81 research outputs found
Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering
[EN] Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a yperspectral
image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.This work was supported by Spanish Administration (Ministerio de Ciencia, Innovacion y Universidades) and European Union (FEDER) under grant TEC2017-84743-P.Prades Nebot, J.; Safont Armero, G.; Salazar Afanador, A.; Vergara DomĂnguez, L. (2020). Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering. Remote Sensing. 12(21):1-22. https://doi.org/10.3390/rs12213585S122122
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
Hyperspectral benthic mapping from underwater robotic platforms
We live on a planet of vast oceans; 70% of the Earth's surface is covered in water. They are integral to supporting life, providing 99% of the inhabitable space on Earth. Our oceans and the habitats within them are under threat due to a variety of factors. To understand the impacts and possible solutions, the monitoring of marine habitats is critically important. Optical imaging as a method for monitoring can provide a vast array of information however imaging through water is complex. To compensate for the selective attenuation of light in water, this thesis presents a novel light propagation model and illustrates how it can improve optical imaging performance. An in-situ hyperspectral system is designed which comprised of two upward looking spectrometers at different positions in the water column. The downwelling light in the water column is continuously sampled by the system which allows for the generation of a dynamic water model. In addition to the two upward looking spectrometers the in-situ system contains an imaging module which can be used for imaging of the seafloor. It consists of a hyperspectral sensor and a trichromatic stereo camera. New calibration methods are presented for the spatial and spectral co-registration of the two optical sensors. The water model is used to create image data which is invariant to the changing optical properties of the water and changing environmental conditions. In this thesis the in-situ optical system is mounted onboard an Autonomous Underwater Vehicle. Data from the imaging module is also used to classify seafloor materials. The classified seafloor patches are integrated into a high resolution 3D benthic map of the surveyed site. Given the limited imaging resolution of the hyperspectral sensor used in this work, a new method is also presented that uses information from the co-registered colour images to inform a new spectral unmixing method to resolve subpixel materials
Identification of urban surface materials using high-resolution hyperspectral aerial imagery
La connaissance des matériaux de surface est essentielle pour l’aménagement et la gestion des
villes. Avec les avancées en télédétection, particulièrement en imagerie de haute résolution spatiale
et spectrale, l’identification et la cartographie détaillée des matériaux de surface en milieu urbain
sont maintenant envisageables. Les signatures spectrales décrivent les interactions entre les objets
au sol et le rayonnement solaire, et elles sont supposées uniques pour chaque type de matériau de
surface.
Dans ce projet de recherche nous avons utilisé des images hyperspectrales aériennes du capteur
CASI, avec une résolution de 1 m2 et 96 bandes contigües entre 380nm et 1040nm. Ces images
couvrant l’île de Montréal (QC, Canada), acquises en 2016, ont été analysées pour identifier les
matériaux de surfaces.
Pour atteindre ces objectifs, notre méthode d’analyse est fondée sur la comparaison des signatures
spectrales d’un pixel quelconque à celles des objets typiques contenues dans des bibliothèques
spectrales (matériaux inertes et végétation). Pour mesurer la correspondance entre la signature
spectrale d’un objet et la signature spectrale de référence nous avons utilisé deux métriques. La
première métrique tient compte de la forme d’une signature spectrale et la seconde, de la différence
des valeurs de réflectance entre la signature spectrale observée et celle de référence. Un
classificateur flou utilisant ces deux métriques est alors appliqué afin de reconnaître le type de
matériau de surface sur la base du pixel. Des signatures spectrales typiques ont été extraites des
deux librairies spectrales (ASTER et HYPERCUBE). Des signatures spectrales des objets typiques
à Montréal mesurées sur le terrain (spectroradiomètre ASD) ont été aussi utilisées comme
références.
Trois grandes catégories de matériaux ont été identifiées dans les images pour faciliter la
comparaison entre les classifications par source de références spectrales : l’asphalte, le béton et la
végétation. La classification utilisant ASTER comme bibliothèque de référence a eu le plus grand
taux de réussite avec 92%, suivi par ASD à 88% et finalement HYPERCUBE avec 80%. Nous
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n’avons pas trouvé de différences significatives entre les trois résultats, ce qui indique que la
classification est indépendante de la source des signatures spectrales de référence.Knowledge of surface cover materials is crucial for urban planning and management. With
advances in remote sensing, especially in high spatial and spectral resolution imagery, the
identification and detailed mapping of surface materials in urban areas based on spectral signatures
are now feasible. Spectral signatures describe the interactions between ground objects and solar
radiation and are assumed unique for each type of material.
In this research, we use airborne CASI images with 1 m2 spatial resolution, with 96 contiguous
bands in a spectral range between 367 nm and 1044 nm. These images covering the island of
Montreal (Quebec, Canada), obtained in 2016, were analyzed to identify urban surface materials.
The objectives of the project were first to find a correspondence between the physical and chemical
characteristic of typical surface materials, present in the Montreal scenes, and the spectral
signatures within the images. Second, to develop a sound methodology for identifying these
surface materials in urban landscapes.
To reach these objectives, our method of analysis is based on a comparison of pixel spectral
signatures to those contained in a reference spectral library that describe typical surface covering
materials (inert materials and vegetation). Two metrics were used in order to measure the
correspondence of pixel spectral signatures and reference spectral signature. The first metric
considers the shape of a spectral signature and the second the difference of reflectance values
between the observed and reference spectral signature. A fuzzy classifier using these two metrics
is then applied to recognize the type of material on a pixel basis. Typical spectral signatures were
extracted from two spectral libraries (ASTER and HYPERCUBE). Spectral signatures of typical
objects in Montreal measured on the ground (ASD spectroradiometer) were also used as reference
spectra. Three general types of surface materials (asphalt, concrete, and vegetation) were used to
ease the comparison between classifications using these spectral libraries. The classification using
ASTER as a reference library had the highest success rate reaching 92%, followed by the field
spectra at 88%, and finally with HYPERCUBE at 80%. There were no significant differences in
the classification results indicating that the methodology works independently of the source of
reference spectral signatures
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