17,470 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
Learning Aerial Image Segmentation from Online Maps
This study deals with semantic segmentation of high-resolution (aerial)
images where a semantic class label is assigned to each pixel via supervised
classification as a basis for automatic map generation. Recently, deep
convolutional neural networks (CNNs) have shown impressive performance and have
quickly become the de-facto standard for semantic segmentation, with the added
benefit that task-specific feature design is no longer necessary. However, a
major downside of deep learning methods is that they are extremely data-hungry,
thus aggravating the perennial bottleneck of supervised classification, to
obtain enough annotated training data. On the other hand, it has been observed
that they are rather robust against noise in the training labels. This opens up
the intriguing possibility to avoid annotating huge amounts of training data,
and instead train the classifier from existing legacy data or crowd-sourced
maps which can exhibit high levels of noise. The question addressed in this
paper is: can training with large-scale, publicly available labels replace a
substantial part of the manual labeling effort and still achieve sufficient
performance? Such data will inevitably contain a significant portion of errors,
but in return virtually unlimited quantities of it are available in larger
parts of the world. We adapt a state-of-the-art CNN architecture for semantic
segmentation of buildings and roads in aerial images, and compare its
performance when using different training data sets, ranging from manually
labeled, pixel-accurate ground truth of the same city to automatic training
data derived from OpenStreetMap data from distant locations. We report our
results that indicate that satisfying performance can be obtained with
significantly less manual annotation effort, by exploiting noisy large-scale
training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
Detection and segmentation of objects in overheard imagery is a challenging
task. The variable density, random orientation, small size, and
instance-to-instance heterogeneity of objects in overhead imagery calls for
approaches distinct from existing models designed for natural scene datasets.
Though new overhead imagery datasets are being developed, they almost
universally comprise a single view taken from directly overhead ("at nadir"),
failing to address a critical variable: look angle. By contrast, views vary in
real-world overhead imagery, particularly in dynamic scenarios such as natural
disasters where first looks are often over 40 degrees off-nadir. This
represents an important challenge to computer vision methods, as changing view
angle adds distortions, alters resolution, and changes lighting. At present,
the impact of these perturbations for algorithmic detection and segmentation of
objects is untested. To address this problem, we present an open source
Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks
from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of
these images cover the same 665 square km geographic extent and are annotated
with 126,747 building footprint labels, enabling direct assessment of the
impact of viewpoint perturbation on model performance. We benchmark multiple
leading segmentation and object detection models on: (1) building detection,
(2) generalization to unseen viewing angles and resolutions, and (3)
sensitivity of building footprint extraction to changes in resolution. We find
that state of the art segmentation and object detection models struggle to
identify buildings in off-nadir imagery and generalize poorly to unseen views,
presenting an important benchmark to explore the broadly relevant challenge of
detecting small, heterogeneous target objects in visually dynamic contexts.Comment: Accepted into IEEE International Conference on Computer Vision (ICCV)
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