Blind source separation is a common processing tool to analyse the constitution of pixels of hyperspectral images. Such methods usually suppose that pure pixel spectra (endmembers) are the same in all the image for each class of materials. In the framework of remote sensing, such an assumption is no longer valid in the presence of intra-class variability due to illumination conditions, weathering, slight variations of the pure materials, etc. In this paper, we first describe the results of investigations highlighting intra-class variability measured in real images. Considering these results, a new formulation of the linear mixing model is presented leading to two new methods. Unconstrained pixel-by-pixel NMF (UP-NMF) is a new blind source separation method based on the assumption of a linear mixing model, which can deal with intra-class variability. To overcome the limitations of UP-NMF, an extended method is also proposed, named Inertia-constrained Pixel-by-pixel NMF (IP-NMF). For each sensed spectrum, these extended versions of NMF extract a corresponding set of source spectra. A constraint is set to limit the spreading of each source’s estimates in IP-NMF. The proposed methods are first tested on a semi-synthetic data set built with spectra extracted from a real hyperspectral image and then numerically mixed. We thus demonstrate the interest of our methods for realistic source variabilities. Finally, IP-NMF is tested on a real data set and it is shown to yield better performance than state of the art methods.

Oil extraction and transportation may lead to small or large scale accidental spills, whether at sea or on land. Detecting these spills is a major problem that can be addressed by means of hyperspectral images and specific processing methods. In this work, several cases of onshore oil spills are studied. First, a controlled experiment was carried out: four boxes containing soil or sand mixed with crude oil or gasoil were deployed on the ONERA site near Fauga, France, and were overflown by HySpex hyperspectral cameras. Owing to this controlled experiment, different detection strategies were developed and tested, with a particular focus on the most automated methods requiring the least supervision. The methods developed were then applied to two very different cases: mapping of the shoreline contaminated due to the explosion of the Deepwater Horizon (DWH) platform based on AVIRIS images (AVIRIS: Airborne Visible/InfraRed Imaging Spectrometer), and detection of a tar pit on a former oil exploration site. The detection strategy depends on the type of oil, light or heavy, recently or formerly spilled, and on the substrate. In the first case (controlled experiment), the proposed methods included spectral index calculations, anomaly detection and spectral unmixing. In the case of DWH, spectral indices were computed and the unmixing method was tested. Finally, to detect the tar pit, a strategy based on anomaly detection and spectral indices was applied. In all the cases studied, the proposed methods were successful in detecting and mapping the oil pollution.

TRUST (Thermal Remote sensing Unmixing for Subpixel Temperature) is a spectral unmixing method developed to provide subpixel abundances and temperatures from radiance images in the thermal domain. By now, this method has been studied in simple study cases, with a low number of endmembers, high spatial resolutions (1 m) and more than 30 spectral bands in the thermal domain. Thus, this article aims to show the applicability of TRUST on a highly challenging study case: the analysis of a heterogeneous urban environment with airborne multispectral (eight thermal bands) images at 8-m resolution. Thus, this study is necessary to generalize the use of TRUST in the analysis of urban thermography. Since TRUST allows linking intrapixel temperatures to specific materials, it appears as a very useful tool to characterize Surface Urban Heat Islands and its dynamics at high spatial resolutions. Moreover, this article presents an improved version of TRUST, called TRUST-DNS (Day and Night Synergy), which takes advantage of daytime and nighttime acquisitions to improve the unmixing performances. In this study, both TRUST and TRUST-DNS were applied on daytime and nighttime airborne thermal images acquired over the center of Madrid during the DESIREX (Dual-use European Security IR Experiment) campaign in 2008. The processed images were obtained with the Aircraft Hyperspectral Scanner (AHS) sensor at 4-m spatial resolution on 4 July. TRUST-DNS appears to be more stable and slightly outperforms TRUST on both day and night images. In addition, TRUST applied on daytime outperforms TRUST on nighttime, illustrating the importance of the temperature contrasts during day for thermal unmixing.

A classification method of hyperspectral reflectance images named CHRIPS (Classification of Hyperspectral Reflectance Images with Physical and Statistical criteria) is presented. This method aims at classifying each pixel from a given set of thirteen classes: unidentified dark surface, water, plastic matter, carbonate, clay, vegetation (dark green, dense green, sparse green, stressed), house roof/tile, asphalt, vehicle/paint/metal surface and non-carbonated gravel. Each class is characterized by physical criteria (detection of specific absorptions or shape features) or statistical criteria (use of dedicated spectral indices) over spectral reflectance. CHRIPS input is a hyperspectral reflectance image covering the spectral range [400–2500 nm]. The presented method has four advantages, namely: (i) is robust in transfer, class identification is based on criteria that are not very sensitive to sensor type; (ii) does not require training, criteria are pre-defined; (iii) includes a reject class, this class reduces misclassifications; (iv) high precision and recall, F ${}_1$ score is generally above 0.9 in our test. As the number of classes is limited, CHRIPS could be used in combination with other classification algorithms able to process the reject class in order to decrease the number of unclassified pixels.

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.

The composition of clay minerals in soils, and more particularly the presence of montmorillonite (as part of the smectite family), is a key factor in soil swell–shrinking as well as off–road vehicle mobility. Detecting these topsoil clay minerals and quantifying the montmorillonite abundance are a challenge since they are usually intimately mixed with other minerals, soil organic carbon and soil moisture content. Imaging spectroscopy coupled with unmixing methods can address these issues, but the quality of the estimation degrades the coarser the spatial resolution is due to pixel heterogeneity. With the advent of UAV-borne and proximal hyperspectral acquisitions, it is now possible to acquire images at a centimeter scale. Thus, the objective of this paper is to evaluate the accuracy and limitations of unmixing methods to retrieve montmorillonite abundance from very-high-resolution hyperspectral images (1.5 cm) acquired from a camera installed on top of a bucket truck over three different agricultural fields, in Loiret department, France. Two automatic endmember detection methods based on the assumption that materials are linearly mixed, namely the Simplex Identification via Split Augmented Lagrangian (SISAL) and the Minimum Volume Constrained Non-negative Matrix Factorization (MVC-NMF), were tested prior to unmixing. Then, two linear unmixing methods, the fully constrained least square method (FCLS) and the multiple endmember spectral mixture analysis (MESMA), and two nonlinear unmixing ones, the generalized bilinear method (GBM) and the multi-linear model (MLM), were performed on the images. In addition, several spectral preprocessings coupled with these unmixing methods were applied in order to improve the performances. Results showed that our selected automatic endmember detection methods were not suitable in this context. However, unmixing methods with endmembers taken from available spectral libraries performed successfully. The nonlinear method, MLM, without prior spectral preprocessing or with the application of the first Savitzky–Golay derivative, gave the best accuracies for montmorillonite abundance estimation using the USGS library (RMSE between 2.2–13.3% and 1.4–19.7%). Furthermore, a significant impact on the abundance estimations at this scale was in majority due to (i) the high variability of the soil composition, (ii) the soil roughness inducing large variations of the illumination conditions and multiple surface scatterings and (iii) multiple volume scatterings coming from the intimate mixture. Finally, these results offer a new opportunity for mapping expansive soils from imaging spectroscopy at very high spatial resolution.