Foreword to the Special Issue on Optical Multiangular Data Exploitation and Outcome of the 2011 GRSS Data Fusion Contest

doi:10.1109/jstars.2012.2186733

Cited by 23 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The contest promotes the development of methods for extracting geospatial information from large-scale, multisensor, multimodal, and multitemporal data. It aims to propose new problem settings that are challenging to address with existing techniques and to establish new benchmarks for scientific challenges in remote sensing image analysis [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19].…”

mentioning

confidence: 99%

Report on the 2022 IEEE Geoscience and Remote Sensing Society Data Fusion Contest: Semisupervised Learning [Technical Committees]

Hänsch¹,

Persello

Vivone

et al. 2022

IEEE Geosci. Remote Sens. Mag.

View full text Add to dashboard Cite

mentioning

confidence: 99%

Report on the 2022 IEEE Geoscience and Remote Sensing Society Data Fusion Contest: Semisupervised Learning [Technical Committees]

Hänsch¹,

Persello

Vivone

et al. 2022

IEEE Geosci. Remote Sens. Mag.

View full text Add to dashboard Cite

Weakly supervised alignment of image manifolds with semantic ties

Tuia

2014

2014 IEEE Geoscience and Remote Sensing Symposium

View full text Add to dashboard Cite

Aligning data distributions that underwent spectral distortions related to acquisition conditions is a key issue to improve the performance of classifiers applied to multi-temporal and multi-angular images. In this paper, we propose a feature extraction methodology, which aligns data manifolds based on their internal geometric structure and on a series of object correspondences highlighted on each image, or tie points. The weakly supervised manifold alignment (WeSMA) is a feature extractor that allows to define a common latent space, in which the images can be projected and processed by the same classifier. WeSMA relaxes the need for labeled pixels in all acquisitions of previous manifold alignment methods, an heavy constraint for remote sensing applications. Experiments on a set of World-View II images acquired at different viewing angles show the interest of the method that can compensate the spectral shift generated by the angular distortion without labels issued from the off-nadir image.

show abstract

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

Mura

Prasad

Pacifici³

et al. 2015

Proc. IEEE

200

View full text Add to dashboard Cite

International audience—Remote sensing is one of the most common ways to extract relevant information about the Earth and our environment. Remote sensing acquisitions can be done by both active (synthetic aperture radar, LiDAR) and passive (optical and thermal range, multispectral and hyperspectral) devices. According to the sensor, a variety of information about the Earth's surface can be obtained. The data acquired by these sensors can provide information about the structure (optical, synthetic aperture radar), elevation (LiDAR) and material content (multi and hyperspectral) of the objects in the image. Once considered together their comple-mentarity can be helpful for characterizing land use (urban analysis, precision agriculture), damage detection (e.g., in natural disasters such as floods, hurricanes, earthquakes, oil-spills in seas), and give insights to potential exploitation of resources (oil fields, minerals). In addition, repeated acquisitions of a scene at different times allows one to monitor natural resources and environmental variables (vegetation phenology, snow cover), anthropological effects (urban sprawl, deforestation), climate changes (desertification, coastal erosion) among others. In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation. This is done by leveraging the outcomes of the Data Fusion contests, organized by the IEEE Geoscience and Remote Sensing Society since 2006. We will report on the outcomes of these contests, presenting the multimodal sets of data made available to the community each year, the targeted applications and an analysis of the submitted methods and results: How was multimodality considered and integrated in the processing chain? What were the improvements/new opportunities offered by the fusion? What were the objectives to be addressed and the reported solutions? And from this, what will be the next challenges

show abstract

Foreword to the Special Issue on Optical Multiangular Data Exploitation and Outcome of the 2011 GRSS Data Fusion Contest

Cited by 23 publications

References 44 publications

Report on the 2022 IEEE Geoscience and Remote Sensing Society Data Fusion Contest: Semisupervised Learning [Technical Committees]

Report on the 2022 IEEE Geoscience and Remote Sensing Society Data Fusion Contest: Semisupervised Learning [Technical Committees]

Weakly supervised alignment of image manifolds with semantic ties

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

Contact Info

Product

Resources

About