Cross-Modal Feature Representation Learning and Label Graph Mining in a Residual Multi-Attentional CNN-LSTM Network for Multi-Label Aerial Scene Classification Article Swipe
Peng Li
,
Peng Chen
,
Dezheng Zhang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14102424
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/rs14102424
The results of aerial scene classification can provide valuable information for urban planning and land monitoring. In this specific field, there are always a number of object-level semantic classes in big remote-sensing pictures. Complex label-space makes it hard to detect all the targets and perceive corresponding semantics in the typical scene, thereby weakening the sensing ability. Even worse, the preparation of a labeled dataset for the training of deep networks is more difficult due to multiple labels. In order to mine object-level visual features and make good use of label dependency, we propose a novel framework in this article, namely a Cross-Modal Representation Learning and Label Graph Mining-based Residual Multi-Attentional CNN-LSTM framework (CM-GM framework). In this framework, a residual multi-attentional convolutional neural network is developed to extract object-level image features. Moreover, semantic labels are embedded by language model and then form a label graph which can be further mapped by advanced graph convolutional networks (GCN). With these cross-modal feature representations (image, graph and text), object-level visual features will be enhanced and aligned to GCN-based label embeddings. After that, aligned visual signals are fed into a bi-LSTM subnetwork according to the built label graph. The CM-GM framework is able to map both visual features and graph-based label representations into a correlated space appropriately, using label dependency efficiently, thus improving the LSTM predictor’s ability. Experimental results show that the proposed CM-GM framework is able to achieve higher accuracy on many multi-label benchmark datasets in remote sensing field.
Related Topics
Concepts
Computer science
Artificial intelligence
Graph
Convolutional neural network
Pattern recognition (psychology)
Residual
Feature learning
Modal
Semantics (computer science)
Subnetwork
Feature (linguistics)
Theoretical computer science
Algorithm
Programming language
Philosophy
Chemistry
Computer security
Linguistics
Polymer chemistry
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs14102424
- https://www.mdpi.com/2072-4292/14/10/2424/pdf?version=1652926719
- OA Status
- gold
- Cited By
- 16
- References
- 66
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4280597585
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4280597585Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/rs14102424Digital Object Identifier
- Title
-
Cross-Modal Feature Representation Learning and Label Graph Mining in a Residual Multi-Attentional CNN-LSTM Network for Multi-Label Aerial Scene ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-05-18Full publication date if available
- Authors
-
Peng Li, Peng Chen, Dezheng ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/rs14102424Publisher landing page
- PDF URL
-
https://www.mdpi.com/2072-4292/14/10/2424/pdf?version=1652926719Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2072-4292/14/10/2424/pdf?version=1652926719Direct OA link when available
- Concepts
-
Computer science, Artificial intelligence, Graph, Convolutional neural network, Pattern recognition (psychology), Residual, Feature learning, Modal, Semantics (computer science), Subnetwork, Feature (linguistics), Theoretical computer science, Algorithm, Programming language, Philosophy, Chemistry, Computer security, Linguistics, Polymer chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 6, 2023: 4, 2022: 2Per-year citation counts (last 5 years)
- References (count)
-
66Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(GCN). | 154 |
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