Nonlinear unmixing of hyperspectral images: Models and algorithms

When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM). However, the LMM may be not valid, and other nonlinear models need to be considered, for instance, when there are multiscattering effects or intimate interactions. Consequently, over the last few years, several significant contributions have been proposed to overcome the limitations inherent in the LMM. In this article, we present an overview of recent advances in nonlinear unmixing modeling

Estimating the crop leaf area index using hyperspectral remote sensing

AbstractThe leaf area index (LAI) is an important vegetation parameter, which is used widely in many applications. Remote sensing techniques are known to be effective but inexpensive methods for estimating the LAI of crop canopies. During the last two decades, hyperspectral remote sensing has been employed increasingly for crop LAI estimation, which requires unique technical procedures compared with conventional multispectral data, such as denoising and dimension reduction. Thus, we provide a comprehensive and intensive overview of crop LAI estimation based on hyperspectral remote sensing techniques. First, we compare hyperspectral data and multispectral data by highlighting their potential and limitations in LAI estimation. Second, we categorize the approaches used for crop LAI estimation based on hyperspectral data into three types: approaches based on statistical models, physical models (i.e., canopy reflectance models), and hybrid inversions. We summarize and evaluate the theoretical basis and different methods employed by these approaches (e.g., the characteristic parameters of LAI, regression methods for constructing statistical predictive models, commonly applied physical models, and inversion strategies for physical models). Thus, numerous models and inversion strategies are organized in a clear conceptual framework. Moreover, we highlight the technical difficulties that may hinder crop LAI estimation, such as the “curse of dimensionality” and the ill-posed problem. Finally, we discuss the prospects for future research based on the previous studies described in this review

Utility of hyperspectral compared to multispectral remote sensing data in estimating forest biomass and structure variables in Finnish boreal forest

Three-quarters of Finland’s land surface area is filled with forests, which compose a great part of the country’s biomass, carbon pools and carbon sinks. In order to acquire up-to-date information on the forests, optical remote sensing techniques are commonly used. Moreover, in the future hyperspectral satellite missions will start providing data to support the needs of natural resource management practices, such as forestry. It is, however, unclear what would be the additional value from using hyperspectral data compared to multispectral in quantifying forest variables of Finnish boreal forest. In this study, we used the remote sensing data by hyperspectral AISA imager (128 bands, 400–1000 nm, resolution 0.7 m) and Sentinel-2 (10 bands, resolution 10 m) to assess the possible benefits of higher spectral resolution. As reference data, we used a new nationwide forest resource dataset (stand-level data), which has a high potential in further remote sensing applications. In addition, we used a set of independent in situ measurements (plot-level data) for validation. We applied two kernel-based machine learning regression algorithms (Gaussian process and support vector regression) to relate boreal forest variables with the remote sensing data. The variables of interest were mean height, basal area, leaf area index (LAI), stem biomass and main tree species. The regression algorithms were trained with stand-level data and estimations were evaluated with stand- and plot-level holdout sets. The estimation accuracies were examined with absolute and relative root-mean-square errors. Successful variable estimations showed that kernel-based regression algorithms are suitable tools for forest structure estimation. Based on the results, the additional value of hyperspectral remote sensing data in forest variable estimation in Finnish boreal forest is mainly related to variables with species-specific information, such as main tree species and LAI. The more interesting variables for forestry industry, such as mean height, basal area and stem biomass, can also be estimated accurately with more traditional multispectral remote sensing data.Peer reviewe

