11,138 research outputs found
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These peculiarities lead to particular signal processing problems,
mainly characterized by indetermination and complex manifolds. The framework of
statistical learning has gained popularity in the last decade. New methods have
been presented to account for the spatial homogeneity of images, to include
user's interaction via active learning, to take advantage of the manifold
structure with semisupervised learning, to extract and encode invariances, or
to adapt classifiers and image representations to unseen yet similar scenes.
This tutuorial reviews the main advances for hyperspectral remote sensing image
classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201
Towards an automated monitoring of human settlements in South Africa using high resolution SPOT satellite imagery
Urban areas in sub-Saharan Africa are growing at an unprecedented pace. Much of this growth is taking place in informal settlements. In South Africa more than 10% of the population live in urban informal settlements. South Africa has established a National Informal Settlement Development Programme (NUSP) to respond to these challenges. This programme is designed to support the National Department of Human Settlement (NDHS) in its implementation of the Upgrading Informal Settlements Programme (UISP) with the objective of eventually upgrading all informal settlements in the country. Currently, the NDHS does not have access to an updated national dataset captured at the same scale using source data that can be used to understand the status of informal settlements in the country.
This pilot study is developing a fully automated workflow for the wall-to-wall processing of SPOT-5 satellite imagery of South Africa. The workflow includes an automatic image information extraction based on multiscale textural and morphological image features extraction. The advanced image feature compression and optimization together with innovative learning and classification techniques allow a processing of the SPOT-5 images using the Landsat-based National Land Cover (NLC) of South Africa from the year 2000 as low-resolution thematic reference layers as. The workflow was tested on 42 SPOT scenes based on a stratified sampling. The derived building information was validated against a visually interpreted building point data set and produced an accuracy of 97 per cent. Given this positive result, is planned to process the most recent wall-to-wall coverage as well as the archived imagery available since 2007 in the near future.JRC.G.2-Global security and crisis managemen
Rapid methods of landslide hazard mapping : Fiji case study
A landslide hazard probability map can help planners (1) prepare for, and/or mitigate against,
the effects of landsliding on communities and infrastructure, and (2) avoid or minimise the
risks associated with new developments. The aims of the project were to establish, by means
of studies in a few test areas, a generic method by which remote sensing and data analysis
using a geographic information system (GIS) could provide a provisional landslide hazard
zonation map. The provision of basic hazard information is an underpinning theme of the
UNâs International Decade for Natural Disaster Reduction (IDNDR). It is an essential
requirement for disaster preparedness and mitigation planning. This report forms part of BGS
project 92/7 (R5554) âRapid assessment of landslip hazardsâ Carried out under the ODA/BGS
Technology Development and Research Programme as part of the British Governmentâs
provision of aid to developing countries. It provides a detailed technical account of work
undertaken in a test area in Viti Levu in collaboration with Fiji Mineral Resources
Department. The study represents a demonstration of a methodology that is applicable to
many developing countries.
The underlying principle is that relationships between past landsliding events, interpreted
from remote sensing, and factors such as the geology, relief, soils etc provide the basis for
modelling where future landslides are most likely to occur. This is achieved using a GIS by
âweightingâ each class of each variable (e.g. each lithology âclassâ of the variable âgeologyâ)
according to the proportion of landslides occurring within it compared to the regional
average. Combinations of variables, produced by summing the weights in individual classes,
provide âmodelsâ of landslide probability. The approach is empirical but has the advantage
of potentially being able to provide regional scale hazard maps over large areas quickly and
cheaply; this is unlikely to be achieved using conventional ground-based geotechnical
methods.
In Fiji, landslides are usually triggered by intense rain storms commonly associated with
tropical cyclones. However, the regional distribution of landslides has not been mapped nor
is it known how far geology and landscape influence the location and severity of landsliding
events. The report discusses the remote sensing and GIS methodology, and describes the
results of the pilot study over an area of 713 km2 in south east Viti Levu. The landslide
model uses geology, elevation, slope angle, slope aspect, soil type, and forest cover as
inputs. The resulting provisional landslide hazard zonation map, divided into high, medium
and low zones of landslide hazard probability, suggests that whilst rainfall is the immediate
cause, others controls do exert a significant influence. It is recommended that consideration
be given in Fiji to implementing the techniques as part of a national strategic plan for
landslide hazard zonation mapping
Unmanned Aerial Vehicle (UAV) for monitoring soil erosion in Morocco
This article presents an environmental remote sensing application using a UAV that is specifically aimed at reducing the data gap between field scale and satellite scale in soil erosion monitoring in Morocco. A fixed-wing aircraft type Sirius I (MAVinci, Germany) equipped with a digital system camera (Panasonic) is employed. UAV surveys are conducted over different study sites with varying extents and flying heights in order to provide both very high resolution site-specific data and lower-resolution overviews, thus fully exploiting the large potential of the chosen UAV for multi-scale mapping purposes. Depending on the scale and area coverage, two different approaches for georeferencing are used, based on high-precision GCPs or the UAVâs log file with exterior orientation values respectively. The photogrammetric image processing enables the creation of Digital Terrain Models (DTMs) and ortho-image mosaics with very high resolution on a sub-decimetre level. The created data products were used for quantifying gully and badland erosion in 2D and 3D as well as for the analysis of the surrounding areas and landscape development for larger extents
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