1,188 research outputs found
Optimal Clustering Framework for Hyperspectral Band Selection
Band selection, by choosing a set of representative bands in hyperspectral
image (HSI), is an effective method to reduce the redundant information without
compromising the original contents. Recently, various unsupervised band
selection methods have been proposed, but most of them are based on
approximation algorithms which can only obtain suboptimal solutions toward a
specific objective function. This paper focuses on clustering-based band
selection, and proposes a new framework to solve the above dilemma, claiming
the following contributions: 1) An optimal clustering framework (OCF), which
can obtain the optimal clustering result for a particular form of objective
function under a reasonable constraint. 2) A rank on clusters strategy (RCS),
which provides an effective criterion to select bands on existing clustering
structure. 3) An automatic method to determine the number of the required
bands, which can better evaluate the distinctive information produced by
certain number of bands. In experiments, the proposed algorithm is compared to
some state-of-the-art competitors. According to the experimental results, the
proposed algorithm is robust and significantly outperform the other methods on
various data sets
Unsupervised spectral sub-feature learning for hyperspectral image classification
Spectral pixel classification is one of the principal techniques used in hyperspectral image (HSI) analysis. In this article, we propose an unsupervised feature learning method for classification of hyperspectral images. The proposed method learns a dictionary of sub-feature basis representations from the spectral domain, which allows effective use of the correlated spectral data. The learned dictionary is then used in encoding convolutional samples from the hyperspectral input pixels to an expanded but sparse feature space. Expanded hyperspectral feature representations enable linear separation between object classes present in an image. To evaluate the proposed method, we performed experiments on several commonly used HSI data sets acquired at different locations and by different sensors. Our experimental results show that the proposed method outperforms other pixel-wise classification methods that make use of unsupervised feature extraction approaches. Additionally, even though our approach does not use any prior knowledge, or labelled training data to learn features, it yields either advantageous, or comparable, results in terms of classification accuracy with respect to recent semi-supervised methods
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
Change detection (CD) is an important application of remote sensing, which
provides timely change information about large-scale Earth surface. With the
emergence of hyperspectral imagery, CD technology has been greatly promoted, as
hyperspectral data with the highspectral resolution are capable of detecting
finer changes than using the traditional multispectral imagery. Nevertheless,
the high dimension of hyperspectral data makes it difficult to implement
traditional CD algorithms. Besides, endmember abundance information at subpixel
level is often not fully utilized. In order to better handle high dimension
problem and explore abundance information, this paper presents a General
End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image
change detection (HSI-CD). The main contributions of this work are threefold:
1) Mixed-affinity matrix that integrates subpixel representation is introduced
to mine more cross-channel gradient features and fuse multi-source information;
2) 2-D CNN is designed to learn the discriminative features effectively from
multi-source data at a higher level and enhance the generalization ability of
the proposed CD algorithm; 3) A new HSI-CD data set is designed for the
objective comparison of different methods. Experimental results on real
hyperspectral data sets demonstrate the proposed method outperforms most of the
state-of-the-arts
Mineral identification using data-mining in hyperspectral infrared imagery
Les applications de lâimagerie infrarouge dans le domaine de la gĂ©ologie sont principalement des applications hyperspectrales. Elles permettent entre autre lâidentification minĂ©rale, la cartographie, ainsi que lâestimation de la portĂ©e. Le plus souvent, ces acquisitions sont rĂ©alisĂ©es in-situ soit Ă lâaide de capteurs aĂ©roportĂ©s, soit Ă lâaide de dispositifs portatifs. La dĂ©couverte de minĂ©raux indicateurs a permis dâamĂ©liorer grandement lâexploration minĂ©rale. Ceci est en partie dĂ» Ă lâutilisation dâinstruments portatifs. Dans ce contexte le dĂ©veloppement de systĂšmes automatisĂ©s permettrait dâaugmenter Ă la fois la qualitĂ© de lâexploration et la prĂ©cision de la dĂ©tection des indicateurs. Câest dans ce cadre que sâinscrit le travail menĂ© dans ce doctorat. Le sujet consistait en lâutilisation de mĂ©thodes dâapprentissage automatique appliquĂ©es Ă lâanalyse (au traitement) dâimages hyperspectrales prises dans les longueurs dâonde infrarouge. Lâobjectif recherchĂ© Ă©tant lâidentification de grains