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Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information
2019
Remote Sensing
Preserved Fulltext
In this study, we propose a fully convolutional neural networks (F-CNNs) classification model to map the flooding extent from Landsat satellite images. ...
A new set of different Landsat images covering flooded areas across Australia were used to evaluate the classification performance of the model. ...
The High Performance Computing (HPC) lab of Queensland University of Technology has been utilized to run the experimental work on training data preparation and F-CNNs model training. ...
doi:10.3390/rs11192331
fatcat:hshwng3jhnfo3kzydvshwopyzy
JOINT GEOMETRIC CALIBRATION OF COLOR AND THERMAL CAMERAS FOR SYNCHRONIZED MULTIMODAL DATASET CREATING
2019
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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They proved the effectivity of the developed techniques for collecting and augmenting synchronized multimodal imagery dataset for convolutional neural networks model training and evaluating. ...
So the purpose of the performed study was to develop a technique for joint calibration of color and long wave infra-red cameras which are to be used for collecting multimodal dataset needed for the tasks ...
The results of joint color and long wave infra-red cameras calibration were used for processing of multimodal image dataset collected for the developing and evaluating convolutional neural network models ...
doi:10.5194/isprs-archives-xlii-2-w18-79-2019
doaj:b57be756d7c94a0a91297b0fc906a62e
fatcat:zqdpcvufsbeidkmzavmzra4kmy
Detecting Gas Vapor Leaks Using Uncalibrated Sensors
[article]
2019
arXiv
pre-print
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Finally, we use conventional convolutional neural networks as a baseline method and compare their performance with the two aforementioned deep neural network algorithms in order to evaluate their effectiveness ...
Additive neural networks (termed AddNet) are based on a multiplication-devoid operator and consequently exhibit energy-efficiency compared to regular neural networks. ...
VOC gas absorbs the infra-red light appearing as a white cloud in the black-hot mode infra-red image as shown in Fig. 2 . ...
arXiv:1908.07619v1
fatcat:u6vk3zwjuvhvfnqdcj7kaz5aqm
Deep Perceptual Mapping for Thermal to Visible Face Recognition
[article]
2015
arXiv
pre-print
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Our approach captures the highly non-linear relationship be- tween the two modalities by using a deep neural network. ...
We show substantive performance improvement on a difficult thermal-visible face dataset. ...
As opposed to the visible spectrum wavelength (0.35µm to 0.74µm), the infra-red spectrum lies in four main ranges. Near infra-red 'NIR' (0.74µm-1µm), short-wave infra-red c 2015. ...
arXiv:1507.02879v1
fatcat:ikrpe46z4bhk7cy7zrskrqdso4
Optical Convolutional Neural Networks – Combining Silicon Photonics and Fourier Optics for Computer Vision
[article]
2020
arXiv
pre-print
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The Convolutional Neural Network (CNN) is a state-of-the-art architecture for a wide range of deep learning problems, the quintessential example of which is computer vision. ...
CNNs principally employ the convolution operation, which can be accelerated using the Fourier transform. ...
was performed using an InGaaS Short-Wavelength Infra-Red (SWIR) camera. ...
arXiv:2103.09044v1
fatcat:zk5i4fol2rgndhzjq7o5wn6jfe
Forest Classification Method Based on Convolutional Neural Networks and Sentinel-2 Satellite Imagery
2019
International Journal of Fuzzy Logic and Intelligent Systems
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The objective of this study is to develop a classification method based on convolutional neural network (CNN) and Sentinel-2 satellite imagery including the spectral feature, spectral index and spatial ...
Overall accuracy was used to evaluate the performance of the classification result. ...
He is a lecturer at Faculty of Computer Science, University of Indonesia. Email: wibowo@cs.ui.ac.id ...
doi:10.5391/ijfis.2019.19.4.272
fatcat:ol7klapbyrgdvdy4bbwpj6mtbq
Deep Perceptual Mapping for Cross-Modal Face Recognition
2016
International Journal of Computer Vision
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2016 pre-print arXiv:1601.05347v1 fatcat:lgcrnwnz7ffxdkxcby4i7ye3oeOther Versions
Our approach captures the highly non-linear relationship between the two modalities by using a deep neural network. ...
Due to a very large modality gap, thermal-to-visible face recognition is one of the most challenging face matching problem. ...
Near infra-red 'NIR' (0.74µm-1µm), short-wave infra-red 'SWIR' (1-3µm), mid-wave infra-red 'MWIR' (3-5µm) and long-wave infra-red . ...
doi:10.1007/s11263-016-0933-2
fatcat:nyrnxf4czzf2nj2fxhvyccvsnq
Machine Learning and Big Scientific Data
[article]
2019
arXiv
pre-print
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After a brief review of some initial applications of machine learning at the Rutherford Appleton Laboratory, we focus on challenges and opportunities for AI in advancing materials science. ...
Finally, we discuss the importance of developing some realistic machine learning benchmarks using Big Scientific Data coming from a number of different scientific domains. ...
