Gated Autoencoder Network for Spectral–Spatial Hyperspectral Unmixing

Convolution-based autoencoder networks have yielded promising performances in exploiting spatial–contextual signatures for spectral unmixing. However, the extracted spectral and spatial features of some networks are aggregated, which makes it difficult to balance their effects on unmixing results. In this paper, we propose two gated autoencoder networks with the intention of adaptively controlling the contribution of spectral and spatial features in unmixing process. Gating mechanism is adopted in the networks to filter and regularize spatial features to construct an unmixing algorithm based on spectral information and supplemented by spatial information. In addition, abundance sparsity regularization and gating regularization are introduced to ensure the appropriate implementation. Experimental results validate the superiority of the proposed method to the state-of-the-art techniques in both synthetic and real-world scenes. This study confirms the effectiveness of gating mechanism in improving the accuracy and efficiency of utilizing spatial signatures for spectral unmixing.

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A Graph Regularized Multilinear Mixing Model for Nonlinear Hyperspectral Unmixing

Remote Sensing ◽

10.3390/rs11192188 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2188

Author(s):

Li ◽

Zhu ◽

Guo ◽

Chen

Keyword(s):

Real World ◽

Optimization Problem ◽

Spatial Information ◽

Mixing Model ◽

Spectral Unmixing ◽

Hyperspectral Images ◽

Hyperspectral Unmixing ◽

Manifold Structure ◽

Underlying Manifold

Spectral unmixing of hyperspectral images is an important issue in the fields of remotesensing. Jointly exploring the spectral and spatial information embedded in the data is helpful toenhance the consistency between mixing/unmixing models and real scenarios. This paper proposesa graph regularized nonlinear unmixing method based on the recent multilinear mixing model(MLM). The MLM takes account of all orders of interactions between endmembers, and indicates thepixel-wise nonlinearity with a single probability parameter. By incorporating the Laplacian graphregularizers, the proposed method exploits the underlying manifold structure of the pixels’ spectra,in order to augment the estimations of both abundances and nonlinear probability parameters.Besides the spectrum-based regularizations, the sparsity of abundances is also incorporated for theproposed model. The resulting optimization problem is addressed by using the alternating directionmethod of multipliers (ADMM), yielding the so-called graph regularized MLM (G-MLM) algorithm.To implement the proposed method on large hypersepectral images in real world, we proposeto utilize a superpixel construction approach before unmixing, and then apply G-MLM on eachsuperpixel. The proposed methods achieve superior unmixing performances to state-of-the-artstrategies in terms of both abundances and probability parameters, on both synthetic and real datasets.

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Scene text removal via cascaded text stroke detection and erasing

Computational Visual Media ◽

10.1007/s41095-021-0242-8 ◽

2021 ◽

Vol 8 (2) ◽

pp. 273-287

Author(s):

Xuewei Bian ◽

Chaoqun Wang ◽

Weize Quan ◽

Juntao Ye ◽

Xiaopeng Zhang ◽

...

Keyword(s):

Performance Improvement ◽

Real World ◽

Large Scale ◽

State Of The Art ◽

The State ◽

Experimental Results ◽

Processing Unit ◽

Final Model ◽

Scene Text ◽

End To End

AbstractRecent learning-based approaches show promising performance improvement for the scene text removal task but usually leave several remnants of text and provide visually unpleasant results. In this work, a novel end-to-end framework is proposed based on accurate text stroke detection. Specifically, the text removal problem is decoupled into text stroke detection and stroke removal; we design separate networks to solve these two subproblems, the latter being a generative network. These two networks are combined as a processing unit, which is cascaded to obtain our final model for text removal. Experimental results demonstrate that the proposed method substantially outperforms the state-of-the-art for locating and erasing scene text. A new large-scale real-world dataset with 12,120 images has been constructed and is being made available to facilitate research, as current publicly available datasets are mainly synthetic so cannot properly measure the performance of different methods.

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Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

10.20944/preprints201912.0059.v1 ◽

2019 ◽

Author(s):

Li Rui ◽

Zheng Shunyi ◽

Duan Chenxi ◽

Yang Yang ◽

Wang Xiqi

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

State Of The Art ◽

Empirical Studies ◽

The State ◽

Attention Mechanism ◽

Spatial Features ◽

Better Than

In recent years, more and more researchers have gradually paid attention to Hyperspectral Image (HSI) classification. It is significant to implement researches on how to use HSI's sufficient spectral and spatial information to its fullest potential. To capture spectral and spatial features, we propose a Double-Branch Dual-Attention mechanism network (DBDA) for HSI classification in this paper, Two branches aer designed to extract spectral and spatial features separately to reduce the interferences between these two kinds of features. What is more, because distinguishing characteristics exist in the two branches, two types of attention mechanisms are applied in two branches above separately, ensuring to exploit spectral and spatial features more discriminatively. Finally, the extracted features are fused for classification. A series of empirical studies have been conducted on four hyperspectral datasets, and the results show that the proposed method performs better than the state-of-the-art method.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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A Multilayer CARU Framework to Obtain Probability Distribution for Paragraph-Based Sentiment Analysis

