scholarly journals Development and use of feature models of anthropogenic objects in thematic processing of space imagery

2021 ◽  
Author(s):  
O.V. Grigoreva ◽  
D.V. Zhukov ◽  
E.V. Kharzhevsky ◽  
A.V. Markov
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Feature Model ◽  
Practical Implementation ◽  
Feature Models ◽  
Optical Range ◽  
Automated Recognition ◽  
Space Imagery ◽  
Training Samples ◽  
Thematic Processing

The article describes the problem of automated recognition of the anthropogenic elements of landscape. The recognition is based on the aerospace data in the optical range of the spectrum and a feature model of an object, consisting of the geometric and reflectance characteristics. Using this model, we formed training samples for a convolutional neural network. There is a real example of the practical implementation of the model in identification of the aviation objects.

scholarly journals Attention-Based Multi-Scale Convolutional Neural Network (A+MCNN) for Multi-Class Classification in Road Images

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5137
Author(s):  
Elham Eslami ◽  
Hae-Bum Yun
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Contextual Information ◽  
Automated Classification ◽  
Automated Recognition ◽  
Pavement Distress ◽  
Multi Scale ◽  
Pavement Distresses ◽  
Multi Class Classification ◽  
Transportation Applications

Automated pavement distress recognition is a key step in smart infrastructure assessment. Advances in deep learning and computer vision have improved the automated recognition of pavement distresses in road surface images. This task remains challenging due to the high variation of defects in shapes and sizes, demanding a better incorporation of contextual information into deep networks. In this paper, we show that an attention-based multi-scale convolutional neural network (A+MCNN) improves the automated classification of common distress and non-distress objects in pavement images by (i) encoding contextual information through multi-scale input tiles and (ii) employing a mid-fusion approach with an attention module for heterogeneous image contexts from different input scales. A+MCNN is trained and tested with four distress classes (crack, crack seal, patch, pothole), five non-distress classes (joint, marker, manhole cover, curbing, shoulder), and two pavement classes (asphalt, concrete). A+MCNN is compared with four deep classifiers that are widely used in transportation applications and a generic CNN classifier (as the control model). The results show that A+MCNN consistently outperforms the baselines by 1∼26% on average in terms of the F-score. A comprehensive discussion is also presented regarding how these classifiers perform differently on different road objects, which has been rarely addressed in the existing literature.

scholarly journals Detection of Dense Small Rigid Targets Based on Convolutional Neural Network and Synthetic Images

2021 ◽  
Vol 38 (1) ◽  
pp. 61-71
Author(s):  
Xianrong Zhang ◽  
Gang Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Synthetic Image ◽  
Initial Training ◽  
Image Processing Algorithm ◽  
Training Set ◽  
Network Training ◽  
Training Samples ◽  
Image Set ◽  
Synthetic Images

Facing the image detection of dense small rigid targets, the main bottleneck of convolutional neural network (CNN)-based algorithms is the lack of massive correctly labeled training images. To make up for the lack, this paper proposes an automatic end-to-end synthesis algorithm to generate a huge amount of labeled training samples. The synthetic image set was adopted to train the network progressively and iteratively, realizing the detection of dense small rigid targets based on the CNN and synthetic images. Specifically, the standard images of the target classes and the typical background mages were imported, and the color, brightness, position, orientation, and perspective of real images were simulated by image processing algorithm, creating a sufficiently large initial training set with correctly labeled images. Then, the network was preliminarily trained on this set. After that, a few real images were compiled into the test set. Taking the missed and incorrectly detected target images as inputs, the initial training set was progressively expanded, and then used to iteratively train the network. The results show that our method can automatically generate a training set that fully substitutes manually labeled dataset for network training, eliminating the dependence on massive manually labeled images. The research opens a new way to implement the tasks similar to the detection of dense small rigid targets, and provides a good reference for solving similar problems through deep learning (DL).

scholarly journals Divide-and-Conquer Dual-Architecture Convolutional Neural Network for Classification of Hyperspectral Images

2019 ◽  
Vol 11 (5) ◽  
pp. 484 ◽  
Author(s):  
Jie Feng ◽  
Lin Wang ◽  
Haipeng Yu ◽  
Licheng Jiao ◽  
Xiangrong Zhang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Classification Performance ◽  
Hyperspectral Images ◽  
Divide And Conquer ◽  
Fixed Size ◽  
Edge Preservation ◽  
Fine Grained ◽  
Training Samples ◽  
Spatial Window

