23,109 research outputs found
Semi-automatic detection and counting of oil palm trees from high spatial resolution airborne imagery
Plantation inventory and management require a range of fine-scale remote-sensing data. Remote-sensing images with high spatial and spectral resolution are an efficient source of such information. This article presents an approach to the extraction and counting of oil palm trees from high spatial resolution airborne imagery data. Counting oil palm trees is a crucial problem in specific agricultural areas, especially in Malaysia. The proposed scheme comprises six major parts: (1) discrimination of oil palms from non-oil palms using spectral analysis, (2) texture analysis, (3) edge enhancement, (4) segmentation process, (5) morphological analysis and (6) blob analysis. The average accuracy obtained was 95%, which indicates that high spatial resolution airborne imagery data with an appropriate assessment technique have the potential to provide us with vital information for oil palm plantation management. Information on the number of oil palm trees is crucial to the ability of plantation management to assess the value of the plantation and to monitor its production
Geo-Information Harvesting from Social Media Data
As unconventional sources of geo-information, massive imagery and text
messages from open platforms and social media form a temporally quasi-seamless,
spatially multi-perspective stream, but with unknown and diverse quality. Due
to its complementarity to remote sensing data, geo-information from these
sources offers promising perspectives, but harvesting is not trivial due to its
data characteristics. In this article, we address key aspects in the field,
including data availability, analysis-ready data preparation and data
management, geo-information extraction from social media text messages and
images, and the fusion of social media and remote sensing data. We then
showcase some exemplary geographic applications. In addition, we present the
first extensive discussion of ethical considerations of social media data in
the context of geo-information harvesting and geographic applications. With
this effort, we wish to stimulate curiosity and lay the groundwork for
researchers who intend to explore social media data for geo-applications. We
encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery
Thanks to recent advances in CNNs, solid improvements have been made in
semantic segmentation of high resolution remote sensing imagery. However, most
of the previous works have not fully taken into account the specific
difficulties that exist in remote sensing tasks. One of such difficulties is
that objects are small and crowded in remote sensing imagery. To tackle with
this challenging task we have proposed a novel architecture called local
feature extraction (LFE) module attached on top of dilated front-end module.
The LFE module is based on our findings that aggressively increasing dilation
factors fails to aggregate local features due to sparsity of the kernel, and
detrimental to small objects. The proposed LFE module solves this problem by
aggregating local features with decreasing dilation factor. We tested our
network on three remote sensing datasets and acquired remarkably good results
for all datasets especially for small objects
Airborne photogrammetry and LIDAR for DSM extraction and 3D change detection over an urban area : a comparative study
A digital surface model (DSM) extracted from stereoscopic aerial images, acquired in March 2000, is compared with a DSM derived from airborne light detection and ranging (lidar) data collected in July 2009. Three densely built-up study areas in the city centre of Ghent, Belgium, are selected, each covering approximately 0.4 km(2). The surface models, generated from the two different 3D acquisition methods, are compared qualitatively and quantitatively as to what extent they are suitable in modelling an urban environment, in particular for the 3D reconstruction of buildings. Then the data sets, which are acquired at two different epochs t(1) and t(2), are investigated as to what extent 3D (building) changes can be detected and modelled over the time interval. A difference model, generated by pixel-wise subtracting of both DSMs, indicates changes in elevation. Filters are proposed to differentiate 'real' building changes from false alarms provoked by model noise, outliers, vegetation, etc. A final 3D building change model maps all destructed and newly constructed buildings within the time interval t(2) - t(1). Based on the change model, the surface and volume of the building changes can be quantified
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