17,645 research outputs found
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
Change detection (CD) is an important application of remote sensing, which
provides timely change information about large-scale Earth surface. With the
emergence of hyperspectral imagery, CD technology has been greatly promoted, as
hyperspectral data with the highspectral resolution are capable of detecting
finer changes than using the traditional multispectral imagery. Nevertheless,
the high dimension of hyperspectral data makes it difficult to implement
traditional CD algorithms. Besides, endmember abundance information at subpixel
level is often not fully utilized. In order to better handle high dimension
problem and explore abundance information, this paper presents a General
End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image
change detection (HSI-CD). The main contributions of this work are threefold:
1) Mixed-affinity matrix that integrates subpixel representation is introduced
to mine more cross-channel gradient features and fuse multi-source information;
2) 2-D CNN is designed to learn the discriminative features effectively from
multi-source data at a higher level and enhance the generalization ability of
the proposed CD algorithm; 3) A new HSI-CD data set is designed for the
objective comparison of different methods. Experimental results on real
hyperspectral data sets demonstrate the proposed method outperforms most of the
state-of-the-arts
An efficient methodology to simulate mixed spectral signatures of land covers through Field Radiometry data
An efficient methodology to simulate mixed spectral signatures of land covers, from endmember data, using linear statistical modelling based on the least squares estimation approach, is proposed. The optimal set of endmember has been obtained by measurements in situ with a field spectroradiometer GER 1500. Also, it is proposed the use of new sub-pixel methods based on statistics and certain “units of sampling” to apply to the landscapes. The resultant point estimations for these new units will be the “observations” and all of them will carry out an special role to simulate the final spectral signature. This methodology is used to simulate spectral signatures of a Mediterranean forest landscape near to Madrid (Spain). Furthermore the spectral signature model obtained through Field Radiometry data will be correlated with the image data of the same zone provided by the Landsat 7 Enhaced Thematic Mapper Plus (ETM+) sensor once corrected. The results obtained in correlation studies seem to conclude its efficiency. At the same time, the results open new research guidelines
A Neural Network Method for Efficient Vegetation Mapping
This paper describes the application of a neural network method designed to improve the efficiency of map production from remote sensing data. Specifically, the ARTMAP neural network produces vegetation maps of the Sierra National Forest, in Northern California, using Landsat Thematic Mapper (TM) data. In addition to spectral values, the data set includes terrain and location information for each pixel. The maps produced by ARTMAP are of comparable accuracy to maps produced by a currently used method, which requires expert knowledge of the area as well as extensive manual editing. In fact, once field observations of vegetation classes had been collected for selected sites, ARTMAP took only a few hours to accomplish a mapping task that had previously taken many months. The ARTMAP network features fast on-line learning, so the system can be updated incrementally when new field observations arrive, without the need for retraining on the entire data set. In addition to maps that identify lifeform and Calveg species, ARTMAP produces confidence maps, which indicate where errors are most likely to occur and which can, therefore, be used to guide map editing
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Imaging spectrometers measure electromagnetic energy scattered in their
instantaneous field view in hundreds or thousands of spectral channels with
higher spectral resolution than multispectral cameras. Imaging spectrometers
are therefore often referred to as hyperspectral cameras (HSCs). Higher
spectral resolution enables material identification via spectroscopic analysis,
which facilitates countless applications that require identifying materials in
scenarios unsuitable for classical spectroscopic analysis. Due to low spatial
resolution of HSCs, microscopic material mixing, and multiple scattering,
spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus,
accurate estimation requires unmixing. Pixels are assumed to be mixtures of a
few materials, called endmembers. Unmixing involves estimating all or some of:
the number of endmembers, their spectral signatures, and their abundances at
each pixel. Unmixing is a challenging, ill-posed inverse problem because of
model inaccuracies, observation noise, environmental conditions, endmember
variability, and data set size. Researchers have devised and investigated many
models searching for robust, stable, tractable, and accurate unmixing
algorithms. This paper presents an overview of unmixing methods from the time
of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models
are first discussed. Signal-subspace, geometrical, statistical, sparsity-based,
and spatial-contextual unmixing algorithms are described. Mathematical problems
and potential solutions are described. Algorithm characteristics are
illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of
Selected Topics in Applied Earth Observations and Remote Sensin
ART Neural Networks for Remote Sensing Image Analysis
ART and ARTMAP neural networks for adaptive recognition and prediction have been applied to a variety of problems, including automatic mapping from remote sensing satellite measurements, parts design retrieval at the Boeing Company, medical database prediction, and robot vision. This paper features a self-contained introduction to ART and ARTMAP dynamics. An application of these networks to image processing is illustrated by means of a remote sensing example. The basic ART and ARTMAP networks feature winner-take-all (WTA) competitive coding, which groups inputs into discrete recognition categories. WTA coding in these networks enables fast learning, which allows the network to encode important rare cases but which may lead to inefficient category proliferation with noisy training inputs. This problem is partially solved by ART-EMAP, which use WTA coding for learning but distributed category representations for test-set prediction. Recently developed ART models (dART and dARTMAP) retain stable coding, recognition, and prediction, but allow arbitrarily distributed category representation during learning as well as performance
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images
In hyperspectral remote sensing data mining, it is important to take into
account of both spectral and spatial information, such as the spectral
signature, texture feature and morphological property, to improve the
performances, e.g., the image classification accuracy. In a feature
representation point of view, a nature approach to handle this situation is to
concatenate the spectral and spatial features into a single but high
dimensional vector and then apply a certain dimension reduction technique
directly on that concatenated vector before feed it into the subsequent
classifier. However, multiple features from various domains definitely have
different physical meanings and statistical properties, and thus such
concatenation hasn't efficiently explore the complementary properties among
different features, which should benefit for boost the feature
discriminability. Furthermore, it is also difficult to interpret the
transformed results of the concatenated vector. Consequently, finding a
physically meaningful consensus low dimensional feature representation of
original multiple features is still a challenging task. In order to address the
these issues, we propose a novel feature learning framework, i.e., the
simultaneous spectral-spatial feature selection and extraction algorithm, for
hyperspectral images spectral-spatial feature representation and
classification. Specifically, the proposed method learns a latent low
dimensional subspace by projecting the spectral-spatial feature into a common
feature space, where the complementary information has been effectively
exploited, and simultaneously, only the most significant original features have
been transformed. Encouraging experimental results on three public available
hyperspectral remote sensing datasets confirm that our proposed method is
effective and efficient
Basic research planning in mathematical pattern recognition and image analysis
Fundamental problems encountered while attempting to develop automated techniques for applications of remote sensing are discussed under the following categories: (1) geometric and radiometric preprocessing; (2) spatial, spectral, temporal, syntactic, and ancillary digital image representation; (3) image partitioning, proportion estimation, and error models in object scene interference; (4) parallel processing and image data structures; and (5) continuing studies in polarization; computer architectures and parallel processing; and the applicability of "expert systems" to interactive analysis
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