Convolutional neural network (CNN) is well-known for its powerful capability on image classification. In hyperspectral images (HSIs), fixed-size spatial window is generally used as the input of CNN for pixel-wise classification. However, single fixed-size spatial architecture hinders the excellent performance of CNN due to the neglect of various land-cover distributions in HSIs. Moreover, insufficient samples in HSIs may cause the overfitting problem. To address these problems, a novel divide-and-conquer dual-architecture CNN (DDCNN) method is proposed for HSI classification. In DDCNN, a novel regional division strategy based on local and non-local decisions is devised to distinguish homogeneous and heterogeneous regions. Then, for homogeneous regions, a multi-scale CNN architecture with larger spatial window inputs is constructed to learn joint spectral-spatial features. For heterogeneous regions, a fine-grained CNN architecture with smaller spatial window inputs is constructed to learn hierarchical spectral features. Moreover, to alleviate the problem of insufficient training samples, unlabeled samples with high confidences are pre-labeled under adaptively spatial constraint. Experimental results on HSIs demonstrate that the proposed method provides encouraging classification performance, especially region uniformity and edge preservation with limited training samples.

Recognizing Network-Like Hand-Drawn Sketches: A Convolutional Neural Network Approach

2009 ◽  
Author(s):  
Luoting Fu ◽  
Levent Burak Kara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Engineering Model ◽  
Fixed Size ◽  
Neural Network Approach ◽  
Visual Objects ◽  
Training Samples ◽  
Different Types ◽  
Engineering Models ◽  
Efficient Exploration

Hand-drawn sketches are powerful cognitive devices for the efficient exploration, visualization and communication of emerging ideas in engineering design. It is desirable that CAD/CAE tools be able to recognize the back-of-the-envelope sketches and extract the intended engineering models. Yet this is a nontrivial task for freehand sketches. Here we present a novel, neural network-based approach designed for the recognition of network-like sketches. Our approach leverages a trainable, detector/recognizer and an autonomous procedure for the generation of training samples. Prior to deployment, a Convolutional Neural Network is trained on a few labeled prototypical sketches and learns the definitions of the visual objects. When deployed, the trained network scans the input sketch at different resolutions with a fixed-size sliding window, detects instances of defined symbols and outputs an engineering model. We demonstrate the effectiveness of the proposed approach in different engineering domains with different types of sketching inputs.

scholarly journals Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG

2021 ◽  
Vol 504 (1) ◽  
pp. 372-392
Author(s):  
Robert W Bickley ◽  
Connor Bottrell ◽  
Maan H Hani ◽  
Sara L Ellison ◽  
Hossen Teimoorinia ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Formation Rate ◽  
Hybrid Approach ◽  
Environmental Parameters ◽  
Decision Threshold ◽  
Trade Offs ◽  
Training Samples ◽  
Wide Range ◽  
Order Of Magnitude

ABSTRACT The Canada–France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ∼5000 deg2 of the sky, representing a first-rate opportunity to identify recently merged galaxies. Because of the large number of galaxies in CFIS, we investigate the use of a convolutional neural network (CNN) for automated merger classification. Training samples of post-merger and isolated galaxy images are generated from the IllustrisTNG simulation processed with the observational realism code RealSim. The CNN’s overall classification accuracy is 88 per cent, remaining stable over a wide range of intrinsic and environmental parameters. We generate a mock galaxy survey from IllustrisTNG in order to explore the expected purity of post-merger samples identified by the CNN. Despite the CNN’s good performance in training, the intrinsic rarity of post-mergers leads to a sample that is only ∼6 per cent pure when the default decision threshold is used. We investigate trade-offs in purity and completeness with a variable decision threshold and find that we recover the statistical distribution of merger-induced star formation rate enhancements. Finally, the performance of the CNN is compared with both traditional automated methods and human classifiers. The CNN is shown to outperform Gini–M20 and asymmetry methods by an order of magnitude in post-merger sample purity on the mock survey data. Although the CNN outperforms the human classifiers on sample completeness, the purity of the post-merger sample identified by humans is frequently higher, indicating that a hybrid approach to classifications may be an effective solution to merger classifications in large surveys.

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