The spatial resolution is 0.5km in the VNIR and short-wave infra-red (SWIR) channels and 1km in the thermal IR channels. In all experiments the nadir view of channels S1-S9 are used as inputs. ...
arXiv:1910.07631v1
fatcat:fpwolsvxmbc7dci3lzwv3r3ba4
Spotting insects from satellites: modeling the presence of Culicoides imicola through Deep CNNs
[article]
2019
arXiv
pre-print
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In this respect, we frame our task as a binary classification problem, underpinning Convolutional Neural Networks (CNNs) as being able to learn useful representation from multi-band images. ...
Additionally, we provide a multi-instance variant, aimed at extracting temporal patterns from a short sequence of spectral images. ...
Differently, the authors of [13] described a threedimensional Convolutional Neural Network (3D-CNN), inferring crops categories (i.e. ...
arXiv:1911.10024v1
fatcat:j6yrrv37mfh2fadxccoxpzh3q4
On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network
2020
PLoS ONE
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In this study, a temporal convolutional neural network (TempCNNs) model is implemented for crop classification, while considering remotely sensed imagery of the selected pilot region with specific focus ...
on the Tobacco crop. ...
Pooling layers are not used in our architectures, as it's mostly used in computer vision tasks where a whole image or parts of image are provided to the neural network as 2D signals with channels on the ...
doi:10.1371/journal.pone.0239746
pmid:32986785
fatcat:2dpv7rajpjgcnhomgjhtbj3u5y
An Automated Snow Mapper Powered by Machine Learning
2021
Remote Sensing
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A multispectral image from Sentinel-2B, a digital elevation model, and machine learning algorithms such as random forest and convolutional neural network, were utilized. ...
It is built in a Python environment based on object-based analysis. AutoSMILE was first applied in a mountainous area of 1002 km2 in Bome County, eastern Tibetan Plateau. ...
, Red Edge 3, Narrow Near Infra-Red, Short Wave Infrared 1 and Short Wave Infrared 2 bands) and another three with 60 m spatial resolution (Coastal Aerosol, Water Vapor and Cirrus bands). ...
doi:10.3390/rs13234826
fatcat:xap7v7et6nbyvkwd2hqt4ikgme
Monitoring the Impact of Large Transport Infrastructure on Land Use and Environment Using Deep Learning and Satellite Imagery
2022
Remote Sensing
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Bands stretch from aerosols, over Near Infrared to the Short Wave Infra-Red (SWIR) spectral range with the spatial resolution ranging from 10 m to 60 m [17] . ...
They try to tackle this problem by providing an overview of the evolution of DL with a focus on image segmentation and object detection in convolutional neural networks (CNN). ...
doi:10.3390/rs14102494
dblp:journals/remotesensing/PavlovicIAC22
fatcat:64i527enj5gzvnfizxxtnt534i
Investigating the Impact of Using IR Bands on Early Fire Smoke Detection from Landsat Imagery with a Lightweight CNN Model
2022
Remote Sensing
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Adding both Short-Wave Infra-Red (SWIR) bands can further improve the model performance compared with adding only one SWIR band. ...
To prepare for potential on-board-of-small-satellite detection, we designed a lightweight Convolutional Neural Network (CNN) model named "Variant Input Bands for Smoke Detection (VIB_SD)", which achieved ...
The images contain six spectral bands, including the RGB bands, the Near-Infra-Red (NIR) band, and two Short-Wave Infra-Red (SWIR) bands (i.e., SWIR_1 and SWIR_2), all possessing a 30-m spatial resolution ...
doi:10.3390/rs14133047
fatcat:a7qs5uhvqjdgpjupk5uywv67xq
NDVI Classification using Supervised Learning Method
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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To keep the Land quality at its best possible, the study on Land cover images, which are acquired from satellites based on time series, spatial and colour, are required to understand how the Land can be ...
Hence, a novel approach with proper model, Machine Learning algorithm and greater accuracy is always acceptable ...
The BAND 1: Green, BAND II: Red, BAND III: Near Infra Red, BAND IV: Short Wave Infra Red.This image obtained is pre-processed by false coloring technique and thereby enhancing the view of image using histogram ...
doi:10.35940/ijitee.b7083.019320
fatcat:b6tannvskjdo7i6lc42k64mf4q
Inter-band Retrieval and Classification Using the Multi-labeled Sentinel-2 BigEarthNet Archive
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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This work exploits the contextual information capturing ability of deep neural networks (DNNs), particularly investigating multispectral band properties from Sentinel-2 image patches. ...
Availability of an enormous volume of data demands handling large-scale, diverse data, which have been made possible with neural network-based architectures. ...
There are six 20 m bands, i.e., band number 5 (vegetation red edge), 6 (vegetation red edge), 7 (vegetation red edge), 8A (vegetation red edge), 11 (short wave infrared), and 12 (short wave infrared), ...
doi:10.1109/jstars.2021.3112209
fatcat:jx3wuv2k3nb6the5ofuojgdbaq
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