A Comparison of Nonlinear Mixing Models for Vegetated Areas Using Simulated and Real Hyperspectral Data

International audienceAbstract--Spectral unmixing (SU) is a crucial processing step when analyzing hyperspectral data. In such analysis, most of the work in the literature relies on the widely acknowledged linear mixing model to describe the observed pixels. Unfortunately, this model has been shown to be of limited interest for specific scenes, in particular when acquired over vegetated areas. Consequently, in the past few years, several nonlinear mixing models have been introduced to take nonlinear effects into account while performing SU. These models have been proposed empirically, however, without any thorough validation. In this paper, the authors take advantage of two sets of real and physical-based simulated data to validate the accuracy of various nonlinear models in vegetated areas. These physics-based models, and their corresponding unmixing algorithms, are evaluated with respect to their ability of fitting the measured spectra and providing an accurate estimation of the abundance coefficients, considered as the spatial distribution of the materials in each pixel

Nonlinear unmixing of vegetated areas: a model comparison based on simulated and real hyperspectral data

International audienceWhen analyzing remote sensing hyperspectral images, numerous works dealing with spectral unmixing assume the pixels result from linear combinations of the endmember signatures. However, this assumption cannot be fulfilled, in particular when considering images acquired over vegetated areas. As a consequence, several nonlinear mixing models have been recently derived to take various nonlinear effects into account when unmixing hyperspectral data. Unfortunately, these models have been empirically proposed and without thorough validation. This paper attempts to fill this gap by taking advantage of two sets of real and physical-based simulated data. Theaccuracy of various linear and nonlinear models and the corresponding unmixing algorithms is evaluated with respect to their ability of fitting the sensed pixels and of providing accurate estimates of the abundances

Remote sensing for the observation of senescence in Conference pear trees

Leaf senescence in trees is the phenological stage during which nutrient resorption happens. In this process, part of the nutrients is transferred to the perennial organs of the plant, contributing to tree vitality and, in pome fruit trees, to flowering intensity the following year. Another share of the nutrients remains inside leaf litter and enters the agroecosystem’s nutrient cycles. The timing and duration of leaf senescence influences the ratio between the two parts of nutrients and thus influences nutrient cycles in the agroecosystem. Among innovative techniques to investigate these processes, satellite remote sensing has proved a valid tool in natural ecosystems. The same cannot be said about fruit orchards, because of the image quality of the satellites active before Sentinel-2, often deemed insufficient for agricultural studies. The features of Sentinel-2, instead, offer new possibilities for monitoring phenology in agricultural environments. This research aims to study senescence in Conference pear trees, in three regions of Flanders (Belgium). One cloud-free Sentinel-2 image, acquired in the middle of the senescence period, was analysed, by means of different spectral indices. Ground data was collected through a network of 34 webcams with an RGB camera. A visual analysis was performed, to determine the beginning of senescence (the moment in which the first yellow/red leaves appear in the canopy) and the end of senescence (the moment in which the entire canopy turns yellow/red). Webcam data showed that leaf (dis)colouration started between September and October, during a one-month time span. Full discolouration of the canopy, occurring at the end of November, was instead more synchronous. Moreover, some trees only turned yellow, while others showed red leaves, probably a stress indicator. Sentinel-2 data revealed that spectral indices correlate well with the date of the beginning of senescence, thus suggesting that it would possible to map it. These results already offer evidence that monitoring variability in the dynamics of senescence is possible from satellite remote sensing. Current focus is on the link between canopy colour, as it appears in the webcam imagery, and satellite data

Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress

"© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” Upon publication, authors are asked to include either a link to the abstract of the published article in IEEE Xplore®, or the article’s Digital Object Identifier (DOI).Many applications require a timely acquisition of high spatial and spectral resolution remote sensing data. This is often not achievable since spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while airborne sensors mounted on a manned aircraft are too expensive to acquire a high temporal resolution. This gap between information needs and data availability inspires research on using Remotely Piloted Aircraft Systems (RPAS) to capture the desired high spectral and spatial information, furthermore providing temporal flexibility. Present hyperspectral imagers on board lightweight RPAS are still rare, due to the operational complexity, sensor weight, and instability. This paper looks into the use of a hyperspectral-hyperspatial fusion technique for an improved biophysical parameter retrieval and physiological assessment in agricultural crops. First, a biophysical parameter extraction study is performed on a simulated citrus orchard. Subsequently, the unmixing-based fusion is applied on a real test case in commercial citrus orchards with discontinuous canopies, in which a more efficient and accurate estimation of water stress is achieved by fusing thermal hyperspatial and hyperspectral (APEX) imagery. Narrowband reflectance indices that have proven their effectiveness as previsual indicators of water stress, such as the Photochemical Reflectance Index (PRI), show a significant increase in tree water-stress detection when applied on the fused dataset compared to the original hyperspectral APEX dataset (R-2 = 0.62, p 0.1). Maximal R-2 values of 0.93 and 0.86 are obtained by a linear relationship between the vegetation index and the resp., water and chlorophyll, parameter content maps.This work was supported in part by the Belgian Science Policy Office in the frame of the Stereo II program (Hypermix project-SR/00/141), in part by the project Chameleon of the Flemish Agency for Innovation by Science and Technology (IWT), and in part by the Spanish Ministry of Science and Education (MEC) for the projects AGL2012-40053-C03-01 and CONSOLIDER RIDECO (CSD2006-67). The European Facility for Airborne Research EUFAR (www.eufar.net) funded the flight campaign (Transnational Access Project 'Hyper-Stress'). The work of D. S. Intrigliolo was supported by the Spanish Ministry of Economy and Competitiveness program "Ramon y Cajal."Delalieux, S.; Zarco-Tejada, PJ.; Tits, L.; Jiménez Bello, MÁ.; Intrigliolo Molina, DS.; Somers, B. (2014). Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 7(6):2571-2582. https://doi.org/10.1109/JSTARS.2014.2330352S257125827

Nonlinear unmixing of vegetated areas: a model comparison based on simulated and real hyperspectral data

When analyzing remote sensing hyperspectral images, numerous works dealing with spectral unmixing assume the pixels result from linear combinations of the endmember signatures. However, this assumption cannot be fulfilled, in particular when considering images acquired over vegetated areas. As a consequence, several nonlinear mixing models have been recently derived to take various nonlinear effects into account when unmixing hyperspectral data. Unfortunately, these models have been empirically proposed and without thorough validation. This paper attempts to fill this gap by taking advantage of two sets of real and physical-based simulated data. The accuracy of various linear and nonlinear models and the corresponding unmixing algorithms is evaluated with respect to their ability of fitting the sensed pixels and of providing accurate estimates of the abundances

Amenability of groups and GG-sets

This text surveys classical and recent results in the field of amenability of groups, from a combinatorial standpoint. It has served as the support of courses at the University of G\"ottingen and the \'Ecole Normale Sup\'erieure. The goals of the text are (1) to be as self-contained as possible, so as to serve as a good introduction for newcomers to the field; (2) to stress the use of combinatorial tools, in collaboration with functional analysis, probability etc., with discrete groups in focus; (3) to consider from the beginning the more general notion of amenable actions; (4) to describe recent classes of examples, and in particular groups acting on Cantor sets and topological full groups

Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin

Front-line remote sensing tools, coupled with machine learning (ML), have a significant role in crop monitoring and disease surveillance. Crop type classification and a disease early warning system are some of these remote sensing applications that provide precise, timely, and cost-effective information at different spatial, temporal, and spectral resolutions. To our knowledge, most disease surveillance systems focus on a single-sensor based solutions and lagging the integration of multiple information sources. Moreover, monitoring larger landscapes using unmanned aerial vehicles (UAV) are challenging, and, therefore combining high resolution satellite imagery data with advanced machine learning (ML) models through the use of mobile apps could help detect and classify banana plants and provide more information on its overall health status. In this study, we classified banana under mixed-complex African landscapes through pixel-based classifications and ML models derived from multi-level satellite images (Sentinel 2, PlanetScope and WorldView-2) and UAV (MicaSense RedEdge) platforms. Our pixel-based classification from random forest (RF) model using combined features of vegetation indices (VIs) and principal component analysis (PCA) showed up to 97% overall accuracy (OA) with less than 10% omission and commission errors (OE and CE) and Kappa coefficient of 0.96 in high resolution multispectral images. We used UAV-RGB aerial images from DR Congo and Republic of Benin fields to develop a mixed-model system combining object detection model (RetinaNet) and a custom classifier for simultaneous banana localization and disease classification. Their accuracies were tested using different performance metrics. Our UAV-RGB mixed-model revealed that the developed object detection and classification model successfully classified healthy and diseased plants with 99.4%, 92.8%, 93.3% and 90.8% accuracy for the four classes: banana bunchy top disease (BBTD), Xanthomonas Wilt of Banana (BXW), healthy banana cluster and individual banana plants, respectively. These approaches of aerial image-based ML models have high potential to provide a decision support system for major banana diseases in Afric

Hyperspectral Signal Unmixing Techniques for Site Specific Plant State Monitoring

