123 research outputs found
GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection
High spectral resolution imagery of the Earth's surface enables users to
monitor changes over time in fine-grained scale, playing an increasingly
important role in agriculture, defense, and emergency response. However, most
current algorithms are still confined to describing local features and fail to
incorporate a global perspective, which limits their ability to capture
interactions between global features, thus usually resulting in incomplete
change regions. In this paper, we propose a Global Multi-head INteractive
self-attention change Detection network (GlobalMind) to explore the implicit
correlation between different surface objects and variant land cover
transformations, acquiring a comprehensive understanding of the data and
accurate change detection result. Firstly, a simple but effective Global Axial
Segmentation (GAS) strategy is designed to expand the self-attention
computation along the row space or column space of hyperspectral images,
allowing the global connection with high efficiency. Secondly, with GAS, the
global spatial multi-head interactive self-attention (Global-M) module is
crafted to mine the abundant spatial-spectral feature involving potential
correlations between the ground objects from the entire rich and complex
hyperspectral space. Moreover, to acquire the accurate and complete
cross-temporal changes, we devise a global temporal interactive multi-head
self-attention (GlobalD) module which incorporates the relevance and variation
of bi-temporal spatial-spectral features, deriving the integrate potential same
kind of changes in the local and global range with the combination of GAS. We
perform extensive experiments on five mostly used hyperspectral datasets, and
our method outperforms the state-of-the-art algorithms with high accuracy and
efficiency.Comment: 14 page, 18 figure
Denoising of Hyperspectral Images Using Group Low-Rank Representation
Hyperspectral images (HSIs) have been used in a
wide range of fields, such as agriculture, food safety, mineralogy
and environment monitoring, but being corrupted by various
kinds of noise limits its efficacy. Low-rank representation (LRR)
has proved its effectiveness in the denoising of HSIs. However,
it just employs local information for denoising, which results
in ineffectiveness when local noise is heavy. In this paper, we
propose an approach of group low-rank representation (GLRR)
for the HSI denoising. In our GLRR, a corrupted HSI is divided
into overlapping patches, the similar patches are combined into
a group, and the group is reconstructed as a whole using LRR.
The proposed method enables the exploitation of both the local
similarity within a patch and the nonlocal similarity across the
patches in a group simultaneously. The additional nonlocallysimilar
patches can bring in extra structural information to the
corrupted patches, facilitating the detection of noise as outliers.
LRR is applied to the group of patches, as the uncorrupted
patches enjoy intrinsic low-rank structure. The effectiveness of
the proposed GLRR method is demonstrated qualitatively and
quantitatively by using both simulated and real-world data in
experiments
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
Hyperspectral Image Analysis through Unsupervised Deep Learning
Hyperspectral image (HSI) analysis has become an active research area in computer vision field with a wide range of applications. However, in order to yield better recognition and analysis results, we need to address two challenging issues of HSI, i.e., the existence of mixed pixels and its significantly low spatial resolution (LR). In this dissertation, spectral unmixing (SU) and hyperspectral image super-resolution (HSI-SR) approaches are developed to address these two issues with advanced deep learning models in an unsupervised fashion. A specific application, anomaly detection, is also studied, to show the importance of SU.Although deep learning has achieved the state-of-the-art performance on supervised problems, its practice on unsupervised problems has not been fully developed. To address the problem of SU, an untied denoising autoencoder is proposed to decompose the HSI into endmembers and abundances with non-negative and abundance sum-to-one constraints. The denoising capacity is incorporated into the network with a sparsity constraint to boost the performance of endmember extraction and abundance estimation.Moreover, the first attempt is made to solve the problem of HSI-SR using an unsupervised encoder-decoder architecture by fusing the LR HSI with the high-resolution multispectral image (MSI). The architecture is composed of two encoder-decoder networks, coupled through a shared decoder, to preserve the rich spectral information from the HSI network. It encourages the representations from both modalities to follow a sparse Dirichlet distribution which naturally incorporates the two physical constraints of HSI and MSI. And the angular difference between representations are minimized to reduce the spectral distortion.Finally, a novel detection algorithm is proposed through spectral unmixing and dictionary based low-rank decomposition, where the dictionary is constructed with mean-shift clustering and the coefficients of the dictionary is encouraged to be low-rank. Experimental evaluations show significant improvement on the performance of anomaly detection conducted on the abundances (through SU).The effectiveness of the proposed approaches has been evaluated thoroughly by extensive experiments, to achieve the state-of-the-art results
Graph Laplacian for Image Anomaly Detection
Reed-Xiaoli detector (RXD) is recognized as the benchmark algorithm for image
anomaly detection; however, it presents known limitations, namely the
dependence over the image following a multivariate Gaussian model, the
estimation and inversion of a high-dimensional covariance matrix, and the
inability to effectively include spatial awareness in its evaluation. In this
work, a novel graph-based solution to the image anomaly detection problem is
proposed; leveraging the graph Fourier transform, we are able to overcome some
