614 research outputs found
One-Bit Compressed Sensing by Greedy Algorithms
Sign truncated matching pursuit (STrMP) algorithm is presented in this paper.
STrMP is a new greedy algorithm for the recovery of sparse signals from the
sign measurement, which combines the principle of consistent reconstruction
with orthogonal matching pursuit (OMP). The main part of STrMP is as concise as
OMP and hence STrMP is simple to implement. In contrast to previous greedy
algorithms for one-bit compressed sensing, STrMP only need to solve a convex
and unconstraint subproblem at each iteration. Numerical experiments show that
STrMP is fast and accurate for one-bit compressed sensing compared with other
algorithms.Comment: 16 pages, 7 figure
Deep Intrinsic Decomposition with Adversarial Learning for Hyperspectral Image Classification
Convolutional neural networks (CNNs) have been demonstrated their powerful
ability to extract discriminative features for hyperspectral image
classification. However, general deep learning methods for CNNs ignore the
influence of complex environmental factor which enlarges the intra-class
variance and decreases the inter-class variance. This multiplies the difficulty
to extract discriminative features. To overcome this problem, this work
develops a novel deep intrinsic decomposition with adversarial learning, namely
AdverDecom, for hyperspectral image classification to mitigate the negative
impact of environmental factors on classification performance. First, we
develop a generative network for hyperspectral image (HyperNet) to extract the
environmental-related feature and category-related feature from the image.
Then, a discriminative network is constructed to distinguish different
environmental categories. Finally, a environmental and category joint learning
loss is developed for adversarial learning to make the deep model learn
discriminative features. Experiments are conducted over three commonly used
real-world datasets and the comparison results show the superiority of the
proposed method. The implementation of the proposed method and other compared
methods could be accessed at https://github.com/shendu-sw/Adversarial Learning
Intrinsic Decomposition for the sake of reproducibility.Comment: Submitted to IEEE TI
MultiScale Spectral-Spatial Convolutional Transformer for Hyperspectral Image Classification
Due to the powerful ability in capturing the global information, Transformer
has become an alternative architecture of CNNs for hyperspectral image
classification. However, general Transformer mainly considers the global
spectral information while ignores the multiscale spatial information of the
hyperspectral image. In this paper, we propose a multiscale spectral-spatial
convolutional Transformer (MultiscaleFormer) for hyperspectral image
classification. First, the developed method utilizes multiscale spatial patches
as tokens to formulate the spatial Transformer and generates multiscale spatial
representation of each band in each pixel. Second, the spatial representation
of all the bands in a given pixel are utilized as tokens to formulate the
spectral Transformer and generate the multiscale spectral-spatial
representation of each pixel. Besides, a modified spectral-spatial CAF module
is constructed in the MultiFormer to fuse cross-layer spectral and spatial
information. Therefore, the proposed MultiFormer can capture the multiscale
spectral-spatial information and provide better performance than most of other
architectures for hyperspectral image classification. Experiments are conducted
over commonly used real-world datasets and the comparison results show the
superiority of the proposed method.Comment: submitted to IEEE GRS
HyperDID: Hyperspectral Intrinsic Image Decomposition with Deep Feature Embedding
The dissection of hyperspectral images into intrinsic components through
hyperspectral intrinsic image decomposition (HIID) enhances the
interpretability of hyperspectral data, providing a foundation for more
accurate classification outcomes. However, the classification performance of
HIID is constrained by the model's representational ability. To address this
limitation, this study rethinks hyperspectral intrinsic image decomposition for
classification tasks by introducing deep feature embedding. The proposed
framework, HyperDID, incorporates the Environmental Feature Module (EFM) and
Categorical Feature Module (CFM) to extract intrinsic features. Additionally, a
Feature Discrimination Module (FDM) is introduced to separate
environment-related and category-related features. Experimental results across
three commonly used datasets validate the effectiveness of HyperDID in
improving hyperspectral image classification performance. This novel approach
holds promise for advancing the capabilities of hyperspectral image analysis by
leveraging deep feature embedding principles. The implementation of the
proposed method could be accessed soon at https://github.com/shendu-sw/HyperDID
for the sake of reproducibility.Comment: Submitted to IEEE TGR
Physics-Informed Deep Reversible Regression Model for Temperature Field Reconstruction of Heat-Source Systems
Temperature monitoring during the life time of heat source components in
engineering systems becomes essential to guarantee the normal work and the
working life of these components. However, prior methods, which mainly use the
interpolate estimation to reconstruct the temperature field from limited
monitoring points, require large amounts of temperature tensors for an accurate
estimation. This may decrease the availability and reliability of the system
and sharply increase the monitoring cost. To solve this problem, this work
develops a novel physics-informed deep reversible regression models for
temperature field reconstruction of heat-source systems (TFR-HSS), which can
better reconstruct the temperature field with limited monitoring points
unsupervisedly. First, we define the TFR-HSS task mathematically, and
numerically model the task, and hence transform the task as an image-to-image
regression problem. Then this work develops the deep reversible regression
model which can better learn the physical information, especially over the
boundary. Finally, considering the physical characteristics of heat conduction
as well as the boundary conditions, this work proposes the physics-informed
reconstruction loss including four training losses and jointly learns the deep
surrogate model with these losses unsupervisedly. Experimental studies have
conducted over typical two-dimensional heat-source systems to demonstrate the
effectiveness of the proposed method.Comment: Submitted to IEEE TI
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