Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 58, Issue 7, Pages 4558-4572Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2963364
Keywords
Feature extraction; Semantics; Cognition; Correlation; Remote sensing; Task analysis; Soil; Attentional region extraction; convolutional neural network (CNN); high-resolution aerial image; label relational reasoning; multilabel classification
Categories
Funding
- China Scholarship Council
- European Research Council (ERC) through the European Union's Horizon 2020 Research and Innovation Program, So2Sat [ERC-2016-StG-714087]
- Helmholtz Association [VH-NG-1018]
- Helmholtz Artificial Intelligence Cooperation Unit (HAICU)-Local Unit Munich Unit at Aeronautics, Space and Transport (MASTr)
- Helmholtz Excellent Professorship Data Science in Earth Observation-Big Data Fusion for Urban Research
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Multilabel classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of them deploy few efforts in exploiting label relations, while such dependencies are crucial for making accurate predictions. Although an long short term memory (LSTM) layer can be introduced to modeling such label dependencies in a chain propagation manner, the efficiency might be questioned when certain labels are improperly inferred. To address this, we propose a novel aerial image multilabel classification network, attention-aware label relational reasoning network. Particularly, our network consists of three elemental modules: 1) a label-wise feature parcel learning module; 2) an attentional region extraction module; and 3) a label relational inference module. To be more specific, the label-wise feature parcel learning module is designed for extracting high-level label-specific features. The attentional region extraction module aims at localizing discriminative regions in these features without region proposal generation, yielding attentional label-specific features. The label relational inference module finally predicts label existences using label relations reasoned from outputs of the previous module. The proposed network is characterized by its capacities of extracting discriminative label-wise features and reasoning about label relations naturally and interpretably. In our experiments, we evaluate the proposed model on two multilabel aerial image data sets, of which one is newly produced. Quantitative and qualitative results on these two data sets demonstrate the effectiveness of our model. To facilitate progress in the multilabel aerial image classification, our produced data set will be made publicly available.
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