Applied Sciences ◽

10.3390/app112311344 ◽

2021 ◽

Vol 11 (23) ◽

pp. 11344

Author(s):

Wei Ke ◽

Ka-Hou Chan

Keyword(s):

Probability Distribution ◽

Information Extraction ◽

Sentiment Analysis ◽

State Of The Art ◽

Final Analysis ◽

The State ◽

Experimental Results ◽

Content Adaptive

Paragraph-based datasets are hard to analyze by a simple RNN, because a long sequence always contains lengthy problems of long-term dependencies. In this work, we propose a Multilayer Content-Adaptive Recurrent Unit (CARU) network for paragraph information extraction. In addition, we present a type of CNN-based model as an extractor to explore and capture useful features in the hidden state, which represent the content of the entire paragraph. In particular, we introduce the Chebyshev pooling to connect to the end of the CNN-based extractor instead of using the maximum pooling. This can project the features into a probability distribution so as to provide an interpretable evaluation for the final analysis. Experimental results demonstrate the superiority of the proposed approach, being compared to the state-of-the-art models.

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RDFuzz: Accelerating Directed Fuzzing with Intertwined Schedule and Optimized Mutation

Mathematical Problems in Engineering ◽

10.1155/2020/7698916 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Jiaxi Ye ◽

Ruilin Li ◽

Bin Zhang

Keyword(s):

Large Scale ◽

State Of The Art ◽

The State ◽

Experimental Results ◽

Exploration And Exploitation ◽

Balance Problem ◽

Evaluation Strategy ◽

Testing Schedule ◽

Available Resources

Directed fuzzing is a practical technique, which concentrates its testing energy on the process toward the target code areas, while costing little on other unconcerned components. It is a promising way to make better use of available resources, especially in testing large-scale programs. However, by observing the state-of-the-art-directed fuzzing engine (AFLGo), we argue that there are two universal limitations, the balance problem between the exploration and the exploitation and the blindness in mutation toward the target code areas. In this paper, we present a new prototype RDFuzz to address these two limitations. In RDFuzz, we first introduce the frequency-guided strategy in the exploration and improve its accuracy by adopting the branch-level instead of the path-level frequency. Then, we introduce the input-distance-based evaluation strategy in the exploitation stage and present an optimized mutation to distinguish and protect the distance sensitive input content. Moreover, an intertwined testing schedule is leveraged to perform the exploration and exploitation in turn. We test RDFuzz on 7 benchmarks, and the experimental results demonstrate that RDFuzz is skilled at driving the program toward the target code areas, and it is not easily stuck by the balance problem of the exploration and the exploitation.

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FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning

Applied Sciences ◽

10.3390/app10082864 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2864 ◽

Cited By ~ 4

Author(s):

Muhammad Asad ◽

Ahmed Moustafa ◽

Takayuki Ito

Keyword(s):

Privacy Preservation ◽

State Of The Art ◽

Homomorphic Encryption ◽

Evaluation Criteria ◽

The State ◽

Compression Algorithm ◽

Experimental Results ◽

Local Learning ◽

Learning Models ◽

Communication Efficiency

Artificial Intelligence (AI) has been applied to solve various challenges of real-world problems in recent years. However, the emergence of new AI technologies has brought several problems, especially with regard to communication efficiency, security threats and privacy violations. Towards this end, Federated Learning (FL) has received widespread attention due to its ability to facilitate the collaborative training of local learning models without compromising the privacy of data. However, recent studies have shown that FL still consumes considerable amounts of communication resources. These communication resources are vital for updating the learning models. In addition, the privacy of data could still be compromised once sharing the parameters of the local learning models in order to update the global model. Towards this end, we propose a new approach, namely, Federated Optimisation (FedOpt) in order to promote communication efficiency and privacy preservation in FL. In order to implement FedOpt, we design a novel compression algorithm, namely, Sparse Compression Algorithm (SCA) for efficient communication, and then integrate the additively homomorphic encryption with differential privacy to prevent data from being leaked. Thus, the proposed FedOpt smoothly trade-offs communication efficiency and privacy preservation in order to adopt the learning task. The experimental results demonstrate that FedOpt outperforms the state-of-the-art FL approaches. In particular, we consider three different evaluation criteria; model accuracy, communication efficiency and computation overhead. Then, we compare the proposed FedOpt with the baseline configurations and the state-of-the-art approaches, i.e., Federated Averaging (FedAvg) and the paillier-encryption based privacy-preserving deep learning (PPDL) on all these three evaluation criteria. The experimental results show that FedOpt is able to converge within fewer training epochs and a smaller privacy budget.

