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A Zero-Shot Sketch-Based Intermodal Object Retrieval Scheme for Remote Sensing Images
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DOI：10.1109/LGRS.2021.3056392.png)
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Abstract
En 中文
Domain-agnostic data retrieval has lately become essential amidst the availability of large-scale data from different types of sensors. However, the unavailability of a sufficient amount of samples of certain classes during training curtails the utility of existing retrieval models in remote sensing (RS) applications. Here, we propose a novel framework for zero-shot intermodal data retrieval of RS data. Thereupon, we design an encoder-decoder structure that ensures enhanced overlapping among the two data domains utilizing cross-triplet and cross-projection loss functions. Furthermore, we propose a sketch-based representation of the RS database Earth on Canvas with diverse classes. We perform a thorough benchmarking of this data set and demonstrate that the proposed framework outperforms state-of-the-art methods for zero-shot sketch-based retrieval framework for RS data.
Keywords:
Semantics
Training
Visualization
Task analysis
Standards
Sensors
Satellites
Cross-modal retrieval
database
earth on canvas (EoC)
information retrieval
remote sensing (RS)
sketches
zero-shot
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