Enhancing Hyperspectral Image Quality using Nonlinear PCA

International audienceIn this paper, we propose a new method aiming at reducing the noise in hyperspectral images. It is based on the nonlinear generalization of Principal Component Analysis (NLPCA). The NLPCA is performed by an auto associative neural network that have the hyperspectral image as input and is trained to reconstruct the same image at the output. Thanks to its bottleneck structure, the AANN forces the hyper spectral image to be projected in a lower dimensionality feature space where noise as well as both linear and nonlinear correlations between spectral bands are removed. This process permits to obtain enhancements in terms of hyperspectral image quality. Experiments are conducted on different real hyper spectral images, with different contexts and resolutions. The results are qualitatively and quantitatively discussed and demonstrate the interest of the proposed method as compared to traditional approaches

Spectral transformation based on nonlinear principal component analysis for dimensionality reduction of hyperspectral images

Publisher's version (útgefin grein)Managing transmission and storage of hyperspectral (HS) images can be extremely difficult. Thus, the dimensionality reduction of HS data becomes necessary. Among several dimensionality reduction techniques, transform-based have found to be effective for HS data. While spatial transformation techniques provide good compression rates, the choice of the spectral decorrelation approaches can have strong impact on the quality of the compressed image. Since HS images are highly correlated within each spectral band and in particular across neighboring bands, the choice of a decorrelation method allowing to retain as much information content as possible is desirable. From this point of view, several methods based on PCA and Wavelet have been presented in the literature. In this paper, we propose the use of NLPCA transform as a method to reduce the spectral dimensionality of HS data. NLPCA represents in a lower dimensional space the same information content with less features than PCA. In these terms, aim of this research is focused on the analysis of the results obtained through the spectral decorrelation phase rather than taking advantage of both spectral and spatial compression. Experimental results assessing the advantage of NLPCA with respect to standard PCA are presented on four different HS datasets.This work was supported by the Agence Nationale de la Recherche [project APHYPIS]Peer Reviewe

Multi-resolution analysis techniques and nonlinear PCA for hybrid pansharpening applications

International audienceHyperspectral images have a higher spectral resolution (i.e., a larger number of bands covering the electromagnetic spectrum), but a lower spatial resolution with respect to multispectral or panchromatic acquisitions. For increasing the capabilities of the data in terms of utilization and interpretation, hyperspectral images having both high spectral and spatial resolution are desired. This can be achieved by combining the hyperspectral image with a high spatial resolution panchromatic image. These techniques are generally known as pansharpening and can be divided into component substitution (CS) and multi-resolution analysis (MRA) based methods. In general, the CS methods result in fused images having high spatial quality but the fused images suffer from spectral distortions. On the other hand, images obtained using MRA techniques are not as sharp as CS methods but they are spectrally consistent. Both substitution and filtering approaches are considered adequate when applied to multispectral and PAN images, but have many drawbacks when the low-resolution image is a hyperspectral image. Thus, one of the main challenges in hyperspectral pansharpening is to improve the spatial resolution while preserving as much as possible of the original spectral information. An effective solution to these problems has been found in the use of hybrid approaches, combining the better spatial information of CS and the more accurate spectral information of MRA techniques. In general, in a hybrid approach a CS technique is used to project the original data into a low dimensionality space. Thus, the PAN image is fused with one or more features by means of MRA approach. Finally the inverse projection is used to obtain the enhanced image in the original data space. These methods, permit to effectively enhance the spatial resolution of the hyperspectral image without relevant spectral distortions and on the same time to reduce the computational load of the entire process. In particular, in this paper we focus our attention on the use of Non-linear Principal Component Analysis (NLPCA) for the projection of the image into a low dimensionality feature space. However, if on one hand the NLPCA has been proved to better represent the intrinsic information of hyperspectral images in the feature space, on the other hand, an analysis of the impact of different fusion techniques applied to the nonlinear principal components in order to define the optimal framework for the hybrid pansharpening has not been carried out yet. More in particular, in this paper we analyze the overall impact of several widely used MRA pansharpening algorithms applied in the nonlinear feature space. The results obtained on both synthetic and real data demonstrate that, an accurate selection of the pansharpening method can lead to an effective improvement of the enhanced hyperspectral image in terms of spectral quality and spatial consistency, as well as a strong reduction in the computational time

