692 research outputs found
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
Hyperspectral colon tissue cell classification
A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Techniques for the extraction of spatial and spectral information in the supervised classification of hyperspectral imagery for land-cover applications
The objective of this PhD thesis is the development of spatialspectral
information extraction techniques for supervised
classification tasks, both by means of classical models and
those based on deep learning, to be used in the classification
of land use or land cover (LULC) multi- and hyper-spectral
images obtained by remote sensing. The main goal is the
efficient application of these techniques, so that they are able
to obtain satisfactory classification results with a low use of
computational resources and low execution time
ES2Net: An Efficient Spectral-Spatial Network for Hyperspectral Image Change Detection
Hyperspectral image change detection (HSI-CD) aims to identify the
differences in bitemporal HSIs. To mitigate spectral redundancy and improve the
discriminativeness of changing features, some methods introduced band selection
technology to select bands conducive for CD. However, these methods are limited
by the inability to end-to-end training with the deep learning-based feature
extractor and lack considering the complex nonlinear relationship among bands.
In this paper, we propose an end-to-end efficient spectral-spatial change
detection network (ES2Net) to address these issues. Specifically, we devised a
learnable band selection module to automatically select bands conducive to CD.
It can be jointly optimized with a feature extraction network and capture the
complex nonlinear relationships among bands. Moreover, considering the large
spatial feature distribution differences among different bands, we design the
cluster-wise spatial attention mechanism that assigns a spatial attention
factor to each individual band to individually improve the feature
discriminativeness for each band. Experiments on three widely used HSI-CD
datasets demonstrate the effectiveness and superiority of this method compared
with other state-of-the-art methods
Classification of Hyperspectral and LiDAR Data Using Coupled CNNs
In this paper, we propose an efficient and effective framework to fuse
hyperspectral and Light Detection And Ranging (LiDAR) data using two coupled
convolutional neural networks (CNNs). One CNN is designed to learn
spectral-spatial features from hyperspectral data, and the other one is used to
capture the elevation information from LiDAR data. Both of them consist of
three convolutional layers, and the last two convolutional layers are coupled
together via a parameter sharing strategy. In the fusion phase, feature-level
and decision-level fusion methods are simultaneously used to integrate these
heterogeneous features sufficiently. For the feature-level fusion, three
different fusion strategies are evaluated, including the concatenation
strategy, the maximization strategy, and the summation strategy. For the
decision-level fusion, a weighted summation strategy is adopted, where the
weights are determined by the classification accuracy of each output. The
proposed model is evaluated on an urban data set acquired over Houston, USA,
and a rural one captured over Trento, Italy. On the Houston data, our model can
achieve a new record overall accuracy of 96.03%. On the Trento data, it
achieves an overall accuracy of 99.12%. These results sufficiently certify the
effectiveness of our proposed model
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
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