Algorithms for feature selection and pattern recognition on Grassmann manifolds

Includes bibliographical references.2015 Summer.This dissertation presents three distinct application-driven research projects united by ideas and topics from geometric data analysis, optimization, computational topology, and machine learning. We first consider hyperspectral band selection problem solved by using sparse support vector machines (SSVMs). A supervised embedded approach is proposed using the property of SSVMs to exhibit a model structure that includes a clearly identifiable gap between zero and non-zero feature vector weights that permits important bands to be definitively selected in conjunction with the classification problem. An SSVM is trained using bootstrap aggregating to obtain a sample of SSVM models to reduce variability in the band selection process. This preliminary sample approach for band selection is followed by a secondary band selection which involves retraining the SSVM to further reduce the set of bands retained. We propose and compare three adaptations of the SSVM band selection algorithm for the multiclass problem. We illustrate the performance of these methods on two benchmark hyperspectral data sets. Second, we propose an approach for capturing the signal variability in data using the framework of the Grassmann manifold (Grassmannian). Labeled points from each class are sampled and used to form abstract points on the Grassmannian. The resulting points have representations as orthonormal matrices and as such do not reside in Euclidean space in the usual sense. There are a variety of metrics which allow us to determine distance matrices that can be used to realize the Grassmannian as an embedding in Euclidean space. Multidimensional scaling (MDS) determines a low dimensional Euclidean embedding of the manifold, preserving or approximating the Grassmannian geometry based on the distance measure. We illustrate that we can achieve an isometric embedding of the Grassmann manifold using the chordal metric while this is not the case with other distances. However, non-isometric embeddings generated by using the smallest principal angle pseudometric on the Grassmannian lead to the best classification results: we observe that as the dimension of the Grassmannian grows, the accuracy of the classification grows to 100% in binary classification experiments. To build a classification model, we use SSVMs to perform simultaneous dimension selection. The resulting classifier selects a subset of dimensions of the embedding without loss in classification performance. Lastly, we present an application of persistent homology to the detection of chemical plumes in hyperspectral movies. The pixels of the raw hyperspectral data cubes are mapped to the geometric framework of the Grassmann manifold where they are analyzed, contrasting our approach with the more standard framework in Euclidean space. An advantage of this approach is that it allows the time slices in a hyperspectral movie to be collapsed to a sequence of points in such a way that some of the key structure within and between the slices is encoded by the points on the Grassmannian. This motivates the search for topological structure, associated with the evolution of the frames of a hyperspectral movie, within the corresponding points on the manifold. The proposed framework affords the processing of large data sets, such as the hyperspectral movies explored in this investigation, while retaining valuable discriminative information. For a particular choice of a distance metric on the Grassmannian, it is possible to generate topological signals that capture changes in the scene after a chemical release

Application of feature selection for predicting leaf chlorophyll content in oats (Avena sativa L.) from hyperspectral imagery

Feature selection can improve predictions generated by partial least squares models. In the context of hyperspectral imaging, it can also enable the development of affordable devices with specialized applications. The feasibility of feature selection for oat leaf chlorophyll estimation from hyperspectral imagery was assessed using a public domain dataset. A wrapper approach resulted in a simplistic model with poor predictive performance. The number of model inputs decreased from 94 to 3 bands when a filter approach based on the minimum redundancy, maximum relevance criterion was attempted. The filtering led to improved prediction quality, with the root mean square error decreasing from 0.17 to 0.16 g m-2 and R 2 increasing from 0.57 to 0.62. Accurate predictions were obtained especially for low chlorophyll levels. The obtained model estimated leaf chlorophyll concentration from near infra-red reflectance, canopy darkness, and its blueness. The prediction robustness needs to be investigated, which can be done by employing an ensemble methodology and testing the model on a new dataset with improved ground-truth measurements and additional crop species

Classification of Hyperspectral Image using SVM Post-Processing for Shape Preserving Filter and PCA

This paper is based on an experimentation to preserve shapes of the natural classes in a hyperspectral image post classification of the image using SVM. The classifier classifies the vegetation types present in the hyperspectral image and then estimates the crop types present in the image. In doing so it preserves the spatial shapes of the vegetation types spread in the image using an Edge-preserving filter. The shape-preserving filter was applied prior to dimension reduction where by the low information content spectral components are discarded using Principal Component Analysis. The classification of the features is performed using SVM. The result has been found very effective in characterizing significant spectral and spatial structures of objects in a scene.

