Quantifying Seagrass Distribution in Coastal Water With Deep Learning Models

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations

Using deep learning for land classification within the konza prairie, 1985 – 2011

Machine learning has been around for decades, but deep learning is the new focus of study within machine learning. The goals of implementing deep learning into remote sensing are resulting in much faster and more accurate results for much larger datasets. The field of remote sensing has focused on increasing the accuracy of land classification. A possible solution for increasing accuracy is the use of convolutional neural networks. The goals of this study were to determine whether convolutional neural networks can be used on moderate-resolution imagery to accurately classify land. The study site of focus was the Konza Prairie in Geary County, Kansas. The image data are from Landsat 4 and 5 spanning the years 1985-2011. The Konza was split into 4 x 4-pixel size fishnet of cells that were classified as either burnt or non-burnt. To better examine the convolutional neural network, it was compared to machine learning and other neural network models. The machine learning models explored were logistic regression, k-nearest neighbor, decision tree, and linear support vector machine. The neural networks implemented included the basic neural network, shallow neural network, flatten time window neural network, convolutional neural network, and deep convolutional neural network. The results show that the k-nearest neighbor produce the highest overall accuracy compared to all the machine learning and neural networks but consist of high errors of omission proving that the classification is not represented accurately. The deep convolutional neural network has the best results for classifying burnt and non-burnt cells and low errors of omission and commission, which best represents the classification

Work In Progress: Safety and Robustness Verification of Autoencoder-Based Regression Models using the NNV Tool

This work in progress paper introduces robustness verification for autoencoder-based regression neural network (NN) models, following state-of-the-art approaches for robustness verification of image classification NNs. Despite the ongoing progress in developing verification methods for safety and robustness in various deep neural networks (DNNs), robustness checking of autoencoder models has not yet been considered. We explore this open space of research and check ways to bridge the gap between existing DNN verification methods by extending existing robustness analysis methods for such autoencoder networks. While classification models using autoencoders work more or less similar to image classification NNs, the functionality of regression models is distinctly different. We introduce two definitions of robustness evaluation metrics for autoencoder-based regression models, specifically the percentage robustness and un-robustness grade. We also modified the existing Imagestar approach, adjusting the variables to take care of the specific input types for regression networks. The approach is implemented as an extension of NNV, then applied and evaluated on a dataset, with a case study experiment shown using the same dataset. As per the authors' understanding, this work in progress paper is the first to show possible reachability analysis of autoencoder-based NNs.Comment: In Proceedings SNR 2021, arXiv:2207.0439