Context dependent spectral unmixing.

A hyperspectral unmixing algorithm that finds multiple sets of endmembers is proposed. The algorithm, called Context Dependent Spectral Unmixing (CDSU), is a local approach that adapts the unmixing to different regions of the spectral space. It is based on a novel function that combines context identification and unmixing. This joint objective function models contexts as compact clusters and uses the linear mixing model as the basis for unmixing. Several variations of the CDSU, that provide additional desirable features, are also proposed. First, the Context Dependent Spectral unmixing using the Mahalanobis Distance (CDSUM) offers the advantage of identifying non-spherical clusters in the high dimensional spectral space. Second, the Cluster and Proportion Constrained Multi-Model Unmixing (CC-MMU and PC-MMU) algorithms use partial supervision information, in the form of cluster or proportion constraints, to guide the search process and narrow the space of possible solutions. The supervision information could be provided by an expert, generated by analyzing the consensus of multiple unmixing algorithms, or extracted from co-located data from a different sensor. Third, the Robust Context Dependent Spectral Unmixing (RCDSU) introduces possibilistic memberships into the objective function to reduce the effect of noise and outliers in the data. Finally, the Unsupervised Robust Context Dependent Spectral Unmixing (U-RCDSU) algorithm learns the optimal number of contexts in an unsupervised way. The performance of each algorithm is evaluated using synthetic and real data. We show that the proposed methods can identify meaningful and coherent contexts, and appropriate endmembers within each context. The second main contribution of this thesis is consensus unmixing. This approach exploits the diversity and similarity of the large number of existing unmixing algorithms to identify an accurate and consistent set of endmembers in the data. We run multiple unmixing algorithms using different parameters, and combine the resulting unmixing ensemble using consensus analysis. The extracted endmembers will be the ones that have a consensus among the multiple runs. The third main contribution consists of developing subpixel target detectors that rely on the proposed CDSU algorithms to adapt target detection algorithms to different contexts. A local detection statistic is computed for each context and then all scores are combined to yield a final detection score. The context dependent unmixing provides a better background description and limits target leakage, which are two essential properties for target detection algorithms

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

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Leaf spectral clusters as potential optical leaf functional types within California ecosystems

Our ability to measure and map plant function at multiple ecological scales is critical for understanding current and future changes in Earth's ecosystems and the global carbon budget. Conventional plant functional types (cPFTs) based on a few productivity-related traits have been previously used to simplify and represent major differences in global plant functions, but more recent research has directly focused on the use of functional trait information. Still, sampling limitations have constrained efforts to truly understand the variance and covariance of functional traits globally. Reflectance spectra offer a fast, repeatable, simultaneous measurement of a wide variety of leaf functional traits and could be used to optically define leaf functional types. To evaluate this concept, we measured leaf reflectance from a wide range of species in a diverse set of ecosystems across central and northern California, including observations from multiple individuals, sites, and seasons. Using principal components analysis, we analyzed spectral variation in relation to categorical attributes such as species and cPFTs, as well as to a set of functional trait metrics calculated from the spectra. We found the first three principal components (PCs) to be weakly related to categorical attributes and more strongly related to spectrally-derived functional metrics. Each PC was more strongly associated with different portions of the spectrum and contained different functional information. We applied a hybrid clustering algorithm to the PC coordinates of the observations to define potential optical leaf functional types. Twelve spectral clusters were identified, and these did not correspond directly to either single cPFTs or species. However, each cluster had a unique functional metric profile. Clusters represented both inter- and intra-species and cPFT functional differences driven by taxonomy, trait evolution and environmental responses, demonstrating their value as optical leaf functional types and the value of the clustering approach used here for defining optical types from leaf spectra. Our findings support the notion that cPFTs do not adequately capture differences in leaf function. They demonstrate that spectral measurements can be used to improve both the definition of PFTs as well as our knowledge regarding the covariance of functional traits within these classes

A comprehensive review of fruit and vegetable classification techniques

Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-of-the-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable

A Study on Change Detection in Hyperspectral Image

Change detection is the procedure of obtaining changes between two Hyperspectral pictures of same topographical zone taken at two unique times. It conveys the essential and important change data of a scene. Due to a breakthrough in Hyperspectral remote sensing Hyperspectral remote sensors can capable of producing narrow spectral resolution images. These high resolution spectral and spatial hyperspectral images can find small variations in images. This work describes an efficient algorithm for detecting changes in Hyperspectral images by using spectral signatures of Hyperspectral images. The objective is developing of a proficient algorithm that can show even small variations in Hyperspectral images. It reviews Hierarchical method for finding changes in Hyperspectral images by comparing spectral homogeneity between spectral change vectors. For any scenery locating and also exploration regarding adjust delivers treasured data regarding achievable changes. Hyperspectral satellite detectors get effectiveness throughout gathering data with large spectral rings. These types of detectors typically deal with spatially and also spectrally high definition graphics and this can be used by adjust discovery. This particular function is actually elaborated and also applied your adjust discovery procedure by simply controlling Hyperspectral graphics. The main aim with this thesis is actually studying and also constructing of Hyperspectral adjust discovery algorithms This kind of analysed approach is really applied to assess Hyperspectral picture image resolution files along with the approach analysed in this particular thesis is really change breakthrough making use of Hierarchical method of spectral change vectors and also making use of principal ingredient examination and also k-means clustering. This particular document offers applying and also verify of trends Hyperspectral image

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level

Techniques for automatic large scale change analysis of temporal multispectral imagery

Change detection in remotely sensed imagery is a multi-faceted problem with a wide variety of desired solutions. Automatic change detection and analysis to assist in the coverage of large areas at high resolution is a popular area of research in the remote sensing community. Beyond basic change detection, the analysis of change is essential to provide results that positively impact an image analyst\u27s job when examining potentially changed areas. Present change detection algorithms are geared toward low resolution imagery, and require analyst input to provide anything more than a simple pixel level map of the magnitude of change that has occurred. One major problem with this approach is that change occurs in such large volume at small spatial scales that a simple change map is no longer useful. This research strives to create an algorithm based on a set of metrics that performs a large area search for change in high resolution multispectral image sequences and utilizes a variety of methods to identify different types of change. Rather than simply mapping the magnitude of any change in the scene, the goal of this research is to create a useful display of the different types of change in the image. The techniques presented in this dissertation are used to interpret large area images and provide useful information to an analyst about small regions that have undergone specific types of change while retaining image context to make further manual interpretation easier. This analyst cueing to reduce information overload in a large area search environment will have an impact in the areas of disaster recovery, search and rescue situations, and land use surveys among others. By utilizing a feature based approach founded on applying existing statistical methods and new and existing topological methods to high resolution temporal multispectral imagery, a novel change detection methodology is produced that can automatically provide useful information about the change occurring in large area and high resolution image sequences. The change detection and analysis algorithm developed could be adapted to many potential image change scenarios to perform automatic large scale analysis of change