Super-resolution of hyperspectral images using local spectral unmixing

International audienceFor many remote sensing applications it is preferable to have images with both high spectral and spatial resolutions. On this regards, hyperspectral and multispectral images have complementary characteristics in terms of spectral and spatial resolutions. In this paper we propose an approach for the fusion of low spatial resolution hyperspectral images with high spatial resolution multispectral images in order to obtain superresolution (spatial and spectral) hyperspectral images. The proposed approach is based on the assumption that, since both hyperspectral and multispectral images acquired on the same scene, the corresponding endmembers should be the same. On a first step the hyperspectral image is spectrally downsampled in order to match the multispectral one. Then an endmember extraction algorithm is performed on the downsampled hyperspectral image and the successive abundance estimation is performed on the multispectral one. Finally, the extracted endmembers are up-sampled back to the original hyperspectral space and then used to reconstruct the super-resolution hyperspectral image according to the abundances obtained from the multispectral image

Hyperspectral super-resolution of locally low rank images from complementary multisource data

International audienceRemote sensing hyperspectral images (HSI) are quite often low rank, in the sense that the data belong to a low dimensional subspace/manifold. This has been recently exploited for the fusion of low spatial resolution HSI with high spatial resolution multispectral images (MSI) in order to obtain super-resolution HSI. Most approaches adopt an unmixing or a matrix factorization perspective. The derived methods have led to state-of-the-art results when the spectral information lies in a low dimensional subspace/manifold. However, if the subspace/manifold dimensionality spanned by the complete data set is large, i.e., larger than the number of multispectral bands, the performance of these methods decrease mainly because the underlying sparse regression problem is severely ill-posed. In this paper, we propose a local approach to cope with this difficulty. Fundamentally, we exploit the fact that real world HSI are locally low rank, that is, pixels acquired from a given spatial neighborhood span a very low dimensional subspace/manifold, i.e., lower or equal than the number of multispectral bands. Thus, we propose to partition the image into patches and solve the data fusion problem independently for each patch. This way, in each patch the subspace/manifold dimensionality is low enough such that the problem is not ill-posed anymore. We propose two alternative approaches to define the hyperspectral super-resolution via local dictionary learning using endmember induction algorithms (HSR-LDL-EIA). We also explore two alternatives to define the local regions, using sliding windows and binary partition trees. The effectiveness of the proposed approaches is illustrated with synthetic and semi real data

Image fusion and spectral unmixing of hyperspectral images for spatial improvement of classification maps

International audienceIn this paper we propose a new approach for the improvement of the spatial resolution of hyperspectral image classification maps combining both spectral unmixing and pansharpening approaches. The main idea is to use a spectral unmixing algorithm based on neural networks to retrieve the abundances of the endmembers present in the scene, and then use the spatial information retrieved from the pansharpened image to find the location of each endmember within the enhanced pixel according to the endmembers abundances. The proposed approach has been applied both to real and synthetic datasets

Streptomyces coelicolor Vesicles: Many Molecules To Be Delivered

Streptomyces coelicolor is a model organism for the study of Streptomyces, a genus of Gram-positive bacteria that undergoes a complex life cycle and produces a broad repertoire of bioactive metabolites and extracellular enzymes. This study investigated the production and characterization of membrane vesicles (MVs) in liquid cultures of S. coelicolor M145 from a structural and biochemical point of view; this was achieved by combining microscopic, physical and -omits analyses. Two main populations of MVs, with different sizes and cargos, were isolated and purified. S. coelicolor MV cargo was determined to be complex, containing different kinds of proteins and metabolites. In particular, a total of 166 proteins involved in cell metabolism/differentiation, molecular processing/transport, and stress response were identified in MVs, the latter functional class also being important for bacterial morpho-physiological differentiation. A subset of these proteins was protected from degradation following treatment of MVs with proteinase K, indicating their localization inside the vesicles. Moreover, S. coelicolor MVs contained an array of metabolites, such as antibiotics, vitamins, amino acids, and components of carbon metabolism. In conclusion, this analysis provides detailed information on S. coelicolor MVs under basal conditions and on their corresponding content, which may be useful in the near future to elucidate vesicle biogenesis and functions.IMPORTANCE Streptomycetes are widely distributed in nature and characterized by a complex life cycle that involves morphological differentiation. They are very relevant in industry because they produce about half of all clinically used antibiotics, as well as other important pharmaceutical products of natural origin. Streptomyces coelicolor is a model organism for the study of bacterial differentiation and bioactive molecule production. S. coelicolor produces extracellular vesicles that carry many molecules, such as proteins and metabolites, including antibiotics. The elucidation of S. coelicolor extracellular vesicle cargo will help us to understand different aspects of streptomycete physiology, such as cell communication during differentiation and response to environmental stimuli. Moreover, the capability of these vesicles for carrying different kinds of biomolecules opens up new biotechnological possibilities related to drug delivery. Indeed, decoding the molecular mechanisms involved in cargo selection may lead to the customization of extracellular vesicle content

