Distributed Unmixing of Hyperspectral Data With Sparsity Constraint

Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which was added to NMF is sparsity constraint that was regularized by L 1/2 norm. In this paper, a new algorithm based on distributed optimization has been used for spectral unmixing. In the proposed algorithm, a network including single-node clusters has been employed. Each pixel in hyperspectral images considered as a node in this network. The distributed unmixing with sparsity constraint has been optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods. The results show that the AAD and SAD of the proposed approach are improved respectively about 6 and 27 percent toward distributed unmixing in SNR=25dB.Comment: 6 pages, conference pape

Multitask Diffusion Adaptation over Networks

Adaptive networks are suitable for decentralized inference tasks, e.g., to monitor complex natural phenomena. Recent research works have intensively studied distributed optimization problems in the case where the nodes have to estimate a single optimum parameter vector collaboratively. However, there are many important applications that are multitask-oriented in the sense that there are multiple optimum parameter vectors to be inferred simultaneously, in a collaborative manner, over the area covered by the network. In this paper, we employ diffusion strategies to develop distributed algorithms that address multitask problems by minimizing an appropriate mean-square error criterion with $\ell_2$-regularization. The stability and convergence of the algorithm in the mean and in the mean-square sense is analyzed. Simulations are conducted to verify the theoretical findings, and to illustrate how the distributed strategy can be used in several useful applications related to spectral sensing, target localization, and hyperspectral data unmixing.Comment: 29 pages, 11 figures, submitted for publicatio

Blind Hyperspectral Unmixing Using Autoencoders

The subject of this thesis is blind hyperspectral unmixing using deep learning based autoencoders. Two methods based on autoencoders are proposed and analyzed. Both methods seek to exploit the spatial correlations in the hyperspectral images to improve the performance. One by using multitask learning to simultaneously unmix a neighbourhood of pixels while the other by using a convolutional neural network autoencoder. This increases the consistency and robustness of the methods. In addition, a review of the various autoencoder methods in the literature is given along with a detailed discussion of different types of autoencoders. The thesis concludes by a critical comparison of eleven different autoencoder based methods. Ablation experiments are performed to answer the question of why autoencoders are so effective in blind hyperspectral unmixing, and an opinion is given on what the future in autoencoder unmixing holds.Efni þessarar ritgerðar er aðgreining fjölrásamynda (e. blind hyperspectral unmixing) með sjálfkóðurum (e. autoencoders) byggðum á djúpum lærdómi (e. deep learning). Tvær aðferðir byggðar á sjálfkóðurum eru kynntar og rannsakaðar. Báðar aðferðirnar leitast við að nýta sér rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Ein aðferð með að nýta sér fjölbeitingarlærdóm (e. multitask learning) og hin með að nota sjálfkóðara útfærðan með földunartaugnaneti (e. convolutional neural network). Hvortveggja bætir samkvæmni og hæfni fjölrásagreiningarinnar. Ennfremur inniheldur ritgerðin yfirgripsmikið yfirlit yfir þær sjálfkóðaraaðferðir sem hafa verið birtar ásamt greinargóðri umræðu um mismunandi gerðir sjálfkóðara og útfærslur á þeim. í lok ritgerðarinnar er svo að finna gagnrýninn samanburð á 11 mismunandi aðferðum byggðum á sjálfkóðurum. Brottnáms (e. ablation) tilraunir eru gerðar til að svara spurningunni hvers vegna sjálfkóðarar eru svo árangursríkir í fjölrásagreiningu og stuttlega rætt um hvað framtíðin ber í skauti sér varðandi aðgreiningu fjölrásamynda með sjálfkóðurum. Megin framlag ritgerðarinnar er eftirfarandi: - Ný sjálfkóðaraaðferð, MTLAEU, sem nýtir á beinan hátt rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Aðferðin notar fjölbeitingarlærdóm til að aðgreina grennd af rófum í einu. - Ný aðferð, CNNAEU, sem notar 2D földunartaugnanet fyrir bæði kóðara og afkóðara og er fyrsta birta aðferðin til að gera það. Aðferðin er þjálfuð á myndbútum (e.patches) og því er rúmfræðileg bygging myndarinnar sem greina á varðveitt í gegnum aðferðina. - Yfirgripsmikil og ítarlegt fræðilegt yfirlit yfir birtar sjálfkóðaraaðferðir fyrir fjölrásagreiningu. Gefinn er inngangur að sjálfkóðurum og elstu tegundir sjálfkóðara eru kynntar. Gefið er greinargott yfirlit yfir helstu birtar aðferðir fyrir fjölrásagreiningu sem byggja á sjálfkóðurum og gerður er gangrýninn samburður á 11 mismunandi sjálfkóðaraaðferðum.The Icelandic Research Fund under Grants 174075-05 and 207233-05

