Spectral Angle Based Unary Energy Functions for Spatial-Spectral Hyperspectral Classification Using Markov Random Fields

In this paper, we propose and compare two spectral angle based approaches for spatial-spectral classification. Our methods use the spectral angle to generate unary energies in a grid-structured Markov random field defined over the pixel labels of a hyperspectral image. The first approach is to use the exponential spectral angle mapper (ESAM) kernel/covariance function, a spectral angle based function, with the support vector machine and the Gaussian process classifier. The second approach is to directly use the minimum spectral angle between the test pixel and the training pixels as the unary energy. We compare the proposed methods with the state-of-the-art Markov random field methods that use support vector machines and Gaussian processes with squared exponential kernel/covariance function. In our experiments with two datasets, it is seen that using minimum spectral angle as unary energy produces better or comparable results to the existing methods at a smaller running time

Multitask Learning of Vegetation Biochemistry from Hyperspectral Data

Statistical models have been successful in accurately estimating the biochemical contents of vegetation from the reflectance spectra. However, their performance deteriorates when there is a scarcity of sizable amount of ground truth data for modeling the complex non-linear relationship occurring between the spectrum and the biochemical quantity. We propose a novel Gaussian process based multitask learning method for improving the prediction of a biochemical through the transfer of knowledge from the learned models for predicting related biochemicals. This method is most advantageous when there are few ground truth data for the biochemical of interest, but plenty of ground truth data for related biochemicals. The proposed multitask Gaussian process hypothesizes that the inter-relationship between the biochemical quantities is better modeled by using a combination of two or more covariance functions and inter-task correlation matrices. In the experiments, our method outperformed the current methods on two real-world datasets

Transfer Learning for High Resolution Aerial Image Classification

With rapid developments in satellite and sensor technologies, increasing amount of high spatial resolution aerial images have become available. Classification of these images are important for many remote sensing image understanding tasks, such as image retrieval and object detection. Meanwhile, image classification in the computer vision field is revolutionized with recent popularity of the convolutional neural networks (CNN), based on which the state-of-the-art classification results are achieved. Therefore, the idea of applying the CNN for high resolution aerial image classification is straightforward. However, it is not trivial mainly because the amount of labeled images in remote sensing for training a deep neural network is limited. As a result, transfer learning techniques were adopted for this problem, where the CNN used for the classification problem is pre-trained on a larger dataset beforehand. In this paper, we propose a specific fine-tuning strategy that results in better CNN models for aerial image classification. Extensive experiments were carried out using the proposed approach with different CNN architectures. Our proposed method shows competitive results compared to the existing approaches, indicating the superiority of the proposed fine-tuning algorith

Online Data-Driven Safety Certification for Systems Subject to Unknown Disturbances

Deploying autonomous systems in safety critical settings necessitates methods to verify their safety properties. This is challenging because real-world systems may be subject to disturbances that affect their performance, but are unknown a priori. This work develops a safety-verification strategy wherein data is collected online and incorporated into a reachability analysis approach to check in real-time that the system avoids dangerous regions of the state space. Specifically, we employ an optimization-based moving horizon estimator (MHE) to characterize the disturbance affecting the system, which is incorporated into an online reachability calculation. Reachable sets are calculated using a computational graph analysis tool to predict the possible future states of the system and verify that they satisfy safety constraints. We include theoretical arguments proving our approach generates reachable sets that bound the future states of the system, as well as numerical results demonstrating how it can be used for safety verification. Finally, we present results from hardware experiments demonstrating our approach's ability to perform online reachability calculations for an unmanned surface vehicle subject to currents and actuator failures.Comment: 6 pages, 7 figure

Conditional Random Fields for Rock Characterization Using Drill Measurements

Analysis of drill performance data provides a power-ful method for estimating subsurface geology. While there have been studies relating such measurement-while-drilling (MWD) parameters to rock properties, none of them has at-tempted to model context, that is, to associate local mea-surements with measurements obtained in neighbouring re-gions. This paper proposes a novel approach to infer geol-ogy from drill measurements by incorporating spatial re-lationships through a Conditional Random Field (CRF) framework. A boosting algorithm is used as a local classi-fier mapping drill measurements to corresponding geologi-cal categories. The CRF then uses this local information in conjunction with neighbouring measurements to jointly rea-son about their categories. Model parameters are learned from training data by maximizing the pseudo-likelihood. The probability distribution of classified borehole sections is calculated using belief propagation. We present exper-imental results of applying the method to MWD data col-lected from a semi-autonomous drill rig at an iron ore mine in Western Australia. 1

Gaussian Processes for Object Detection in High Resolution Remote Sensing Images

Object detection in high resolution remote sensing images is a crucial yet challenging problem for many applications. With the development of satellite and sensor technologies, remote sensing images attain very high spatial resolution, giving rise to the employment of many computer vision algorithms. Therefore, the object detection is usually formalized as a supervised classification task. In this paper, we propose to apply the Gaussian process (GP) classification algorithm for our detection problem. Among different classifiers, the GP classifier is a Bayesian classification method that is able to make estimations in a probabilistic way. To demonstrate the performance of the proposed approach, we experiment the proposed framework with different feature extraction schemes and classification methods. We carry out a cross-validation experiment over an image dataset that consists of objects and non-objects to train an object detector, and apply the trained detector in an unobserved image scene to search for the objects of interest. Our results show that the GP classifier is competitive to support vector machines (SVM), which is considered stateof-the-art