Deep Learning Approaches for Seagrass Detection in Multispectral Imagery

Seagrass forms the basis for critically important marine ecosystems. Seagrass is an important factor to balance marine ecological systems, and it is of great interest to monitor its distribution in different parts of the world. Remote sensing imagery is considered as an effective data modality based on which seagrass monitoring and quantification can be performed remotely. Traditionally, researchers utilized multispectral satellite images to map seagrass manually. Automatic machine learning techniques, especially deep learning algorithms, recently achieved state-of-the-art performances in many computer vision applications. This dissertation presents a set of deep learning models for seagrass detection in multispectral satellite images. It also introduces novel domain adaptation approaches to adapt the models for new locations and for temporal image series. In Chapter 3, I compare a deep capsule network (DCN) with a deep convolutional neural network (DCNN) for seagrass detection in high-resolution multispectral satellite images. These methods are tested on three satellite images in Florida coastal areas and obtain comparable performances. In addition, I also propose a few-shot deep learning strategy to transfer knowledge learned by DCN from one location to the others for seagrass detection. In Chapter 4, I develop a semi-supervised domain adaptation method to generalize a trained DCNN model to multiple locations for seagrass detection. First, the model utilizes a generative adversarial network (GAN) to align marginal distribution of data in the source domain to that in the target domain using unlabeled data from both domains. Second, it uses a few labeled samples from the target domain to align class-specific data distributions between the two. The model achieves the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods. In Chapter 5, I develop a semantic segmentation method for seagrass detection in multispectral time-series images. First, I train a state-of-the-art image segmentation method using an active learning approach where I use the DCNN classifier in the loop. Then, I develop an unsupervised domain adaptation (UDA) algorithm to detect seagrass across temporal images. I also extend our unsupervised domain adaptation work for seagrass detection across locations. In Chapter 6, I present an automated bathymetry estimation model based on multispectral satellite images. Bathymetry refers to the depth of the ocean floor and contributes a predominant role in identifying marine species in seawater. Accurate bathymetry information of coastal areas will facilitate seagrass detection by reducing false positives because seagrass usually do not grow beyond a certain depth. However, bathymetry information of most parts of the world is obsolete or missing. Traditional bathymetry measurement systems require extensive labor efforts. I utilize an ensemble machine learning-based approach to estimate bathymetry based on a few in-situ sonar measurements and evaluate the proposed model in three coastal locations in Florida

Flood Detection Using Multi-Modal and Multi-Temporal Images: A Comparative Study

Natural disasters such as flooding can severely affect human life and property. To provide rescue through an emergency response team, we need an accurate flooding assessment of the affected area after the event. Traditionally, it requires a lot of human resources to obtain an accurate estimation of a flooded area. In this paper, we compared several traditional machine-learning approaches for flood detection including multi-layer perceptron (MLP), support vector machine (SVM), deep convolutional neural network (DCNN) with recent domain adaptation-based approaches, based on a multi-modal and multi-temporal image dataset. Specifically, we used SPOT-5 and RADAR images from the flood event that occurred in November 2000 in Gloucester, UK. Experimental results show that the domain adaptation-based approach, semi-supervised domain adaptation (SSDA) with 20 labeled data samples, achieved slightly better values of the area under the precision-recall (PR) curve (AUC) of 0.9173 and F1 score of 0.8846 than those by traditional machine approaches. However, SSDA required much less labor for ground-truth labeling and should be recommended in practice

Semi-Supervised Adversarial Domain Adaptation for Seagrass Detection Using Multispectral Images in Coastal Areas

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods

Converting Optical Videos to Infrared Videos Using Attention GAN and Its Impact on Target Detection and Classification Performance

To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. The approach does not require paired images. The performance of the proposed attention GAN has been demonstrated using objective and subjective evaluations. Most importantly, the impact of attention GAN has been demonstrated in improved target detection and classification performance using real-infrared videos

Impact of Atmospheric Correction on Classification and Quantification of Seagrass Density from WorldView-2 Imagery