Subspace Structure Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing

Hyperspectral unmixing is a crucial task for hyperspectral images (HSI) processing, which estimates the proportions of constituent materials of a mixed pixel. Usually, the mixed pixels can be approximated using a linear mixing model. Since each material only occurs in a few pixels in real HSI, sparse nonnegative matrix factorization (NMF) and its extensions are widely used as solutions. Some recent works assume that materials are distributed in certain structures, which can be added as constraints to sparse NMF model. However, they only consider the spatial distribution within a local neighborhood and define the distribution structure manually, while ignoring the real distribution of materials that is diverse in different images. In this paper, we propose a new unmixing method that learns a subspace structure from the original image and incorporate it into the sparse NMF framework to promote unmixing performance. Based on the self-representation property of data points lying in the same subspace, the learned subspace structure can indicate the global similar graph of pixels that represents the real distribution of materials. Then the similar graph is used as a robust global spatial prior which is expected to be maintained in the decomposed abundance matrix. The experiments conducted on both simulated and real-world HSI datasets demonstrate the superior performance of our proposed method

A REVIEW ON MULTIPLE-FEATURE-BASED ADAPTIVE SPARSE REPRESENTATION (MFASR) AND OTHER CLASSIFICATION TYPES

A new technique Multiple-feature-based adaptive sparse representation (MFASR) has been demonstrated for Hyperspectral Images (HSI's) classification. This method involves mainly in four steps at the various stages. The spectral and spatial information reflected from the original Hyperspectral Images with four various features. A shape adaptive (SA) spatial region is obtained in each pixel region at the second step. The algorithm namely sparse representation has applied to get the coefficients of sparse for each shape adaptive region in the form of matrix with multiple features. For each test pixel, the class label is determined with the help of obtained coefficients. The performances of MFASR have much better classification results than other classifiers in the terms of quantitative and qualitative percentage of results. This MFASR will make benefit of strong correlations that are obtained from different extracted features and this make use of effective features and effective adaptive sparse representation. Thus, the very high classification performance was achieved through this MFASR technique

Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package

Spectral pixels are often a mixture of the pure spectra of the materials, called endmembers, due to the low spatial resolution of hyperspectral sensors, double scattering, and intimate mixtures of materials in the scenes. Unmixing estimates the fractional abundances of the endmembers within the pixel. Depending on the prior knowledge of endmembers, linear unmixing can be divided into three main groups: supervised, semi-supervised, and unsupervised (blind) linear unmixing. Advances in Image processing and machine learning substantially affected unmixing. This paper provides an overview of advanced and conventional unmixing approaches. Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories. We compare the performance of the unmixing techniques on three simulated and two real datasets. The experimental results reveal the advantages of different unmixing categories for different unmixing scenarios. Moreover, we provide an open-source Python-based package available at https://github.com/BehnoodRasti/HySUPP to reproduce the results

Foreword to the special Issue on Hyperspectral remote sensing and imaging spectroscopy

The twenty six papers in this special issue focus on the technologies of hyperspectral remote sensing (HRS)and imaging spectroscopy. HRS has emerged as a powerful tool to understand phenomena at local and global scales by virtue of imaging through a diverse range of platforms, including terrestrial in-situ imaging platforms, unmanned and manned aerial vehicles, and satellite platforms. By virtue of imaging over a wide range of spectral wavelengths, it is possible to characterize object specific properties very accurately. As a result, hyperspectral imaging (also known as imaging spectroscopy) has gained popularity for a wide variety of applications, including environment monitoring, precision agriculture, mineralogy, forestry, urban planning, and defense applications. The increased analysis capability comes at a cost—there are a variety of challenges that must be overcome for robust image analysis of such data, including high dimensionality, limited sample size for training supervised models, noise and atmospheric affects, mixed pixels, etc. The papers in this issue represent some of the recent developments in image analysis algorithms and unique applications of hyperspectral imaging data

Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing

Hyperspectral imaging, also known as image spectrometry, is a landmark technique in geoscience and remote sensing (RS). In the past decade, enormous efforts have been made to process and analyze these hyperspectral (HS) products mainly by means of seasoned experts. However, with the ever-growing volume of data, the bulk of costs in manpower and material resources poses new challenges on reducing the burden of manual labor and improving efficiency. For this reason, it is, therefore, urgent to develop more intelligent and automatic approaches for various HS RS applications. Machine learning (ML) tools with convex optimization have successfully undertaken the tasks of numerous artificial intelligence (AI)-related applications. However, their ability in handling complex practical problems remains limited, particularly for HS data, due to the effects of various spectral variabilities in the process of HS imaging and the complexity and redundancy of higher dimensional HS signals. Compared to the convex models, non-convex modeling, which is capable of characterizing more complex real scenes and providing the model interpretability technically and theoretically, has been proven to be a feasible solution to reduce the gap between challenging HS vision tasks and currently advanced intelligent data processing models