Multi-source data fusion in the context of automatic target recognition (ATR) involves the fusion of multiple, independent observations of a phenomenon. If the collection of sources is diverse, the resulting classification system is expected to perform better than one based on any one source. In recent work, the authors have demonstrated the use of such decision fusion strategies in alleviating the over-dimensionality and small-sample-size problems associated with hyperspectral data. Multi-temporal hyperspectral recognition and classification tasks are even more prone to over-dimensionality of features and small training sample size problems. In this work, the authors will extend their previously proposed framework to multi-temporal, hyperspectral target recognition / classification problems. The performance of the proposed system will be compared against that of conventional hyperspectral feature extraction techniques. The efficacy of the proposed system is quantified by overall recognition accuracies.

A Robust Multi-Classifier Decision Fusion Framework for Hyperspectral, Multi-Temporal Classification

Invasive plants are a threat to natural ecosystems. Sri Lanka's rich biodiversity is severely affected by the uncontrollable spread of this foreign flora. The authorities involved in invasive plant management face various challenges when identifying, monitoring, and as well as forecasting the spread of invasive plants. Another challenge faced by the field members is the difficulty in communication during the eradication process. The proposed solution is a software platform that aims to solve the above problems using mobile and web-based technologies. The mobile application is capable of identifying invasive plants, mapping their geographical location, and off-the-grid communication. The web application provides services to monitor and forecast the potential spread of harmful non-native plant species.

Invasive Plant Detection and Management Platform

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/pdf/application-of-hyperspectral-remote-sensing-in-detecting-and-27dyrdun3h.pdf

Application of hyperspectral remote sensing in detecting and mapping Sericea lespedeza in Missouri

Two classes of methods are designed for extracting features from spectro-temporal reflectance maps. Methods designed for these two approaches include various stepwise selection methods, windowing, and clustering techniques. The first class of methods is based on the consideration that all the elements of the spectro-temporal map are independent of each other (Mathur et al., 2006a). The second class of methods is based on the consideration that the elements of the spectro-temporal map have some vicinal dependency among them (Mathur et al., 2006b). Various data analyses are performed to evaluate the accuracies of the proposed methods. These include sub-sampling the original data at different rates in both spectral and temporal dimensions and then extracting features. Another set of analysis is done on data simulated according to various satellite and airborne sensor profiles. The efficacies of the new methods are demonstrated within an aquatic invasive species detection application, namely discriminating Waterhyacinth from other aquatic vegetation such as American Lotus.

Identification of pertinent regions in spectro-temporal maps for vegetative target detection

Atmospheric data sets are represented by an amount of heterogeneous and redundant data. As number of measurements grows, a strategy is needed to select and efficiently analyze the useful information from the whole data set. The aim of this work is to propose a feature extraction technique based on construction of clusters of similar features. The main objective of the proposed process is to attempt to reach a more accurate classification task and to achieve a more compact representation of the underlying structure of the data. The paper reports the results obtained using the above extraction and analysis procedure of a real data set on atmospheric pollution. It is shown that the proposed approach is able to detect underlying relationship between features and thus get to ameliorate classification accuracy rate.

Feature extraction for atmospheric pollution detection

This paper presents a feature extraction method for exploiting hyperspectral hypertemporal data and applies the new method to the problem of invasive species detection. By definition, hyperspectral hypertemporal imagery is very high dimensional data, and dimensionality reduction will play a critical role in utilizing such data. We present a feature extraction method that takes advantage of the high correlation among elements of the spectral and temporal feature space. This high correlation can be attributed to the premise that the changes in the reflectance of closely spaced wavelengths do not always change dramatically over short periods of time. The proposed feature clustering method is based on the assumption that adjacent elements in the spectro-temporal feature space are highly correlated and can be grouped together to form lower- dimensional feature spaces. The proposed hyperspectral hypertemporal feature clustering method is tested and validated within an invasive vegetation detection application. The hypothesis is that as time progresses, the spectral response of different plant species change differently. Thus, there should be hyperspectral hypertemporal features that can be used to discriminate between the vegetative species. Additionally, the results of the feature clustering method can be used to determine which regions of the spectrum and which collection dates are optimum for the given invasives detection problem.

Exploiting Hyperspectral Hypertemporal Imagery with Feature Clustering for Invasive Species Detection

There is a need to identify and extract the most useful information from spectral and temporal features, where usefulness is measured in terms of signal classification or target detection. For this reason, instead of using the entire spectral and temporal feature space, pertinent features are extracted to reduce dimensionality. The classification accuracy increases if the distributions of the classes are statistically more separate in the feature space. One method to measure the ability of a feature to discriminate between two classes is to calculate the area under the feature's receiver operating characteristics (ROC) curves. To estimate the classification capabilities of the different spectro-temporal features, ROC areas were calculated for each feature in the spectro-temporal feature vector for the two-class system. An algorithm was then designed and implemented to obtain the best combination of the individual features to serve as a "best feature". Once the best features were determined, the system was tested to estimate the accuracy of target detection.

Automated Stepwise Selection of Hyperspectral Hypertemporal Features for Target Detection

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Exploiting Hyperspectral Hypertemporal Imagery with Feature Clustering for Invasive Species Detection

Automated Stepwise Selection of Hyperspectral Hypertemporal Features for Target Detection