Robust Linear Spectral Unmixing using Anomaly Detection

This paper presents a Bayesian algorithm for linear spectral unmixing of hyperspectral images that accounts for anomalies present in the data. The model proposed assumes that the pixel reflectances are linear mixtures of unknown endmembers, corrupted by an additional nonlinear term modelling anomalies and additive Gaussian noise. A Markov random field is used for anomaly detection based on the spatial and spectral structures of the anomalies. This allows outliers to be identified in particular regions and wavelengths of the data cube. A Bayesian algorithm is proposed to estimate the parameters involved in the model yielding a joint linear unmixing and anomaly detection algorithm. Simulations conducted with synthetic and real hyperspectral images demonstrate the accuracy of the proposed unmixing and outlier detection strategy for the analysis of hyperspectral images

Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector

Hyperspectral monitoring of green roof vegetation health state in sub-mediterranean climate: preliminary results

In urban and industrial environments, the constant increase of impermeable surfaces has produced drastic changes in the natural hydrological cycle. Decreasing green areas not only produce negative effects from a hydrological-hydraulic perspective, but also from an energy point of view, modifying the urban microclimate and generating, as shown in the literature, heat islands in our cities. In this context, green infrastructures may represent an environmental compensation action that can be used to re-equilibrate the hydrological and energy balance and reduce the impact of pollutant load on receiving water bodies. To ensure that a green infrastructure will work properly, vegetated areas have to be continuously monitored to verify their health state. This paper presents a ground spectroscopy monitoring survey of a green roof installed at the University of Calabria fulfilled via the acquisition and analysis of hyperspectral data. This study is part of a larger research project financed by European Structural funds aimed at understanding the influence of green roofs on rainwater management and energy consumption for air conditioning in the Mediterranean area. Reflectance values were acquired with a field-portable spectroradiometer that operates in the range of wavelengths 350–2500 nm. The survey was carried out during the time period November 2014–June 2015 and data were acquired weekly. Climatic, thermo-physical, hydrological and hydraulic quantities were acquired as well and related to spectral data. Broadband and narrowband spectral indices, related to chlorophyll content and to chlorophyll–carotenoid ratio, were computed. The two narrowband indices NDVI705 and SIPI turned out to be the most representative indices to detect the plant health status

A novel semi-fragile forensic watermarking scheme for remote sensing images

Peer-reviewedA semi-fragile watermarking scheme for multiple band images is presented. We propose to embed a mark into remote sensing images applying a tree structured vector quantization approach to the pixel signatures, instead of processing each band separately. The signature of themmultispectral or hyperspectral image is used to embed the mark in it order to detect any significant modification of the original image. The image is segmented into threedimensional blocks and a tree structured vector quantizer is built for each block. These trees are manipulated using an iterative algorithm until the resulting block satisfies a required criterion which establishes the embedded mark. The method is shown to be able to preserve the mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their position in the whole image.Se presenta un esquema de marcas de agua semi-frágiles para múltiples imágenes de banda. Proponemos incorporar una marca en imágenes de detección remota, aplicando un enfoque de cuantización del vector de árbol estructurado con las definiciones de píxel, en lugar de procesar cada banda por separado. La firma de la imagen hiperespectral se utiliza para insertar la marca en el mismo orden para detectar cualquier modificación significativa de la imagen original. La imagen es segmentada en bloques tridimensionales y un cuantificador de vector de estructura de árbol se construye para cada bloque. Estos árboles son manipulados utilizando un algoritmo iteractivo hasta que el bloque resultante satisface un criterio necesario que establece la marca incrustada. El método se muestra para poder preservar la marca bajo compresión con pérdida (por encima de un umbral establecido) pero, al mismo tiempo, detecta posiblemente bloques forjados y su posición en la imagen entera.Es presenta un esquema de marques d'aigua semi-fràgils per a múltiples imatges de banda. Proposem incorporar una marca en imatges de detecció remota, aplicant un enfocament de quantització del vector d'arbre estructurat amb les definicions de píxel, en lloc de processar cada banda per separat. La signatura de la imatge hiperespectral s'utilitza per inserir la marca en el mateix ordre per detectar qualsevol modificació significativa de la imatge original. La imatge és segmentada en blocs tridimensionals i un quantificador de vector d'estructura d'arbre es construeix per a cada bloc. Aquests arbres són manipulats utilitzant un algoritme iteractiu fins que el bloc resultant satisfà un criteri necessari que estableix la marca incrustada. El mètode es mostra per poder preservar la marca sota compressió amb pèrdua (per sobre d'un llindar establert) però, al mateix temps, detecta possiblement blocs forjats i la seva posició en la imatge sencera

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Mapping olive varieties and within-field spatial variability using high resolution QuickBird imagery

[Abstract]: The growth of the Australian olive (Olea europaea L.) industry requires support from research to ensure its profitability and sustainability. To contribute to this goal, our project tested the ability of remote sensing imagery to map olive groves and their attributes. Specifically, this study aimed to: (a) discriminate olives varieties; and to (b) detect and interpret within-field spatial variability. Using high spatial resolution (2.8m) QuickBird multispectral imagery acquired over Yallamundi (southeast Queensland) on 24 December 2003, both visual interpretation and statistical (divergence) measures were employed to discriminate olive varieties. Similarly, the detection and interpretation of within-field spatial variability was conducted on enhanced false colour composite imagery, and confirmed by the use of statistical methods. Results showed that the two olive varieties (i.e. Kalamata and Frantoio) can be visually differentiated and mapped on the enhanced image based on texture. The spectral signature plots showed little difference in the mean spectral reflectance values, indicating that the two varieties have a very low spectral separability. In terms of within-field spatial variability, the presence or absence of Rhodes grass (Chloris gayana) was detected using visual interpretation, corroborated by the results of quantitative statistical measures. Spatial variability in soil properties, caused by the presence of a patch of sandy soil, was also detected visually. Finally, the “imprint” of former cover-type or land-use prior to olive plantation establishment in 1998 was identified. More work is being done to develop image classification techniques for mapping within-field spatial variability in olive varieties, biomass and condition using hyperspectral image data, as well as interpreting the cause of observed variability

Ground-based hyperspectral analysis of the urban nightscape

Airborne hyperspectral cameras provide the basic information to estimate the energy wasted skywards by outdoor lighting systems, as well as to locate and identify their sources. However, a complete characterization of the urban light pollution levels also requires evaluating these effects from the city dwellers standpoint, e.g. the energy waste associated to the excessive illuminance on walls and pavements, light trespass, or the luminance distributions causing potential glare, to mention but a few. On the other hand, the spectral irradiance at the entrance of the human eye is the primary input to evaluate the possible health effects associated with the exposure to artificial light at night, according to the more recent models available in the literature. In this work we demonstrate the possibility of using a hyperspectral imager (routinely used in airborne campaigns) to measure the ground-level spectral radiance of the urban nightscape and to retrieve several magnitudes of interest for light pollution studies. We also present the preliminary results from a field campaign carried out in the downtown of Barcelona.Postprint (author's final draft