minĂ©raux de petites tailles utilisĂ©s comme indicateurs minĂ©ral -ogiques. Une application potentielle de cette recherche serait le dĂ©veloppement dâun outil logiciel dâassistance pour lâanalyse des Ă©chantillons lors de lâexploration minĂ©rale. Les expĂ©riences ont Ă©tĂ© menĂ©es en laboratoire dans la gamme relative Ă lâinfrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m Ă 11.8 m. Ces essais ont permis de proposer une mĂ©thode pour calculer lâannulation du continuum. La mĂ©thode utilisĂ©e lors de ces essais utilise la factorisation matricielle non nĂ©gative (NMF). En utlisant une factorisation du premier ordre on peut dĂ©duire le rayonnement de pĂ©nĂ©tration, lequel peut ensuite ĂȘtre comparĂ© et analysĂ© par rapport Ă dâautres mĂ©thodes plus communes. Lâanalyse des rĂ©sultats spectraux en comparaison avec plusieurs bibliothĂšques existantes de donnĂ©es a permis de mettre en Ă©vidence la suppression du continuum. Les expĂ©rience ayant menĂ©s Ă ce rĂ©sultat ont Ă©tĂ© conduites en utilisant une plaque Infragold ainsi quâun objectif macro LWIR. Lâidentification automatique de grains de diffĂ©rents matĂ©riaux tels que la pyrope, lâolivine et le quartz a commencĂ©. Lors dâune phase de comparaison entre des approches supervisĂ©es et non supervisĂ©es, cette derniĂšre sâest montrĂ©e plus appropriĂ© en raison du comportement indĂ©pendant par rapport Ă lâĂ©tape dâentraĂźnement. Afin de confirmer la qualitĂ© de ces rĂ©sultats quatre expĂ©riences ont Ă©tĂ© menĂ©es. Lors dâune premiĂšre expĂ©rience deux algorithmes ont Ă©tĂ© Ă©valuĂ©s pour application de regroupements en utilisant lâapproche FCC (False Colour Composite). Cet essai a permis dâobserver une vitesse de convergence, jusquâa vingt fois plus rapide, ainsi quâune efficacitĂ© significativement accrue concernant lâidentification en comparaison des rĂ©sultats de la littĂ©rature. Cependant des essais effectuĂ©s sur des donnĂ©es LWIR ont montrĂ© un manque de prĂ©diction de la surface du grain lorsque les grains Ă©taient irrĂ©guliers avec prĂ©sence dâagrĂ©gats minĂ©raux. La seconde expĂ©rience a consistĂ©, en une analyse quantitaive comparative entre deux bases de donnĂ©es de Ground Truth (GT), nommĂ©e rigid-GT et observed-GT (rigide-GT: Ă©tiquet manuel de la rĂ©gion, observĂ©e-GT:Ă©tiquetage manuel les pixels). La prĂ©cision des rĂ©sultats Ă©tait 1.5 fois meilleur lorsque lâon a utlisĂ© la base de donnĂ©es observed-GT que rigid-GT. Pour les deux derniĂšres epxĂ©rience, des donnĂ©es venant dâun MEB (Microscope Ălectronique Ă Balayage) ainsi que dâun microscopie Ă fluorescence (XRF) ont Ă©tĂ© ajoutĂ©es. Ces donnĂ©es ont permis dâintroduire des informations relatives tant aux agrĂ©gats minĂ©raux quâĂ la surface des grains. Les rĂ©sultats ont Ă©tĂ© comparĂ©s par des techniques dâidentification automatique des minĂ©raux, utilisant ArcGIS. Cette derniĂšre a montrĂ© une performance prometteuse quand Ă lâidentification automatique et Ă aussi Ă©tĂ© utilisĂ©e pour la GT de validation. Dans lâensemble, les quatre mĂ©thodes de cette thĂšse reprĂ©sentent des mĂ©thodologies bĂ©nĂ©fiques pour lâidentification des minĂ©raux. Ces mĂ©thodes prĂ©sentent lâavantage dâĂȘtre non-destructives, relativement prĂ©cises et dâavoir un faible coĂ»t en temps calcul ce qui pourrait les qualifier pour ĂȘtre utilisĂ©e dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7ÎŒm to 11.8ÎŒm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grainâs surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grainâs surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field
NOISY BAND SELECTION BASED ON THE INTEGRATION OF THE STACKED-AUTOENCODER AND CONVOLUTIONAL NEURAL NETWORK APPROACHES FOR HYPERSPECTRAL DATA: Seleção de bandas ruidosas baseada na integração de Stacked-AutoEncoder e redes neurais convulacionais para dados hiperespectrais
The presence of noise on hyperspectral images causes degradation and hinders efficiency of processing for land cover classification. In this sense, removing noise or detecting noisy bands automatically on hyperspectral images becomes a challenge for research in remote sensing. To cope this problem, an integrated model (SAE-1DCNN) is presented in this study, based on Stacked-Autoencoders (SAE) and Convolutional Neural Networks (CNN) algorithms for the selection and exclusion of noisy bands. The proposed model employs convolutional layers to improve the performance of autoencoders focused on discriminating the training data by analyzing the hyperspectral signature of the pixel. Thus, in the SAE-1DCNN model, information can be compressed, and then redundant information can be detected and extracted by taking advantage of the efficiency of the deep architecture based on the convolutional and pooling layers. Hyperspectral data from the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor were used to evaluate the performance of the proposed automatic method based on feature selection. The results showed effectiveness to identify noisy bands automatically, suggesting that the proposed methodology was found to be promising and can be an alternative to identify noisy bands within the scope of hyperspectral data pre-processing.
