Advances in Hyperspectral Image Classification Methods for Vegetation and Agricultural Cropland Studies

Hyperspectral data are becoming more widely available via sensors on airborne and unmanned aerial vehicle (UAV) platforms, as well as proximal platforms. While space-based hyperspectral data continue to be limited in availability, multiple spaceborne Earth-observing missions on traditional platforms are scheduled for launch, and companies are experimenting with small satellites for constellations to observe the Earth, as well as for planetary missions. Land cover mapping via classification is one of the most important applications of hyperspectral remote sensing and will increase in significance as time series of imagery are more readily available. However, while the narrow bands of hyperspectral data provide new opportunities for chemistry-based modeling and mapping, challenges remain. Hyperspectral data are high dimensional, and many bands are highly correlated or irrelevant for a given classification problem. For supervised classification methods, the quantity of training data is typically limited relative to the dimension of the input space. The resulting Hughes phenomenon, often referred to as the curse of dimensionality, increases potential for unstable parameter estimates, overfitting, and poor generalization of classifiers. This is particularly problematic for parametric approaches such as Gaussian maximum likelihoodbased classifiers that have been the backbone of pixel-based multispectral classification methods. This issue has motivated investigation of alternatives, including regularization of the class covariance matrices, ensembles of weak classifiers, development of feature selection and extraction methods, adoption of nonparametric classifiers, and exploration of methods to exploit unlabeled samples via semi-supervised and active learning. Data sets are also quite large, motivating computationally efficient algorithms and implementations. This chapter provides an overview of the recent advances in classification methods for mapping vegetation using hyperspectral data. Three data sets that are used in the hyperspectral classification literature (e.g., Botswana Hyperion satellite data and AVIRIS airborne data over both Kennedy Space Center and Indian Pines) are described in Section 3.2 and used to illustrate methods described in the chapter. An additional high-resolution hyperspectral data set acquired by a SpecTIR sensor on an airborne platform over the Indian Pines area is included to exemplify the use of new deep learning approaches, and a multiplatform example of airborne hyperspectral data is provided to demonstrate transfer learning in hyperspectral image classification. Classical approaches for supervised and unsupervised feature selection and extraction are reviewed in Section 3.3. In particular, nonlinearities exhibited in hyperspectral imagery have motivated development of nonlinear feature extraction methods in manifold learning, which are outlined in Section 3.3.1.4. Spatial context is also important in classification of both natural vegetation with complex textural patterns and large agricultural fields with significant local variability within fields. Approaches to exploit spatial features at both the pixel level (e.g., co-occurrencebased texture and extended morphological attribute profiles [EMAPs]) and integration of segmentation approaches (e.g., HSeg) are discussed in this context in Section 3.3.2. Recently, classification methods that leverage nonparametric methods originating in the machine learning community have grown in popularity. An overview of both widely used and newly emerging approaches, including support vector machines (SVMs), Gaussian mixture models, and deep learning based on convolutional neural networks is provided in Section 3.4. Strategies to exploit unlabeled samples, including active learning and metric learning, which combine feature extraction and augmentation of the pool of training samples in an active learning framework, are outlined in Section 3.5. Integration of image segmentation with classification to accommodate spatial coherence typically observed in vegetation is also explored, including as an integrated active learning system. Exploitation of multisensor strategies for augmenting the pool of training samples is investigated via a transfer learning framework in Section 3.5.1.2. Finally, we look to the future, considering opportunities soon to be provided by new paradigms, as hyperspectral sensing is becoming common at multiple scales from ground-based and airborne autonomous vehicles to manned aircraft and space-based platforms

Introducing artificial data generation in active learning for land use/land cover classification

Fonseca, J., Douzas, G., & Bacao, F. (2021). Increasing the effectiveness of active learning: Introducing artificial data generation in active learning for land use/land cover classification. Remote Sensing, 13(13), 1-20. [2619]. https://doi.org/10.3390/rs13132619In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite its effectiveness, it is still significantly reliant on user interaction, which makes it both expensive and time consuming to implement. Most of the current literature focuses on the optimization of AL by modifying the selection criteria and the classifiers used. Although improvements in these areas will result in more effective data collection, the use of artificial data sources to reduce human–computer interaction remains unexplored. In this paper, we introduce a new component to the typical AL framework, the data generator, a source of artificial data to reduce the amount of user-labeled data required in AL. The implementation of the proposed AL framework is done using Geometric SMOTE as the data generator. We compare the new AL framework to the original one using similar acquisition functions and classifiers over three AL-specific performance metrics in seven benchmark datasets. We show that this modification of the AL framework significantly reduces cost and time requirements for a successful AL implementation in all of the datasets used in the experiment.publishersversionpublishe

