3,642 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
Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review
Bark beetle outbreaks can result in a devastating impact on forest ecosystem
processes, biodiversity, forest structure and function, and economies. Accurate
and timely detection of bark beetle infestations is crucial to mitigate further
damage, develop proactive forest management activities, and minimize economic
losses. Incorporating remote sensing (RS) data with machine learning (ML) (or
deep learning (DL)) can provide a great alternative to the current approaches
that rely on aerial surveys and field surveys, which are impractical over vast
geographical regions. This paper provides a comprehensive review of past and
current advances in the early detection of bark beetle-induced tree mortality
from three key perspectives: bark beetle & host interactions, RS, and ML/DL. We
parse recent literature according to bark beetle species & attack phases, host
trees, study regions, imagery platforms & sensors, spectral/spatial/temporal
resolutions, spectral signatures, spectral vegetation indices (SVIs), ML
approaches, learning schemes, task categories, models, algorithms,
classes/clusters, features, and DL networks & architectures. This review
focuses on challenging early detection, discussing current challenges and
potential solutions. Our literature survey suggests that the performance of
current ML methods is limited (less than 80%) and depends on various factors,
including imagery sensors & resolutions, acquisition dates, and employed
features & algorithms/networks. A more promising result from DL networks and
then the random forest (RF) algorithm highlighted the potential to detect
subtle changes in visible, thermal, and short-wave infrared (SWIR) spectral
regions.Comment: Under review, 33 pages, 5 figures, 8 Table
Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing
International audience—Remote sensing is one of the most common ways to extract relevant information about the Earth and our environment. Remote sensing acquisitions can be done by both active (synthetic aperture radar, LiDAR) and passive (optical and thermal range, multispectral and hyperspectral) devices. According to the sensor, a variety of information about the Earth's surface can be obtained. The data acquired by these sensors can provide information about the structure (optical, synthetic aperture radar), elevation (LiDAR) and material content (multi and hyperspectral) of the objects in the image. Once considered together their comple-mentarity can be helpful for characterizing land use (urban analysis, precision agriculture), damage detection (e.g., in natural disasters such as floods, hurricanes, earthquakes, oil-spills in seas), and give insights to potential exploitation of resources (oil fields, minerals). In addition, repeated acquisitions of a scene at different times allows one to monitor natural resources and environmental variables (vegetation phenology, snow cover), anthropological effects (urban sprawl, deforestation), climate changes (desertification, coastal erosion) among others. In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation. This is done by leveraging the outcomes of the Data Fusion contests, organized by the IEEE Geoscience and Remote Sensing Society since 2006. We will report on the outcomes of these contests, presenting the multimodal sets of data made available to the community each year, the targeted applications and an analysis of the submitted methods and results: How was multimodality considered and integrated in the processing chain? What were the improvements/new opportunities offered by the fusion? What were the objectives to be addressed and the reported solutions? And from this, what will be the next challenges
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
Recurrent Multiresolution Convolutional Networks for VHR Image Classification
Classification of very high resolution (VHR) satellite images has three major
challenges: 1) inherent low intra-class and high inter-class spectral
similarities, 2) mismatching resolution of available bands, and 3) the need to
regularize noisy classification maps. Conventional methods have addressed these
challenges by adopting separate stages of image fusion, feature extraction, and
post-classification map regularization. These processing stages, however, are
not jointly optimizing the classification task at hand. In this study, we
propose a single-stage framework embedding the processing stages in a recurrent
multiresolution convolutional network trained in an end-to-end manner. The
feedforward version of the network, called FuseNet, aims to match the
resolution of the panchromatic and multispectral bands in a VHR image using
convolutional layers with corresponding downsampling and upsampling operations.
Contextual label information is incorporated into FuseNet by means of a
recurrent version called ReuseNet. We compared FuseNet and ReuseNet against the
use of separate processing steps for both image fusion, e.g. pansharpening and
resampling through interpolation, and map regularization such as conditional
random fields. We carried out our experiments on a land cover classification
task using a Worldview-03 image of Quezon City, Philippines and the ISPRS 2D
semantic labeling benchmark dataset of Vaihingen, Germany. FuseNet and ReuseNet
surpass the baseline approaches in both quantitative and qualitative results
Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture
L'abstract è presente nell'allegato / the abstract is in the attachmen
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