9,394 research outputs found
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
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
An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data
Feature selection has been studied widely in the literature. However, the
efficacy of the selection criteria for low sample size applications is
neglected in most cases. Most of the existing feature selection criteria are
based on the sample similarity. However, the distance measures become
insignificant for high dimensional low sample size (HDLSS) data. Moreover, the
variance of a feature with a few samples is pointless unless it represents the
data distribution efficiently. Instead of looking at the samples in groups, we
evaluate their efficiency based on pairwise fashion. In our investigation, we
noticed that considering a pair of samples at a time and selecting the features
that bring them closer or put them far away is a better choice for feature
selection. Experimental results on benchmark data sets demonstrate the
effectiveness of the proposed method with low sample size, which outperforms
many other state-of-the-art feature selection methods.Comment: European Signal Processing Conference 201
Aggregated Deep Local Features for Remote Sensing Image Retrieval
Remote Sensing Image Retrieval remains a challenging topic due to the special
nature of Remote Sensing Imagery. Such images contain various different
semantic objects, which clearly complicates the retrieval task. In this paper,
we present an image retrieval pipeline that uses attentive, local convolutional
features and aggregates them using the Vector of Locally Aggregated Descriptors
(VLAD) to produce a global descriptor. We study various system parameters such
as the multiplicative and additive attention mechanisms and descriptor
dimensionality. We propose a query expansion method that requires no external
inputs. Experiments demonstrate that even without training, the local
convolutional features and global representation outperform other systems.
After system tuning, we can achieve state-of-the-art or competitive results.
Furthermore, we observe that our query expansion method increases overall
system performance by about 3%, using only the top-three retrieved images.
Finally, we show how dimensionality reduction produces compact descriptors with
increased retrieval performance and fast retrieval computation times, e.g. 50%
faster than the current systems.Comment: Published in Remote Sensing. The first two authors have equal
contributio
Detecting animals in African Savanna with UAVs and the crowds
Unmanned aerial vehicles (UAVs) offer new opportunities for wildlife
monitoring, with several advantages over traditional field-based methods. They
have readily been used to count birds, marine mammals and large herbivores in
different environments, tasks which are routinely performed through manual
counting in large collections of images. In this paper, we propose a
semi-automatic system able to detect large mammals in semi-arid Savanna. It
relies on an animal-detection system based on machine learning, trained with
crowd-sourced annotations provided by volunteers who manually interpreted
sub-decimeter resolution color images. The system achieves a high recall rate
and a human operator can then eliminate false detections with limited effort.
Our system provides good perspectives for the development of data-driven
management practices in wildlife conservation. It shows that the detection of
large mammals in semi-arid Savanna can be approached by processing data
provided by standard RGB cameras mounted on affordable fixed wings UAVs
Temporal optimisation of image acquisition for land cover classification with random forest and MODIS time-series
The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8–10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results
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