16,811 research outputs found
High-resolution optical and SAR image fusion for building database updating
This paper addresses the issue of cartographic database (DB) creation or updating using high-resolution synthetic aperture radar and optical images. In cartographic applications, objects of interest are mainly buildings and roads. This paper proposes a processing chain to create or update building DBs. The approach is composed of two steps. First, if a DB is available, the presence of each DB object is checked in the images. Then, we verify if objects coming from an image segmentation should be included in the DB. To do those two steps, relevant features are extracted from images in the neighborhood of the considered object. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of DempsterâShafer evidence theory
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
The agricultural impact of the 2015â2016 floods in Ireland as mapped through Sentinel 1 satellite imagery
peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1
The agricultural impact of the 2015â2016 floods in Ireland as mapped through Sentinel 1 satellite imagery
R. OâHaraemail
, S. Green
and T. McCarthy
DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019
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Abstract
The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015â2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the floodâs depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include highâtemporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales
Basin scale assessment of landslides geomorphological setting by advanced InSAR analysis
An extensive investigation of more than 90 landslides affecting a small river basin in Central
Italy was performed by combining field surveys and remote sensing techniques. We thus defined the
geomorphological setting of slope instability processes. Basic information, such as landslides mapping
and landslides type definition, have been acquired thanks to geomorphological field investigations
and multi-temporal aerial photos interpretation, while satellite SAR archive data (acquired by ERS
and Envisat from 1992 to 2010) have been analyzed by means of A-DInSAR (Advanced Differential
Interferometric Synthetic Aperture Radar) techniques to evaluate landslides past displacements
patterns. Multi-temporal assessment of landslides state of activity has been performed basing
on geomorphological evidence criteria and past ground displacement measurements obtained by
A-DInSAR. This step has been performed by means of an activity matrix derived from information
achieved thanks to double orbital geometry. Thanks to this approach we also achieved more detailed
knowledge about the landslides kinematics in time and space
Patterns of past and recent conversion of indigenous grasslands in the South Island, New Zealand
We used recent satellite imagery to quantify the extent, type, and rate of conversion of remaining indigenous grasslands in the inland eastern South Island of New Zealand in recent years. We describe the pattern of conversion in relation to national classifications of land use capability and land environments, and ecological and administrative districts and regions. We show that although large areas of indigenous grasslands remain, grassland loss has been ongoing. Indigenous grassland was reduced in the study area by 3% (70 200 ha) between 1990 and 2008. Almost two-thirds of post-1990 conversion occurred in threatened environments with less than 30% of indigenous cover remaining, primarily in the Waitaki, Mackenzie and Central Otago administrative districts. This conversion occurred primarily on non-arable land. In the Mackenzie and Waitaki districts the rate of conversion in 2001-2008 was approximately twice that in 1990-2001. Opportunities to protect more of the full range of indigenous grasslands lie with the continuing tenure review process in these districts
Semantic Cross-View Matching
Matching cross-view images is challenging because the appearance and
viewpoints are significantly different. While low-level features based on
gradient orientations or filter responses can drastically vary with such
changes in viewpoint, semantic information of images however shows an invariant
characteristic in this respect. Consequently, semantically labeled regions can
be used for performing cross-view matching. In this paper, we therefore explore
this idea and propose an automatic method for detecting and representing the
semantic information of an RGB image with the goal of performing cross-view
matching with a (non-RGB) geographic information system (GIS). A segmented
image forms the input to our system with segments assigned to semantic concepts
such as traffic signs, lakes, roads, foliage, etc. We design a descriptor to
robustly capture both, the presence of semantic concepts and the spatial layout
of those segments. Pairwise distances between the descriptors extracted from
the GIS map and the query image are then used to generate a shortlist of the
most promising locations with similar semantic concepts in a consistent spatial
layout. An experimental evaluation with challenging query images and a large
urban area shows promising results
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