of RXD's limitations while reducing computational cost at the same time. Tests
over both hyperspectral and medical images, using both synthetic and real
anomalies, prove the proposed technique is able to obtain significant gains
over performance by other algorithms in the state of the art.Comment: Published in Machine Vision and Applications (Springer
Hydrocarbon quantification using neural networks and deep learning based hyperspectral unmixing
Hydrocarbon (HC) spills are a global issue, which can seriously impact human life and the environment, therefore early identification and remedial measures taken at an early stage are important. Thus, current research efforts aim at remotely quantifying incipient quantities of HC mixed with soils. The increased spectral and spatial resolution of hyperspectral sensors has opened ground-breaking perspectives in many industries including remote inspection of large areas and the environment. The use of subpixel detection algorithms, and in particular the use of the mixture models, has been identified as a future advance that needs to be incorporated in remote sensing. However, there are some challenging tasks since the spectral signatures of the targets of interest may not be immediately available. Moreover, real time processing and analysis is required to support fast decision-making. Progressing in this direction, this thesis pioneers and researches novel methodologies for HC quantification capable of exceeding the limitations of existing systems in terms of reduced cost and processing time with improved accuracy. Therefore the goal of this research is to develop, implement and test different methods for improving HC detection and quantification using spectral unmixing and machine learning. An efficient hybrid switch method employing neural networks and hyperspectral is proposed and investigated. This robust method switches between state of the art hyperspectral unmixing linear and nonlinear models, respectively. This procedure is well suited for the quantification of small quantities of substances within a pixel with high accuracy as the most appropriate model is employed. Central to the proposed approach is a novel method for extracting parameters to characterise the non-linearity of the data. These parameters are fed into a feedforward neural network which decides in a pixel by pixel fashion which model is more suitable. The quantification process is fully automated by applying further classification techniques to the acquired hyperspectral images. A deep learning neural network model is designed for the quantification of HC quantities mixed with soils. A three-term backpropagation algorithm with dropout is proposed to avoid overfitting and reduce the computational complexity of the model.
The above methods have been evaluated using classical repository datasets from the literature and a laboratory controlled dataset. For that, an experimental procedure has been designed to produce a labelled dataset. The data was obtained by mixing and homogenizing different soil types with HC substances, respectively and measuring the reflectance with a hyperspectral sensor.
Findings from the research study reveal that the two proposed models have high performance, they are suitable for the detection and quantification of HC mixed with soils, and surpass existing methods. Improvements in sensitivity, accuracy, computational time are achieved. Thus, the proposed approaches can be used to detect HC spills at an early stage in order to mitigate significant pollution from the spill areas
DEVELOPING INNOVATIVE SPECTRAL AND MACHINE LEARNING METHODS FOR MINERAL AND LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATASETS
The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived from Spectral Mapping Methods (SMMs), including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and SIDSAMtan using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski area (India). The SIDSAMtan outperforms SID and SAM in mineral mapping. A spectral similarity matrix of target and non-target classes based optimum threshold selection was developed to implement the SMMs successfully. Three new effective SMMs such as Dice Spectral Similarity Coefficient (DSSC), Kumar-Johnson Spectral Similarity Coefficient (KJSSC), and their hybrid, i.e., KJDSSCtan has been proposed, which outperforms the existing SMMs (i.e., SAM, SID, and SIDSAMtan) in spectral discrimination of spectrally similar minerals. The developed optimum threshold selection and proposed SMMs are recommended for accurate mineral mapping using hyperspectral data. An integrated spectral enhancement and ML methods have been developed to perform automated lithological classification using AVIRIS-NG hyperspectral data. The Support Vector Machine (SVM) outperforms the Random Forest (RF) and Linear Discriminant Analysis (LDA) in lithological classification. The performance of SVM also shows the least sensitivity to the number and uncertainty of training datasets. This study proposed a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks for accurate lithological classification using ML models. Different input features, such as (a) spectral, (b) spectral and transformed spectral, (c) spectral and morphological, (d) spectral and textural, and (e) optimum hybrid, were evaluated for lithological classification. The developed approach has been assessed in the Chattarpur area (India) consists of similar spectral characteristics and poorly exposed rocks, weathered, and partially vegetated terrain. The optimal hybrid input features outperform other input features to accurately classify different rock types using the SVM and RF models, which is ~15% higher than as obtained using spectral input features alone. The developed integrated approach of spectral enhancement and ML algorithms, and a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks, are recommended for accurate lithological classification. The developed methods can be effectively utilized in other remote sensing applications, such as vegetation/forest mapping and soil classification
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