The Outcome of the 2021 IEEE GRSS Data Fusion Contest - Track DSE: Detection of Settlements without electricity

International audienceIn this article, we elaborate on the scientific outcomes of the 2021 Data Fusion Contest (DFC2021), which was organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society, on the subject of geospatial artificial intelligence for social good. The ultimate objective of the contest was to model the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data towards sustainable developments. DFC2021 consisted of two challenge tracks: Detection of settlements without electricity (DSE) and multitemporal semantic change detection. We focus here on the outcome of the DSE track. This article presents the corresponding approaches and reports the results of the best-performing methods during the contest

Neural network architectures for information extraction from hyper-spectral images

L’Imaging spectroscopy, meglio conosciuta come telerilevamento da dati iperspettrali, è una tecnica che permette di identificare materiali presenti nell’aria, suolo e acqua, sulla base della riflettanza risultante dall’interazione dell’energia solare con la struttura molecalare dell’elemento. I recenti passi avanti nello sviluppo della sensoristica aeropsaziale ha portato allo sviluppo di strumenti in grado di acquisire centinaia di immagini, rappresentanti intervalli di banda sempre più stretti e contigui, relativi alla stessa zona della superficie terrestre. Come conseguenza di questo, ogni vettore di pixel in un immagine ha associata una “firma spettrale”, che caratterizza univocamente il materiale osservato dal sensore. I sensori iperspettrali ricoprono principalmente lunghezze d’onda che vanno dalla banda del visibile (0.4μm – 0.7 μm) all’infrarosso intermedio (2.4μm). Se consideriamo la consistenza di questo tipo di dati, è facile capire l’ìimportanza di trovare un metodo che permetta di trasformare il dato iniziale composto da centinaia di bande in uno a dimensionalità ridotta ed allo stesso tempo mantenere la maggior quantità di informazione possibile. Queste tecniche sono note come “feature reduction”. Oltre che permettere una gestione migliore del data, le tecniche di feature reduction hanno un ruolo cruciale nell’implementazione di algoritmi di inversione. Questo lavoro cerca di dare un contributo alla ricerca nel campo dell’estrazione dell’informazione dai dati iperspettrali. A questo proposito vengono utilizzati algoritmi di rete neurali, già riconosciuti come una delle migliori famiglie di algoritmi per l’analisi di dati iperspettrali. Oltre alla presentazione di un nuovo approccio per la riduzione della dimensionalità del dato iperspettrale, vengono affrontati anche altri argomenti riguardanti I dati iperspettrali, con particolare attenzione al problema dell’ “unmixing”, meglio conosciuta come classificazione sub-pixel. In questa tesi i primi tre capitoli sono dedicati alla presentazione dei vari problemi, alla descrizione dell’attuale stato dell’arte e alle soluzioni proposte. I capitoli rimanenti vengono invece dedicati alla descrizione e alla valutazione dei risultati ottenuti con diversi scenari applicative. Infine alcune considerazioni concludono il lavoro.Imaging spectroscopy, also known as hyper-spectral remote sensing, is an imaging technique capable of identifying materials and objects in the air, land and water on the basis of the unique reflectance patterns that result from the interaction of solar energy with the molecular structure of the material. Recent advances in aerospace sensor technology have led to the development of instruments capable of collecting hundreds of images, with each image corresponding to narrow contiguous wavelength intervals, for the same area on the surface of the Earth. As a result, each pixel (vector) in the scene has an associated spectral signature or “fingerprint" that uniquely characterizes the underlying objects. Hyper-spectral sensors mainly cover wavelengths from the visible range (0.4_m- 0.7_m) to the middle infrared range (2.4_m). If we consider the consistency of this data, we can easily understand the importance of finding a method which can transform the data cube into one with reduced dimensionality and maintain, at the same time, as much information content as possible. These techniques are known under the general name of feature reduction. Besides enabling an easier storage and management of the data, features reduction procedures can be crucial for the implementation of optimum inversion algorithms. This research work strives to give a contribution along the direction of extracting information from hyperspectral data. A major instrument is considered for this purpose, which is the use of neural networks algorithms, already recognized to represent a rather competitive family of algorithms for the analysis of hyperspectral data. Besides introducing a novel neural network approach for handling the dimensionality reduction of hyperspectral data, other specific issues will be considered, with a special focus on the unmixing problem, or sub-pixel classification. While the first three chapters are dedicated to the presentation of the problems, to the current state of art and to the, theoretically sound, proposed solutions, the remaining sections are dedicated to the description and the assessment of the results obtained in different applicative scenarios. Some final considerations conclude the work