Experimental Study of Hierarchical Clustering for Unmixing of Hyperspectral Images

[EN] Estimation of the number of materials that are present in a hyperspectral image is a necessary step in many hyperspectral image processing algorithms, including classification and unmixing. Previously, we presented an algorithm that estimated the number of materials in the image using clustering principles. This algorithm is an iterative approach with two input parameters: the initial number of materials (P0) and the number of materials added in each iteration (¿). Since the choice of P0 and ¿ can have a large impact on the estimation accuracy. In this paper, we made an experimental study of the effect of these parameters on the algorithm performance. Thus, we show that the choice of a large ¿ can significantly reduce the estimation accuracy. These results can help to make an appropriate choice of these two parameters.This research has been supported by Generalitat Valenciana, grant PROMETEO 2019/109.Prades Nebot, J.; Salazar Afanador, A.; Safont, G.; Vergara Domínguez, L. (2021). Experimental Study of Hierarchical Clustering for Unmixing of Hyperspectral Images. IEEE. 1-5. https://doi.org/10.1109/ICARES53960.2021.96652011

Implementation strategies for hyperspectral unmixing using Bayesian source separation

Bayesian Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing. Moreover, it is sensible to impose a sum-to-one (full additivity) constraint to the estimated source abundances in each pixel. Even though non-negativity and full additivity are two necessary properties to get physically interpretable results, the use of BPSS algorithms has been so far limited by high computation time and large memory requirements due to the Markov chain Monte Carlo calculations. An implementation strategy which allows one to apply these algorithms on a full hyperspectral image, as typical in Earth and Planetary Science, is introduced. Effects of pixel selection, the impact of such sampling on the relevance of the estimated component spectra and abundance maps, as well as on the computation times, are discussed. For that purpose, two different dataset have been used: a synthetic one and a real hyperspectral image from Mars.Comment: 10 pages, 6 figures, submitted to IEEE Transactions on Geoscience and Remote Sensing in the special issue on Hyperspectral Image and Signal Processing (WHISPERS

Spectral unmixing approach in hyperspectral remote sensing: a tool for oil palm mapping

Las plantaciones de palma de aceite típicamente abarcan grandes áreas, por esto, la teledetección remota se ha convertido en una herramienta útil para el monitoreo avanzado de este cultivo. Este trabajo revisa y evalúa dos enfoques para analizar las plantaciones de palma de aceite a partir de datos de teledetección remota hiperespectral: desmezclado espectral lineal y variabilidad espectral. Además, se propone un marco computacional basado en el desmezclado espectral para la estimación de las fracciones de abundancias de cultivos de palma de aceite. Este enfoque también considera la variabilidad espectral de las firmas en las imágenes hiperespectrales. El marco computacional propuesto modifica el modelo de mezcla lineal mediante la introducción de un vector de pesos, de manera que se puedan identificar las bandas espectrales que menos contribuyen a la estimación de fracciones de abundancias erróneas. Este enfoque aprovecha la detección de los árboles de palma de aceite, ya que permite diferenciarlos de otros materiales en términos de fracciones de abundancia. Los resultados experimentales obtenidos a partir de datos de teledetección remota hiperespectral en el rango de 410-990 nm, muestran mejoras de un 8.18 % en la métrica de Precisión del Usuario (Uacc) en la identificación de palmas de aceite por el marco propuesto con respecto a los métodos tradicionales de desmezclado espectral; el método propuesto logró un 95 % de Uacc. Esto confirma las capacidades del marco computacional formulado y facilita la gestión y el monitoreo de grandes áreas de plantaciones de palma de aceite.Oil palm plantations typically span large areas; therefore, remote sensing has become a useful tool for advanced oil palm monitoring. This work reviews and evaluates two approaches to analyze oil palm plantations based on hyperspectral remote sensing data: linear spectral unmixing and spectral variability. Moreover, a computational framework based on spectral unmixing for the estimation of fractional abundances of oil palm plantations is proposed in this study. Such approach also considers the spectral variability of hyperspectral image signatures. More specifically, the proposed computational framework modifies the linear mixing model by introducing a weighting vector, so that the spectral bands that contribute the least to the estimation of erroneous fractional abundances can be identified. This approach improves palm detection as it allows to differentiate them from other materials in terms of fractional abundances. Experimental results obtained from hyperspectral remote sensing data in the range 410-990 nm show improvements of 8.18 % in User Accuracy (Uacc) in the identification of oil palms by the proposed framework with respect to traditional unmixing methods. Thus, the proposed method achieved a 95% Uacc. This confirms the capabilities of the proposed computational framework and facilitates the management and monitoring of large areas of oil palm plantations

Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image

Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction algorithms can be employed for recovering the spectral signatures while abundances are estimated using an inversion step. Recent works have shown that exploiting spatial dependencies between image pixels can improve spectral unmixing. Markov random fields (MRF) are classically used to model these spatial correlations and partition the image into multiple classes with homogeneous abundances. This paper proposes to define the MRF sites using similarity regions. These regions are built using a self-complementary area filter that stems from the morphological theory. This kind of filter divides the original image into flat zones where the underlying pixels have the same spectral values. Once the MRF has been clearly established, a hierarchical Bayesian algorithm is proposed to estimate the abundances, the class labels, the noise variance, and the corresponding hyperparameters. A hybrid Gibbs sampler is constructed to generate samples according to the corresponding posterior distribution of the unknown parameters and hyperparameters. Simulations conducted on synthetic and real AVIRIS data demonstrate the good performance of the algorithm

Implementation of webcam-based hyperspectral imaging system

In the present work, a hyperspectral imaging system (imaging spectrometer) using a commercial webcam has been designed and developed. This system was able to capture two-dimensional spectra (in emission, transmission and reflection modes) directly from the scene in the desired wavelengths. Imaging of the object is done directly by linear sweep (pushbroom method). To do so, the spectrometer is equipped with a suitable collecting lens and a linear travel stage. A 1920 x 1080 pixel CMOS webcam was used as a detector. The spectrometer has been calibrated by the reference spectral lines of standard lamps. The spectral resolution of this system was about 2nm and its spatial resolution was about 1 mm for a 10 cm long object. The hardware solution is based on data acquisition working on the USB platform and controlled by a LabVIEW program. In this system, the initial output was a three-dimensional matrix in which two dimensions of the matrix were related to the spatial information of the object and the third dimension was the spectrum of any point of the object. Finally, the images in different wavelengths were created by reforming the data of the matrix. The free spectral range (FSR) of the system was 400 to 1100 nm. The system was successfully tested for some applications, such as plasma diagnosis as well as applications in food and agriculture sciences