Environmental Indicators for the Coastal Region of the U.S. Great Lakes

The goal of this research collaboration was to develop indicators that both estimate environmental condition and suggest plausible causes of ecosystem degradation in the coastal region of the U.S. Great Lakes. The collaboration consisted of 8 broad components, each of which generated different types of environmental responses and characteristics of the coastal region. These indicators included biotic communities of amphibians, birds, diatoms, fish, macroinvertebrates, and wetland plants as well as indicators of polycyclic aromatic hydrocarbon (PAH) photo-induced toxicity and landscape characterization. These components are summarized below and discussed in more detailed in 5 separate reports (Section II). Stress gradients within the U.S. Great Lakes coastal region were defined from 207 variables (e.g., agriculture, atmospheric deposition, land use/land cover, human populations, point source pollution, and shoreline modification) from 19 different data sources that were publicly available for the coastal region. Biotic communities along these gradients were sampled with a stratified, random design among representative ecosystems within the coastal zone. To achieve the sampling across this massive area, the coastal region was subdivided into 2 major ecological provinces and further subdivided into 762 segment sheds. Stress gradients were defined for the major categories of human-induced disturbance in the coastal region and an overall stress index was calculated which represented a combination of all the stress gradients. Investigators of this collaboration have had extensive interactions with the Great Lakes community. For instance, the Lake Erie Lakewide Area Management Plan (LAMP) has adopted many of the stressor measures as integral indicators of the condition of watersheds tributary to Lake Erie. Furthermore, the conceptual approach and applications for development of a generalized stressor gradient have been incorporated into a document defining the tiered aquatic life criteria for defining biological integrity of the nation’s waters. A total of 14 indicators of the U.S. Great Lakes coastal region are presented for potential application. Each indicator is summarized with respect to its use, methodology, spatial context, and diagnosis capability. In general, the results indicate that stress related to agricultural activity and human population density/development had the largest impacts on the biotic community indicators. In contrast, the photoinduced PAH indicator was primarily related to industrial activity in the U.S. Great Lakes, and over half of the sites sampled were potentially at risk of PAH toxicity to larval fish. One of the indicators developed for land use/land change was developed from Landsat imagery for the entire U.S. Great Lakes basin and for the period from 1992 to 2001. This indicator quantified the extensive conversions of both agricultural and forest land to residential area that has occurred during a short 9 year period. Considerable variation in the responses were manifest at different spatial scales and many at surprisingly large scales. Significant advances were made with respect to development of methods for identifying and testing environmental indicators. In addition, many indicators and concepts developed from this project are being incorporated into management plans and U.S. 8 EPA methods documents. Further details, downloadable documents, and updates on these indicators can be found at the GLEI website - http://glei.nrri.umn.edu

Assessment and Monitoring of Deltaic Wetlands and Fluvial Systems: Refining and Validating a Multimetric Index of Resaca Ecosystem Health

Resacas are ancient distributary channels of the lower Rio Grande River historically serving as floodwater drainage pathways onto the Laguna Madre, Texas. Resacas provide numerous ecosystem services and critical habitat for many species. Mainly influenced by urbanization, integrating a multimetric index for assessing and monitoring resaca ecosystem health is required for adaptive management. The Resaca Health Index (RHI) integrates structural and functional components: (1) nonredundant metrics describing fish communities (Fish Community Index); (2) Trophic State Index derived from Secchi depth; (3) decay constant from riparian vegetation leaf litter decomposing in the water column; and (4) a riparian quality index based on vegetation cover, structure, and channel alterations to assess the riparian habitat which directly influences adjacent aquatic environments. The RHI was applied to 11 resacas, correctly ranking them in accordance with known disturbances; making the RHI a tool that may facilitate holistic, science-based solutions for conservation and restoration of resacas

A survey on big multimedia data processing and management in smart cities

© 2019 Association for Computing Machinery. All rights reserved. Integration of embedded multimedia devices with powerful computing platforms, e.g., machine learning platforms, helps to build smart cities and transforms the concept of Internet of Things into Internet of Multimedia Things (IoMT). To provide different services to the residents of smart cities, the IoMT technology generates big multimedia data. The management of big multimedia data is a challenging task for IoMT technology. Without proper management, it is hard to maintain consistency, reusability, and reconcilability of generated big multimedia data in smart cities. Various machine learning techniques can be used for automatic classification of raw multimedia data and to allow machines to learn features and perform specific tasks. In this survey, we focus on various machine learning platforms that can be used to process and manage big multimedia data generated by different applications in smart cities. We also highlight various limitations and research challenges that need to be considered when processing big multimedia data in real-time

The Role of Synthetic Data in Improving Supervised Learning Methods: The Case of Land Use/Land Cover Classification

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information ManagementIn remote sensing, Land Use/Land Cover (LULC) maps constitute important assets for various applications, promoting environmental sustainability and good resource management. Although, their production continues to be a challenging task. There are various factors that contribute towards the difficulty of generating accurate, timely updated LULC maps, both via automatic or photo-interpreted LULC mapping. Data preprocessing, being a crucial step for any Machine Learning task, is particularly important in the remote sensing domain due to the overwhelming amount of raw, unlabeled data continuously gathered from multiple remote sensing missions. However a significant part of the state-of-the-art focuses on scenarios with full access to labeled training data with relatively balanced class distributions. This thesis focuses on the challenges found in automatic LULC classification tasks, specifically in data preprocessing tasks. We focus on the development of novel Active Learning (AL) and imbalanced learning techniques, to improve ML performance in situations with limited training data and/or the existence of rare classes. We also show that much of the contributions presented are not only successful in remote sensing problems, but also in various other multidisciplinary classification problems. The work presented in this thesis used open access datasets to test the contributions made in imbalanced learning and AL. All the data pulling, preprocessing and experiments are made available at https://github.com/joaopfonseca/publications. The algorithmic implementations are made available in the Python package ml-research at https://github.com/joaopfonseca/ml-research