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors’ retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon (AGCseagrass) estimates. This study assessed atmospheric correction’s impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance (Lw), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction

Rapid Quantification of Biofouling With an Inexpensive, Underwater Camera and Image Analysis

To reduce the transport of potentially invasive species on ships\u27 submerged surfaces, rapid-and accurate-estimates of biofouling are needed so shipowners and regulators can effectively assess and manage biofouling. This pilot study developed a model approach for that task. First, photographic images were collected in situ with a submersible, inexpensive pocket camera. These images were used to develop image processing algorithms and train machine learning models to classify images containing natural assemblages of fouling organisms. All of the algorithms and models were implemented in a widely available software package (MATLAB©). Initially, an unsupervised clustering model was used, and three types of fouling were delineated. Using a supervised classification approach, however, seven types of fouling could be identified. In this manner, fouling was successfully quantified over time on experimental panels immersed in seawater. This work provides a model for the easy, quick, and cost-effective classification of biofouling

On Use of Independent Component Analysis for Ocular Artifacts Reduction of Electroencephalogram and While Using Kurtosis as the Threshold

Brain electrical activity commonly represented by the Electroencephalogram (EEG), due to its miniscule amplitude (on the order of a hundred microvolts), is often contaminated with various artifacts. Independent Component Analysis (ICA) may be a useful technique to reduce some artifacts prior analyzing EEG. Present report discusses use of kurtosis to determine the threshold for detecting the artifacts-related independent components. Kurtosis may represent how peaked or how flat the artifacts that affect a signal are compared to the normal behavior of the original signal. Two statistical approaches were used for the kurtosis-based threshold selection: the Z-score and the confidence interval. The independent components determined as artifact-related may be either set to zero for the greater artifact suppression or scaled down for the reduced effect on the artifact-free regions of EEG. Based on the observed results, we may conclude that the present technique may be used for ocular artifacts reduction in EEG

Creation of an Internet of Things (IoT) system for the live and remote monitoring of solar photovoltaic facilities

Reliable and widely accepted, renewable energy sources stand as the optimal substitute for fossil fuels in meeting our growing energy demands. Specifically, solar energy can be harnessed into electrical power through solar cells. Many solar installations are situated in remote locations like rooftops, mountains, and deserts. Effective monitoring of these solar photovoltaic systems is crucial to maximize their performance. Monitoring solutions come in various configurations. The simplest approach involves offline data collection, entailing the placement of a data collection device on the installation which then stores data locally for a specific timeframe. However, this conventional monitoring method falls short in providing real-time data. In contrast, leveraging Internet of Things (IoT) technology to oversee solar photovoltaic power generation offers a substantial performance boost. This project aims to develop an IoT-powered system for real-time remote monitoring of solar photovoltaic installations. The collected data is stored in the IoT cloud, accessible through an application via an active internet connection from anywhere worldwide. This grants immediate insights into the installation’s status, facilitating maintenance and prompt fault detection

A comparative performance analysis of zero voltage switching class e and selected enhanced class E inverters

This paper presents a comparative analysis of the class E and selected enhanced class E inverters, namely, the second and third harmonic group of class EFn, E/Fn and the class E Flat Top inverter. The inverters are designed under identical specifications and evaluated against the variation of switching frequency (f), duty ratio (D), capacitance ratio (k), and the load resistance (RL). To offer a comparative understanding, the performance parameters, namely, the power output capability, efficiency, peak switch voltage and current, peak resonant capacitor voltages, and the peak current in the lumped network, are determined quantitatively. It is found that the class EF2 and E/F3 inverters, in general, have higher efficiency and comparable power output capability with respect to the class E inverter. More specifically, the class EF2 (parallel LC and in series to the load network) and E/F3 (parallel LC and in series to the load network) maintain 90% efficiency compared to 80% for class E inverter at the optimum operating condition. Furthermore, the peak switch voltage and current in these inverters are on average 20–30% lower than the class E and other versions for k > 1. The analysis also shows that the class EF2 and E/F3 inverters should be operated in the stretch of 1 k D = 0.3–0.6 at the optimum load to sustain the high-performance standard