2,626 research outputs found
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
Aerial scene recognition is a fundamental task in remote sensing and has
recently received increased interest. While the visual information from
overhead images with powerful models and efficient algorithms yields
considerable performance on scene recognition, it still suffers from the
variation of ground objects, lighting conditions etc. Inspired by the
multi-channel perception theory in cognition science, in this paper, for
improving the performance on the aerial scene recognition, we explore a novel
audiovisual aerial scene recognition task using both images and sounds as
input. Based on an observation that some specific sound events are more likely
to be heard at a given geographic location, we propose to exploit the knowledge
from the sound events to improve the performance on the aerial scene
recognition. For this purpose, we have constructed a new dataset named AuDio
Visual Aerial sceNe reCognition datasEt (ADVANCE). With the help of this
dataset, we evaluate three proposed approaches for transferring the sound event
knowledge to the aerial scene recognition task in a multimodal learning
framework, and show the benefit of exploiting the audio information for the
aerial scene recognition. The source code is publicly available for
reproducibility purposes.Comment: ECCV 202
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest
In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference
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