Pixel based classification for Landsat 8 OLI multispectral satellite images using deep learning neural network

2021 ◽  
Vol 24 ◽  
pp. 100645
Author(s):  
Mohan Singh ◽  
Kapil Dev Tyagi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Satellite Images ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Multispectral Satellite Images ◽  
Deep Learning Neural Network

scholarly journals Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning

2021 ◽  
Vol 13 (16) ◽  
pp. 3166
Author(s):  
Jash R. Parekh ◽  
Ate Poortinga ◽  
Biplov Bhandari ◽  
Timothy Mayer ◽  
David Saah ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Large Scale ◽  
Mean Squared Error ◽  
Remotely Sensed ◽  
Landsat 8 ◽  
Impervious Surfaces ◽  
Remotely Sensed Data ◽  
Statistical Indices ◽  
Deep Learning Neural Network

The large scale quantification of impervious surfaces provides valuable information for urban planning and socioeconomic development. Remote sensing and GIS techniques provide spatial and temporal information of land surfaces and are widely used for modeling impervious surfaces. Traditionally, these surfaces are predicted by computing statistical indices derived from different bands available in remotely sensed data, such as the Landsat and Sentinel series. More recently, researchers have explored classification and regression techniques to model impervious surfaces. However, these modeling efforts are limited due to lack of labeled data for training and evaluation. This in turn requires significant effort for manual labeling of data and visual interpretation of results. In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks. The four model configurations labeled U-Net_SGD_Bands, U-Net_Adam_Bands, U-Net_Adam_Bands+SI, and VGG-19_Adam_Bands+SI resulted in a root mean squared error (RMSE) of 0.1582, 0.1358, 0.1375, and 0.1582 and an accuracy of 90.87%, 92.28%, 92.46%, and 90.11%, respectively, on the test set. The U-Net_Adam_Bands+SI Model, similar to the others mentioned above, is a deep learning neural network that combines Landsat 8 bands with statistical indices. This model performs the best among all four on statistical accuracy and produces qualitatively sharper and brighter predictions of impervious surfaces as compared to the other models.

scholarly journals Multilayer Perceptron Neural Network for Surface Water Extraction in Landsat 8 OLI Satellite Images

2018 ◽  
Vol 10 (5) ◽  
pp. 755 ◽  
Author(s):  
Wei Jiang ◽  
Guojin He ◽  
Tengfei Long ◽  
Yuan Ni ◽  
Huichan Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Surface Water ◽  
Multilayer Perceptron ◽  
Satellite Images ◽  
Water Extraction ◽  
Landsat 8 ◽  
Landsat 8 Oli

scholarly journals Augmented Reality Maintenance Assistant Using YOLOv5

2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Recognition ◽  
Augmented Reality ◽  
Real Time ◽  
Recognition System ◽  
High Accuracy ◽  
Video Streams ◽  
The Neural Network ◽  
Deep Learning Neural Network

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

scholarly journals Modeling the heat resistance of nickel-based superalloys by a deep learning neural network

2020 ◽  
Author(s):  
Dmitry A. Tarasov ◽  
Andrey G. Tyagunov ◽  
Oleg B. Milder
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Heat Resistance ◽  
Deep Learning Neural Network
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