85 research outputs found
SpectralDiff: A Generative Framework for Hyperspectral Image Classification with Diffusion Models
Hyperspectral Image (HSI) classification is an important issue in remote
sensing field with extensive applications in earth science. In recent years, a
large number of deep learning-based HSI classification methods have been
proposed. However, existing methods have limited ability to handle
high-dimensional, highly redundant, and complex data, making it challenging to
capture the spectral-spatial distributions of data and relationships between
samples. To address this issue, we propose a generative framework for HSI
classification with diffusion models (SpectralDiff) that effectively mines the
distribution information of high-dimensional and highly redundant data by
iteratively denoising and explicitly constructing the data generation process,
thus better reflecting the relationships between samples. The framework
consists of a spectral-spatial diffusion module, and an attention-based
classification module. The spectral-spatial diffusion module adopts forward and
reverse spectral-spatial diffusion processes to achieve adaptive construction
of sample relationships without requiring prior knowledge of graphical
structure or neighborhood information. It captures spectral-spatial
distribution and contextual information of objects in HSI and mines
unsupervised spectral-spatial diffusion features within the reverse diffusion
process. Finally, these features are fed into the attention-based
classification module for per-pixel classification. The diffusion features can
facilitate cross-sample perception via reconstruction distribution, leading to
improved classification performance. Experiments on three public HSI datasets
demonstrate that the proposed method can achieve better performance than
state-of-the-art methods. For the sake of reproducibility, the source code of
SpectralDiff will be publicly available at
https://github.com/chenning0115/SpectralDiff
Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation
Sliding-window based low-rank matrix approximation (LRMA) is a technique
widely used in hyperspectral images (HSIs) denoising or completion. However,
the uncertainty quantification of the restored HSI has not been addressed to
date. Accurate uncertainty quantification of the denoised HSI facilitates to
applications such as multi-source or multi-scale data fusion, data
assimilation, and product uncertainty quantification, since these applications
require an accurate approach to describe the statistical distributions of the
input data. Therefore, we propose a prior-free closed-form element-wise
uncertainty quantification method for LRMA-based HSI restoration. Our
closed-form algorithm overcomes the difficulty of the HSI patch mixing problem
caused by the sliding-window strategy used in the conventional LRMA process.
The proposed approach only requires the uncertainty of the observed HSI and
provides the uncertainty result relatively rapidly and with similar
computational complexity as the LRMA technique. We conduct extensive
experiments to validate the estimation accuracy of the proposed closed-form
uncertainty approach. The method is robust to at least 10% random impulse noise
at the cost of 10-20% of additional processing time compared to the LRMA. The
experiments indicate that the proposed closed-form uncertainty quantification
method is more applicable to real-world applications than the baseline Monte
Carlo test, which is computationally expensive. The code is available in the
attachment and will be released after the acceptance of this paper.Comment: Accepted for publication by IEEE Transactions on Geoscience and
Remote Sensing. IEEE Transactions on Geoscience and Remote Sensing (TGRS
A Bayesian Network Based Adaptability Design of Product Structures for Function Evolution
Structure adaptability design is critical for function evolution in product families, in which many structural and functional design factors are intertwined together with manufacturing cost, customer satisfaction, and final market sales. How to achieve a delicate balance among all of these factors to maximize the market performance of the product is too complicated to address based on traditional domain experts’ knowledge or some ad hoc heuristics. Here, we propose a quantitative product evolution design model that is based on Bayesian networks to model the dynamic relationship between customer needs and product structure design. In our model, all of the structural or functional features along with customer satisfaction, manufacturing cost, sale price, market sales, and indirect factors are modeled as random variables denoted as nodes in the Bayesian networks. The structure of the Bayesian model is then determined based on the historical data, which captures the dynamic sophisticated relationship of customer demands of a product, structural design, and market performance. Application of our approach to an electric toothbrush product family evolution design problem shows that our model allows for designers to interrogate with the model and obtain theoretical and decision support for dynamic product feature design process
Densify Your Labels: Unsupervised Clustering with Bipartite Matching for Weakly Supervised Point Cloud Segmentation
We propose a weakly supervised semantic segmentation method for point clouds
that predicts "per-point" labels from just "whole-scene" annotations while
achieving the performance of recent fully supervised approaches. Our core idea
is to propagate the scene-level labels to each point in the point cloud by
creating pseudo labels in a conservative way. Specifically, we over-segment
point cloud features via unsupervised clustering and associate scene-level
labels with clusters through bipartite matching, thus propagating scene labels
only to the most relevant clusters, leaving the rest to be guided solely via
unsupervised clustering. We empirically demonstrate that over-segmentation and
bipartite assignment plays a crucial role. We evaluate our method on ScanNet
and S3DIS datasets, outperforming state of the art, and demonstrate that we can
achieve results comparable to fully supervised methods.Comment: The first two authors contributed equally; Project website:
https://densify-your-labels.github.io
Laser-Induced Damage Initiation and Growth of Optical Materials
The lifetime of optical components is determined by the combination of laser-induced damage initiation probability and damage propagation rate during subsequent laser shots. This paper reviews both theoretical and experimental investigations on laser-induced damage initiation and growth at the surface of optics. The damage mechanism is generally considered as thermal absorption and electron avalanche, which play dominant roles for the different laser pulse durations. The typical damage morphology in the surface of components observed in experiments is also closely related to the damage mechanism. The damage crater in thermal absorption process, which can be estimated by thermal diffusion model, is typical distortion, melting, and ablation debris often with an elevated rim caused by melted material flow and resolidification. However, damage initiated by electron avalanche is often accompanied by generation of plasma, crush, and fracture, which can be explained by thermal explosion model. Damage growth at rear surface of components is extremely severe which can be explained by several models, such as fireball growth, impact crater, brittle fracture, and electric field enhancement. All the physical effects are not independent but mutually coupling. Developing theoretical models of multiphysics coupling are an important trend for future theoretical research. Meanwhile, more attention should be paid to integrated analysis both in theory and experiment
Absolute co-supplement and absolute co-coclosed modules
A module M is called an absolute co-coclosed (absolute co-supplement) module if whenever M ≅ T/X the submodule X of T is a coclosed (supplement) submodule of T. Rings for which all modules are absolute co-coclosed (absolute co-supplement) are precisely determined. We also investigate the rings whose (finitely generated) absolute co-supplement modules are projective. We show that a commutative domain R is a Dedekind domain if and only if every submodule of an absolute co-supplement R-module is absolute co-supplement. We also prove that the class Coclosed of all short exact sequences 0→A→B→C→0 such that A is a coclosed submodule of B is a proper class and every extension of an absolute co-coclosed module by an absolute co-coclosed module is absolute co-coclosed.Scientific and Technical Research Council of Turke
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