Keywords: noisy bands; feature selection; convolutional neural network; stacked-autoencoders; hyperspectral dataRESUMO - A presença de ruĂdo em imagens hiperespectrais causa degradação e dificulta a eficiĂȘncia no processamento para a classificação da cobertura terrestre. Nesse sentido, a remoção do ruĂdo ou a detecção automĂĄtica de bandas ruidosas em imagens hiperespectrais torna-se um desafio para pesquisas na ĂĄrea de sensoriamento remoto. Para solucionar esse problema, um modelo integrado (SAE-1DCNN) Ă© apresentado nesse estudo, baseado nos algoritmos de Deep Learning conhecidos como: Stacked-Autoencoders (SAE) e Redes Neurais Convolucionais (CNN) para a seleção e exclusĂŁo de bandas ruidosas. O modelo proposto emprega as camadas convolucionais para melhorar o desempenho dos Autoencoders focados na discriminação dos dados de treinamento por meio da anĂĄlise da assinatura hiperespectral do pixel. Assim, no modelo SAE-1DCNN, a informação pode ser comprimida, e depois a informação redundante pode ser detectada e extraĂda tirando partido da eficiĂȘncia da arquitetura profunda baseada nas camadas convolucionais e de agrupamento. Os dados hiperespectrais do sensor AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) foram utilizados para avaliar o desempenho do mĂ©todo automĂĄtico proposto, com base na seleção de atributos. Os resultados mostraram eficĂĄcia na identificação automĂĄtica de bandas ruidosas, sugerindo que a metodologia proposta foi considerada promissora e pode ser uma alternativa para identificar bandas ruidosas no Ăąmbito de prĂ©-processamento de dados hiperespectrais.
ABSTRACT - The presence of noise on hyperspectral images causes degradation and hinders efficiency of processing for land cover classification. In this sense, removing noise or detecting noisy bands automatically on hyperspectral images becomes a challenge for research in remote sensing. To cope this problem, an integrated model (SAE-1DCNN) is presented in this study, based on Stacked-Autoencoders (SAE) and Convolutional Neural Networks (CNN) algorithms for the selection and exclusion of noisy bands. The proposed model employs convolutional layers to improve the performance of autoencoders focused on discriminating the training data by analyzing the hyperspectral signature of the pixel. Thus, in the SAE-1DCNN model, information can be compressed, and then redundant information can be detected and extracted by taking advantage of the efficiency of the deep architecture based on the convolutional and pooling layers. Hyperspectral data from the AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) sensor were used to evaluate the performance of the proposed automatic method based on feature selection. The results showed effectiveness to identify noisy bands automatically, suggesting that the proposed methodology was found to be promising and can be an alternative to identify noisy bands within the scope of hyperspectral data pre-processing
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Key Information Retrieval in Hyperspectral Imagery through Spatial-Spectral Data Fusion
Hyperspectral (HS) imaging is measuring the radiance of materials within each pixel area at a large number of contiguous spectral wavelength bands. The key spatial information such as small targets and border lines are hard to be precisely detected from HS data due to the technological constraints. Therefore, the need for image processing techniques is an important field of research in HS remote sensing. A novel semisupervised spatial-spectral data fusion method for resolution enhancement of HS images through maximizing the spatial correlation of the endmembers (signature of pure or purest materials in the scene) using a superresolution mapping (SRM) technique is proposed in this paper. The method adopts a linear mixture model and a fully constrained least squares spectral unmixing algorithm to obtain the endmember abundances (fractional images) of HS images. Then, the extracted endmember distribution maps are fused with the spatial information using a spatial-spectral correlation maximizing model and a learning-based SRM technique to exploit the subpixel level data. The obtained results validate the reliability of the technique for key information retrieval. The proposed method is very efficient and is low in terms of computational cost which makes it favorable for real-time applications
Hyperspectral Remote Sensing Benchmark Database for Oil Spill Detection with an Isolation Forest-Guided Unsupervised Detector
Oil spill detection has attracted increasing attention in recent years since
marine oil spill accidents severely affect environments, natural resources, and
the lives of coastal inhabitants. Hyperspectral remote sensing images provide
rich spectral information which is beneficial for the monitoring of oil spills
in complex ocean scenarios. However, most of the existing approaches are based
on supervised and semi-supervised frameworks to detect oil spills from
hyperspectral images (HSIs), which require a huge amount of effort to annotate
a certain number of high-quality training sets. In this study, we make the
first attempt to develop an unsupervised oil spill detection method based on
isolation forest for HSIs. First, considering that the noise level varies among
different bands, a noise variance estimation method is exploited to evaluate
the noise level of different bands, and the bands corrupted by severe noise are
removed. Second, kernel principal component analysis (KPCA) is employed to
reduce the high dimensionality of the HSIs. Then, the probability of each pixel
belonging to one of the classes of seawater and oil spills is estimated with
the isolation forest, and a set of pseudo-labeled training samples is
automatically produced using the clustering algorithm on the detected
probability. Finally, an initial detection map can be obtained by performing
the support vector machine (SVM) on the dimension-reduced data, and then, the
initial detection result is further optimized with the extended random walker
(ERW) model so as to improve the detection accuracy of oil spills. Experiments
on airborne hyperspectral oil spill data (HOSD) created by ourselves
demonstrate that the proposed method obtains superior detection performance
with respect to other state-of-the-art detection approaches
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