Convolutional neural networks with hierarchical context transfer for high-resolution spatiotemporal predictions

Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. In addition, the road network plays an important part in transportation, and currently one of the main related challenges is detecting and monitoring the occurring changes in order to update the existent cartography. This task is challenging due to the nature of the object (continuous and often with no clearly defined borders) and the nature of remotely sensed images (noise, obstructions). In this paper, we propose a novel framework based on convolutional neural networks (CNNs) to classify secondary roads in high-resolution aerial orthoimages divided in tiles of 256 × 256 pixels. We will evaluate the framework’s performance on unseen test data and compare the results with those obtained by other popular CNNs trained from scratch.

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Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors

International Journal of Distributed Sensor Networks ◽

10.1177/1550147720920485 ◽

2020 ◽

Vol 16 (5) ◽

pp. 155014772092048

Author(s):

Miguel Ángel López-Medina ◽

Macarena Espinilla ◽

Chris Nugent ◽

Javier Medina Quero

Keyword(s):

Neural Networks ◽

High Resolution ◽

Convolutional Neural Networks ◽

Viewing Angle ◽

Vision Sensor ◽

Low Resolution ◽

Data Set ◽

Wide Viewing Angle ◽

Vision Sensors ◽

Thermal Vision

The automatic detection of falls within environments where sensors are deployed has attracted considerable research interest due to the prevalence and impact of falling people, especially the elderly. In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. Second, we include fuzzy representation of thermal information. Third, we enable the generation of a large data set from a set of few images using ad hoc data augmentation, which increases the original data set size, generating new synthetic images. Fourth, we define three types of convolutional neural networks which are adapted for each thermal vision sensor in order to evaluate the impact of the architecture on fall detection performance. The results show encouraging performance in single-occupancy contexts. In multiple occupancy, the low-resolution thermal vision sensor with a wide viewing angle obtains better performance and reduction of learning time, in comparison with the high-resolution thermal vision sensors with a central viewing angle.

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A Joint Convolutional Neural Networks and Context Transfer for Street Scenes Labeling

IEEE Transactions on Intelligent Transportation Systems ◽

10.1109/tits.2017.2726546 ◽

2018 ◽

Vol 19 (5) ◽

pp. 1457-1470 ◽

Cited By ~ 66

Author(s):

Qi Wang ◽

Junyu Gao ◽

Yuan Yuan

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Context Transfer

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Fusion High-Resolution Network for Diagnosing ChestX-ray Images

Electronics ◽

10.3390/electronics9010190 ◽

2020 ◽

Vol 9 (1) ◽

pp. 190 ◽

Cited By ~ 3

Author(s):

Zhiwei Huang ◽

Jinzhao Lin ◽

Liming Xu ◽

Huiqian Wang ◽

Tong Bai ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

High Resolution ◽

Convolutional Neural Networks ◽

Characteristic Curve ◽

Area Under The Curve ◽

Disease Classification ◽

Deep Convolutional Neural Networks ◽

Learning Methods ◽

Global And Local

The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing deep learning methods are based on classic pretrained models, trained by global ChestX-ray images. In this paper, we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network (FHRNet). The FHRNet concatenates the global average pooling layers of the global and local feature extractors—it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification. Compared with the results of other available methods, our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset, according to the receiver operating characteristic curve and area-under-the-curve score. An ablation study further confirmed the effectiveness of the global and local branch networks in improving the classification accuracy of thorax diseases.

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Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

Scientific Reports ◽

10.1038/s41598-019-53797-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 20

Author(s):

Teja Kattenborn ◽

Jana Eichel ◽

Fabian Ewald Fassnacht

Keyword(s):

Neural Networks ◽

High Resolution ◽

Convolutional Neural Networks ◽

Low Cost ◽

Training Data ◽

Observation Data ◽

Visual Interpretation ◽

Fine Grained ◽

Wide Range ◽

Vegetation Species

AbstractRecent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

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