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1. Classification and Feature Extraction for Hydraulic Structures Data Using Advanced CNN Architectures

   https://doi.org/10.1109/TransAI51903.2021.00032  

   Talafha, Sameerah ; Wu, Di ; Rekabdar, Banafsheh ; Li, Ruopu ; Wang, Guangxing ( September 2021 , 2021 Third International Conference on Transdisciplinary AI (TransAI))

   An efficient feature selection method can significantly boost results in classification problems. Despite ongoing improvement, hand-designed methods often fail to extract features capturing high- and mid-level representations at effective levels. In machine learning (Deep Learning), recent developments have improved upon these hand-designed methods by utilizing automatic extraction of features. Specifically, Convolutional Neural Networks (CNNs) are a highly successful technique for image classification which can automatically extract features, with ongoing learning and classification of these features. The purpose of this study is to detect hydraulic structures (i.e., bridges and culverts) that are important to overland flow modeling and environmental applications. The dataset used in this work is a relatively small dataset derived from 1-m LiDAR-derived Digital Elevation Models (DEMs) and National Agriculture Imagery Program (NAIP) aerial imagery. The classes for our experiment consist of two groups: the ones with a bridge/culvert being present are considered "True", and those without a bridge/culvert are considered "False". In this paper, we use advanced CNN techniques, including Siamese Neural Networks (SNNs), Capsule Networks (CapsNets), and Graph Convolutional Networks (GCNs), to classify samples with similar topographic and spectral characteristics, an objective which is challenging utilizing traditional machine learning techniques, such as Support Vector Machine (SVM), Gaussian Classifier (GC), and Gaussian Mixture Model (GMM). The advanced CNN-based approaches combined with data pre-processing techniques (e.g., data augmenting) produced superior results. These approaches provide efficient, cost-effective, and innovative solutions to the identification of hydraulic structures. 

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2. Characterizing robustness and sensitivity of convolutional neural networks for quantitative analysis of mitochondrial morphology

   https://doi.org/10.1007/s40484-018-0156-3  

   Chai, Xiaoqi ; Ba, Qinle ; Yang, Ge ( December 2018 , Quantitative Biology)

   Quantitative analysis of mitochondrial morphology plays important roles in studies of mitochondrial biology. The analysis depends critically on segmentation of mitochondria, the image analysis process of extracting mitochondrial morphology from images. The main goal of this study is to characterize the performance of convolutional neural networks (CNNs) in segmentation of mitochondria from fluorescence microscopy images. Recently, CNNs have achieved remarkable success in challenging image segmentation tasks in several disciplines. So far, however, our knowledge of their performance in segmenting biological images remains limited. In particular, we know little about their robustness, which defines their capability of segmenting biological images of different conditions, and their sensitivity, which defines their capability of detecting subtle morphological changes of biological objects.

   We have developed a method that uses realistic synthetic images of different conditions to characterize the robustness and sensitivity of CNNs in segmentation of mitochondria. Using this method, we compared performance of two widely adopted CNNs: the fully convolutional network (FCN) and the U‐Net. We further compared the two networks against the adaptive active‐mask (AAM) algorithm, a representative of high‐performance conventional segmentation algorithms.

   The FCN and the U‐Net consistently outperformed the AAM in accuracy, robustness, and sensitivity, often by a significant margin. The U‐Net provided overall the best performance.

   Our study demonstrates superior performance of the U‐Net and the FCN in segmentation of mitochondria. It also provides quantitative measurements of the robustness and sensitivity of these networks that are essential to their applications in quantitative analysis of mitochondrial morphology.

    

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3. A Data-Driven Approach to Classifying Wave Breaking in Infrared Imagery

   https://doi.org/10.3390/rs11070859  

   Buscombe, Daniel ; Carini, Roxanne J. ( April 2019 , Remote Sensing)

   null (Ed.)

   We apply deep convolutional neural networks (CNNs) to estimate wave breaking type (e.g., non-breaking, spilling, plunging) from close-range monochrome infrared imagery of the surf zone. Image features are extracted using six popular CNN architectures developed for generic image feature extraction. Logistic regression on these features is then used to classify breaker type. The six CNN-based models are compared without and with augmentation, a process that creates larger training datasets using random image transformations. The simplest model performs optimally, achieving average classification accuracies of 89% and 93%, without and with image augmentation respectively. Without augmentation, average classification accuracies vary substantially with CNN model. With augmentation, sensitivity to model choice is minimized. A class activation analysis reveals the relative importance of image features to a given classification. During its passage, the front face and crest of a spilling breaker are more important than the back face. For a plunging breaker, the crest and back face of the wave are most important, which suggests that CNN-based models utilize the distinctive ‘streak’ temperature patterns observed on the back face of plunging breakers for classification. 

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4. A METHOD TO GENERATE FLOOD MAPS IN 3D USING DEM AND DEEP LEARNING

   https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-25-2020  

   Gebrehiwot, A. ; Hashemi-Beni, L. ( January 2020 , ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   null (Ed.)

   Abstract. High-resolution remote sensing imagery has been increasingly used for flood applications. Different methods have been proposed for flood extent mapping from creating water index to image classification from high-resolution data. Among these methods, deep learning methods have shown promising results for flood extent extraction; however, these two-dimensional (2D) image classification methods cannot directly provide water level measurements. This paper presents an integrated approach to extract the flood extent in three-dimensional (3D) from UAV data by integrating 2D deep learning-based flood map and 3D cloud point extracted from a Structure from Motion (SFM) method. We fine-tuned a pretrained Visual Geometry Group 16 (VGG-16) based fully convolutional model to create a 2D inundation map. The 2D classified map was overlaid on the SfM-based 3D point cloud to create a 3D flood map. The floodwater depth was estimated by subtracting a pre-flood Digital Elevation Model (DEM) from the SfM-based DEM. The results show that the proposed method is efficient in creating a 3D flood extent map to support emergency response and recovery activates during a flood event. 

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5. BNU- Net: a Novel Deep Learning Approach for LV MRI Analysis in Short-Axis MRI

   Chu, Wenhui ; Molina, Giovanni ; Navkar, Nikhil ; Eick, Christoph ; Becker, Aaron ; Tsekos, Nikolaos ( October 2019 , IEEE International Conference on Bioinformatics and Bioengineering (BIBE))

   This work presents a novel deep learning architecture called BNU-Net for the purpose of cardiac segmentation based on short-axis MRI images. Its name is derived from the Batch Normalized (BN) U-Net architecture for medical image segmentation. New generations of deep neural networks (NN) are called convolutional NN (CNN). CNNs like U-Net have been widely used for image classification tasks. CNNs are supervised training models which are trained to learn hierarchies of features automatically and robustly perform classification. Our architecture consists of an encoding path for feature extraction and a decoding path that enables precise localization. We compare this approach with a parallel approach named U-Net. Both BNU-Net and U-Net are cardiac segmentation approaches: while BNU-Net employs batch normalization to the results of each convolutional layer and applies an exponential linear unit (ELU) approach that operates as activation function, U-Net does not apply batch normalization and is based on Rectified Linear Units (ReLU). The presented work (i) facilitates various image preprocessing techniques, which includes affine transformations and elastic deformations, and (ii) segments the preprocessed images using the new deep learning architecture. We evaluate our approach on a dataset containing 805 MRI images from 45 patients. The experimental results reveal that our approach accomplishes comparable or better performance than other state-of-the-art approaches in terms of the Dice coefficient and the average perpendicular distance. Index Terms—Magnetic Resonance Imaging; Batch Normalization; Exponential Linear Units 

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