Journal
REMOTE SENSING
Volume 9, Issue 2, Pages -Publisher
MDPI
DOI: 10.3390/rs9020109
Keywords
remote sensing images; image annotation; multi label classification; low rank representation; graph construction; semantic graph
Categories
Funding
- Natural Science Foundation of China [61402378]
- Natural Science Foundation of Chongqing Science and Technology Commission [cstc2014jcyjA40031, cstc2016jcyjA0351]
- Fundamental Research Funds for the Central Universities of China [2362015XK07, XDJK2016B009, XDJK2016E076]
- National Undergraduate Training Programs for Innovation and Entrepreneurship [201610635047]
- Southwest University Undergraduate Science and Technology Innovation Fund Project [20153601001]
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Annotating remote sensing images is a challenging task for its labor demanding annotation process and requirement of expert knowledge, especially when images can be annotated with multiple semantic concepts (or labels). To automatically annotate these multi-label images, we introduce an approach called Multi-Label Classification based on Low Rank Representation (MLC-LRR). MLC-LRR firstly utilizes low rank representation in the feature space of images to compute the low rank constrained coefficient matrix, then it adapts the coefficient matrix to define a feature-based graph and to capture the global relationships between images. Next, it utilizes low rank representation in the label space of labeled images to construct a semantic graph. Finally, these two graphs are exploited to train a graph-based multi-label classifier. To validate the performance of MLC-LRR against other related graph-based multi-label methods in annotating images, we conduct experiments on a public available multi-label remote sensing images (Land Cover). We perform additional experiments on five real-world multi-label image datasets to further investigate the performance of MLC-LRR. Empirical study demonstrates that MLC-LRR achieves better performance on annotating images than these comparing methods across various evaluation criteria; it also can effectively exploit global structure and label correlations of multi-label images.
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