concepts and visual content, both low-level visual features and high-level semantic features are exploited to construct the image graph. Meanwhile, label co-occurrence information is used to build the label graph, which discovers the semantic meaning to enhance the label prediction for unlabeled images. By employing the information from images and labels, the proposed method can efficiently deal with the sparsity and cold-start problem brought by limited image–label pairs. Experimental results on the UCMerced and Corel5k datasets show that our model outperforms most baseline algorithms for multi-label annotation of remote sensing images and performs efficiently on large-scale unlabeled datasets.
Highlights
Remote sensing image annotation is important in a wide range of remote sensing applications, such as environmental monitoring [1], remote sensing retrieval [2], and land use and land cover issues [3]
We propose weighted non-negative matrix tri-factorization combined with dual graphs of images and labels to utilize more information
We performed a comparison among Multi-label least squares (MLLS), Bi-Relation graph (BG), Multi-label ReliefF (MRF), Multiview-based multi-label propagation (MMP), and the proposed WDG-negative matrix tri-factorization (NMTF) method when the training dataset size was 20%
Summary
Remote sensing image annotation is important in a wide range of remote sensing applications, such as environmental monitoring [1], remote sensing retrieval [2], and land use and land cover issues [3]. With the continuous development of modern satellite technology, many terabytes of images are delivered by satellite sensors every day It is tedious and labor-intensive to manually annotate so many images. Rapidly developing remote sensing techniques are improving image resolution, which means that satellite images can provide more detailed geometrical information. This has completely changed the perspective of the traditional remote sensing image annotation task. Providing one image with more than one label (multi-label) can help to describe the image in more detail at the semantic level, which is useful in image retrieval and understanding This is a trend for remote sensing image applications. An effective and efficient automatic multi-label annotation method for remote sensing images is urgently needed by the remote sensing community
Sign-in/Register to view highlights & summary 
Published Version (
Free)
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 callDisclaimer: 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.
