7,267 research outputs found
Application of Multifractal Analysis to Segmentation of Water Bodies in Optical and Synthetic Aperture Radar Satellite Images
A method for segmenting water bodies in optical and synthetic aperture radar
(SAR) satellite images is proposed. It makes use of the textural features of
the different regions in the image for segmentation. The method consists in a
multiscale analysis of the images, which allows us to study the images
regularity both, locally and globally. As results of the analysis, coarse
multifractal spectra of studied images and a group of images that associates
each position (pixel) with its corresponding value of local regularity (or
singularity) spectrum are obtained. Thresholds are then applied to the
multifractal spectra of the images for the classification. These thresholds are
selected after studying the characteristics of the spectra under the assumption
that water bodies have larger local regularity than other soil types.
Classifications obtained by the multifractal method are compared quantitatively
with those obtained by neural networks trained to classify the pixels of the
images in covered against uncovered by water. In optical images, the
classifications are also compared with those derived using the so-called
Normalized Differential Water Index (NDWI)
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
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
We aim at segmenting small organs (e.g., the pancreas) from abdominal CT
scans. As the target often occupies a relatively small region in the input
image, deep neural networks can be easily confused by the complex and variable
background. To alleviate this, researchers proposed a coarse-to-fine approach,
which used prediction from the first (coarse) stage to indicate a smaller input
region for the second (fine) stage. Despite its effectiveness, this algorithm
dealt with two stages individually, which lacked optimizing a global energy
function, and limited its ability to incorporate multi-stage visual cues.
Missing contextual information led to unsatisfying convergence in iterations,
and that the fine stage sometimes produced even lower segmentation accuracy
than the coarse stage.
This paper presents a Recurrent Saliency Transformation Network. The key
innovation is a saliency transformation module, which repeatedly converts the
segmentation probability map from the previous iteration as spatial weights and
applies these weights to the current iteration. This brings us two-fold
benefits. In training, it allows joint optimization over the deep networks
dealing with different input scales. In testing, it propagates multi-stage
visual information throughout iterations to improve segmentation accuracy.
Experiments in the NIH pancreas segmentation dataset demonstrate the
state-of-the-art accuracy, which outperforms the previous best by an average of
over 2%. Much higher accuracies are also reported on several small organs in a
larger dataset collected by ourselves. In addition, our approach enjoys better
convergence properties, making it more efficient and reliable in practice.Comment: Accepted to CVPR 2018 (10 pages, 6 figures
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