TerraSenseTK: a toolkit for remote soil nutrient estimation

Intensive farming endangers soil quality in various ways. Researchers show that if these practices continue, humanity will be faced with food production issues. For this matter, Earth Observation, more concretely Soil Sensing, along with Machine Learning, can be employed to monitor several indicators of soil degradation, such as soil salinity, soil heavy metal contamination and soil nutrients estimation. More concretely, Soil Nutrients are of great importance. For instance, to understand which crop better suits the land, the soil nutrients must be identified. However, sampling soil is a laborous and expensive task, which can be leveraged by Remote Sensing and Machine Learning. Several studies have already been developed in this matter, although many gaps still exist. Among them, the lack of cross-dataset evaluations of existing algorithms, and also the steep learning curve to the Earth Observation domain that prevents many researchers from embracing this field. In this sense, we propose TerraSense ToolKit (TSTK), a python toolkit that addresses these challenges. In this work, the possibility to use Remote sensing along with Machine Learning algorithms to per form Soil Nutrient Estimation is explored, additionally, a nutrient estimation toolkit is proposed, and the effectivity of it is tested in a soil nutrient estimation case study. This toolkit is capable of simplifying Remote Sensing experiments and aims at reducing the barrier to entry to the field of Earth Observation. It comes with a preconfigured case study which implements a soil sensing pipeline. To evaluate the usability of the toolkit, experiments with five different crops were executed, namely with Wheat, Barley, Maize, Sunflower and Vineyards. This case study gave visibility to an underlying unbalanced data problem, which is not well addressed in the current State of the Art.A agricultura intensiva poe em perigo a qualidade do solo de v ˜ arias formas. Os investigadores ´ mostram que, se continuarmos com estas praticas, a humanidade ser ´ a confrontada com quest ´ oes de ˜ produc¸ao alimentar. Para este efeito, a Observac¸ ˜ ao da Terra, mais concretamente o Sensoriamento ˜ do Solo, juntamente com a aprendizagem automatica, podem ser utilizadas para monitorizar v ´ arios ´ indicadores da degradac¸ao do solo, tais como a salinidade do solo, a contaminac¸ ˜ ao do solo por metais ˜ pesados e a quantificac¸ao dos nutrientes do solo. Mais concretamente, os Nutrientes do Solo s ˜ ao de ˜ grande importancia. Por exemplo para compreender qual a cultura que melhor se adapta ao solo, os ˆ nutrientes do solo devem ser identificados. No entanto, a amostragem do solo e uma tarefa trabalhosa ´ e dispendiosa, que pode ser impulsionada pela percepc¸ao remota e pela aprendizagem autom ˜ atica. ´ Ja foram desenvolvidos v ´ arios estudos sobre este assunto, embora ainda existam muitas lacunas. ´ Entre eles, a falta de avaliac¸oes cruzadas dos algoritmos existentes, e tamb ˜ em a curva de aprendiza- ´ gem acentuada para o campo de Observac¸ao da Terra que impede muitos investigadores de enveredar ˜ por este campo. Neste sentido, propomos TSTK, um toolkit em python que aborda estes desafios. Neste trabalho, e explorada a possibilidade de usar a Percepc¸ ´ ao Remota juntamente com os algo- ˜ ritmos de Aprendizagem Automatica para realizar a Estimativa de Nutrientes do Solo. Al ´ em disso, ´ e´ proposto um toolkit de estimativa de nutrientes e tambem um pipeline para o devido efeito, a efetividade ´ do toolkit e testada num caso de estudo de Estimac¸ ´ ao de Nutrientes no Solo. ˜ Este toolkit e capaz de simplificar as experi ´ encias de Percepc¸ ˆ ao Remota e visa reduzir a barreira ˜ de entrada no campo da Observac¸ao da Terra. Para avaliar a usabilidade do toolkit, foram executadas ˜ experiencias com cinco culturas diferentes, nomeadamente Trigo, Cevada, Milho, Girassol e Vinha. Este ˆ caso de estudo deu visibilidade a um problema subjacente de dados desiquilibrados, o qual nao˜ e bem ´ identificado no Estado da Arte atual

Tree species classification from AVIRIS-NG hyperspectral imagery using convolutional neural networks

This study focuses on the automatic classification of tree species using a three-dimensional convolutional neural network (CNN) based on field-sampled ground reference data, a LiDAR point cloud and AVIRIS-NG airborne hyperspectral remote sensing imagery with 2 m spatial resolution acquired on 14 June 2021. I created a tree species map for my 10.4 km2 study area which is located in the Jurapark Aargau, a Swiss regional park of national interest. I collected ground reference data for six major tree species present in the study area (Quercus robur, Fagus sylvatica, Fraxinus excelsior, Pinus sylvestris, Tilia platyphyllos, total n = 331). To match the sampled ground reference to the AVIRIS-NG 425 band hyperspectral imagery, I delineated individual tree crowns (ITCs) from a canopy height model (CHM) based on LiDAR point cloud data. After matching the ground reference data to the hyperspectral imagery, I split the extracted image patches to training, validation, and testing subsets. The amount of training, validation and testing data was increased by applying image augmentation through rotating, flipping, and changing the brightness of the original input data. The classifier is a CNN trained on the first 32 principal components (PC’s) extracted from AVIRIS-NG data. The CNN uses image patches of 5 × 5 pixels and consists of two convolutional layers and two fully connected layers. The latter of which is responsible for the final classification using the softmax activation function. The results show that the CNN classifier outperforms comparable conventional classification methods. The CNN model is able to predict the correct tree species with an overall accuracy of 70% and an average F1-score of 0.67. A random forest classifier reached an overall accuracy of 67% and an average F1-score of 0.61 while a support-vector machine classified the tree species with an overall accuracy of 66% and an average F1-score of 0.62. This work highlights that CNNs based on imaging spectroscopy data can produce highly accurate high resolution tree species distribution maps based on a relatively small set of training data thanks to the high dimensionality of hyperspectral images and the ability of CNNs to utilize spatial and spectral features of the data. These maps provide valuable input for modelling the distributions of other plant and animal species and ecosystem services. In addition, this work illustrates the importance of direct collaboration with environmental practitioners to ensure user needs are met. This aspect will be evaluated further in future work by assessing how these products are used by environmental practitioners and as input for modelling purposes