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Saga of Superfluid Solids

Physics ◽

10.3390/physics2010006 ◽

2020 ◽

Vol 2 (1) ◽

pp. 49-66 ◽

Cited By ~ 4

Author(s):

Vyacheslav I. Yukalov

Keyword(s):

Numerical Simulations ◽

State Of The Art ◽

Theoretical Models ◽

The State ◽

Experimental Results ◽

Standing Problem

The article presents the state of the art and reviews the literature on the long-standing problem of the possibility for a sample to be at the same time solid and superfluid. Theoretical models, numerical simulations, and experimental results are discussed.

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Hyperspectral Pansharpening Based on Homomorphic Filtering and Weighted Tensor Matrix

Remote Sensing ◽

10.3390/rs11091005 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1005

Author(s):

Jiahui Qu ◽

Yunsong Li ◽

Qian Du ◽

Wenqian Dong ◽

Bobo Xi

Keyword(s):

Spatial Resolution ◽

Spatial Information ◽

Mean Squared Error ◽

High Spatial Resolution ◽

Experimental Results ◽

Spectral Information ◽

Morphological Operation ◽

Effective Technique ◽

Squared Error ◽

Homomorphic Filtering

Hyperspectral pansharpening is an effective technique to obtain a high spatial resolution hyperspectral (HS) image. In this paper, a new hyperspectral pansharpening algorithm based on homomorphic filtering and weighted tensor matrix (HFWT) is proposed. In the proposed HFWT method, open-closing morphological operation is utilized to remove the noise of the HS image, and homomorphic filtering is introduced to extract the spatial details of each band in the denoised HS image. More importantly, a weighted root mean squared error-based method is proposed to obtain the total spatial information of the HS image, and an optimized weighted tensor matrix based strategy is presented to integrate spatial information of the HS image with spatial information of the panchromatic (PAN) image. With the appropriate integrated spatial details injection, the fused HS image is generated by constructing the suitable gain matrix. Experimental results over both simulated and real datasets demonstrate that the proposed HFWT method effectively generates the fused HS image with high spatial resolution while maintaining the spectral information of the original low spatial resolution HS image.

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Using a Panchromatic Image to Improve Hyperspectral Unmixing

Remote Sensing ◽

10.3390/rs12172834 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2834

Author(s):

Simon Rebeyrol ◽

Yannick Deville ◽

Véronique Achard ◽

Xavier Briottet ◽

Stephane May

Keyword(s):

Matrix Factorization ◽

State Of The Art ◽

Hyperspectral Data ◽

Spectral Unmixing ◽

Endmember Extraction ◽

Local Approach ◽

Hyperspectral Unmixing ◽

Panchromatic Image ◽

Art Methods ◽

Non Negative Matrix Factorization

Hyperspectral unmixing is a widely studied field of research aiming at estimating the pure material signatures and their abundance fractions from hyperspectral images. Most spectral unmixing methods are based on prior knowledge and assumptions that induce limitations, such as the existence of at least one pure pixel for each material. This work presents a new approach aiming to overcome some of these limitations by introducing a co-registered panchromatic image in the unmixing process. Our method, called Heterogeneity-Based Endmember Extraction coupled with Local Constrained Non-negative Matrix Factorization (HBEE-LCNMF), has several steps: a first set of endmembers is estimated based on a heterogeneity criterion applied on the panchromatic image followed by a spectral clustering. Then, in order to complete this first endmember set, a local approach using a constrained non-negative matrix factorization strategy, is proposed. The performance of our method, in regards of several criteria, is compared to those of state-of-the-art methods obtained on synthetic and satellite data describing urban and periurban scenes, and considering the French HYPXIM/HYPEX2 mission characteristics. The synthetic images are built with real spectral reflectances and do not contain a pure pixel for each endmember. The satellite images are simulated from airborne acquisition with the spatial and spectral features of the mission. Our method demonstrates the benefit of a panchromatic image to reduce some well-known limitations in unmixing hyperspectral data. On synthetic data, our method reduces the spectral angle between the endmembers and the real material spectra by 46% compared to the Vertex Component Analysis (VCA) and N-finder (N-FINDR) methods. On real data, HBEE-LCNMF and other methods yield equivalent performance, but, the proposed method shows more robustness over the data sets compared to the tested state-of-the-art methods. Moreover, HBEE-LCNMF does not require one to know the number of endmembers.

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