In precision farming, field management is based on observing and responding to intra-field variations. Hyperspectral remote sensing has shown great potential in providing timely and accurate information on the spatial variability of field- and plant conditions. However, due to the discontinuous open canopies typical of most (perennial) cropping systems, the size of the image pixels will exceed, in many cases, the size of the objects of interest. The reflectance signal of a pixel is thus the integrated result of spectral contributions of both the crop and non-crop components (i.e. soils, weeds and shadows) building up a pixel footprint. As a consequence, image interpretation and the extraction of the biophysical parameters from the measured hyperspectral signature is hampered. Accurate site-specific monitoring of the crop thus requires removing all the undesired background effects from a measured mixed pixel, resulting in a purified vegetation signature, which can be used to derive the desired information regarding the plant. To this end, Signal Unmixing (SU) methodologies are presented in this dissertation, deriving the pure spectral signature of the crop component on a per-pixel basis. The basis is Multiple Endmember Spectral Mixture Analysis (MESMA). Spectral libraries or Look-up Tables (LUTs) are used, i.e. a collection of spectra representing the possible reflectance values of the different endmembers. The MESMA algorithm is an iterative process that selects endmember combinations from spectral libraries, and the combination which results in the lowest reconstruction error of the mixed signal is selected as the best representation of the components present within the pixel. The selected signature can on its turn be used to derive the desired information regarding the crop s vigour status.In the first part of this work, solutions for the major bottlenecks of the MESMA methodology are presented. As the accuracy of MESMA is determined by the adequacy of the spectral library, it is crucial that the spectral library used for the unmixing of the image is representative of all endmembers present. An extensive tree LUT was thus created using a radiative transfer model, incorporating a high level of detail in the spectral signatures. However, variability in endmembers may lead to more than one pure spectrum combination resulting in the same mixture spectrum, a problem commonly referred to as ill-posedness. In Chapter 3, the integration of in situ measured soil moisture content into the SU model is therefore proposed to provide an estimation of the soil signature, as such reducing the number of possible solutions. This integration leads to a better extraction of the vegetation spectra, which on its turn results in an improved estimation of the trees vigour. Finally, the large size of the LUTs restricts the computational efficiency of the SU model. Incorporating geometric unmixing principles into MESMA enables a more efficient evaluation of all the different endmember combinations (Chapter 4). Whereas the traditional MESMA explores alldifferent endmember combinations separately, and selects the most appropriate combination as a final step, our approach selects the best endmember combination prior to unmixing, as such increasing the computational efficiency of MESMA.In addition to MESMA, two other unmixing methodologies are presented. Alternating Least Squares (ALS) unmixing is proposed as a Signal Unmixing methodology in Chapter 5. While MESMA requires extensive LUTs from which the most representative signal can be selected, ALS only needs an initial estimate of the spectral signature of each of the components present in the mixed pixel. This initial estimate is further optimised by ALS, and the pure spectral signature of the tree can thus be extracted from the mixed pixel signal. All the previous methodologies are tested on mixtures comprised of trees, soil and shadows. As the high spectral similarity between the trees and weeds hampers an accurate extraction of the tree signature, the performance of shape-based unmixing for separating spectrally similar endmembers is evaluated in Chapter 6. These insights can then be used to develop shape-based unmixing further into an SU model. Overall, this work provides a conceptual framework for the operational implementation of SU methodologies in a precision farming context. New methodologies are presented to extract the pure tree signature from hyperspectral mixed pixels, as well as to improve the computational efficiency and the accuracy of these methods. With the extracted tree signatures, an improved monitoring of the trees condition is achieved. SU thus provides a new avenue to explore the use of hyperspectral imagery in a precision farming context.nrpages: 151status: publishe

Automated visual fruit detection for harvest estimation and robotic harvesting

Fully automated detection and localisation of fruit in orchards is a key component in creating automated robotic harvesting systems, a dream of many farmers around the world to cope with large production and personnel costs. In recent years a lot of research on this topic has been performed, using basic computer vision techniques, like colour based segmentation, as a suggested solution. When not using standard RGB cameras, research tends to resort to other sensors, like hyper spectral or 3D. Recent advances in computer vision present a broad range of advanced object detection techniques that could improve the quality of fruit detection from RGB images drastically. We suggest to use a object categorisation technique based on a boosted cascade of weak classifiers to implement a fully automated semi-supervised fruit detector and demonstrate it on both strawberries and apples. Compared to existing techniques we improve fruit detection, mainly in the case of fruit clusters, using a supervised machine learning instead of hand crafting image filters specific to the application. Moreover we integrate application specific colour information to ensure a more stable output of our fully automated detection algorithm. The developed technique is validated on strawberries and apples and is proven to have large benefits in the field of automated harvest and crop estimation.status: publishe