Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer

Ghosh, Arthita; Ehrlich, Max; Davis, Larry S.; Chellappa, Rama

doi:10.1109/igarss.2019.8900639

Cited by 3 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine vision simulates this cognitive process in many industrial applications. Application areas for material recognition are, for example, sorting processes such as waste separation [2], the monitoring of construction progress [3], and urban or botanical investigations with remote sensing [4]. The knowledge about material properties is of great importance for the interaction of robots with everyday objects [5] or for the ongoing automation of manufacturing and other industrial processes using modern smart technology, also known as Industry 4.0 [6].…”

Section: Introductionmentioning

confidence: 99%

Material Classification with a 7th Sense; Boosting Recognition Ability with Thermal Imagery

Großmann

Horn

Niggemann

2022

Preprint

View full text Add to dashboard Cite

Material recognition using optical sensors is a key enabler technology in the field of automation. Nowadays, in the age of deep learning, the challenge shifted from (manual) feature engineering to collecting big data. State of the art recognition approaches are based on deep neural networks employing huge databases. But still, it is difficult to transfer these latest recognition results into the wild – various lighting conditions, a changing image quality, or different and new material classes are challenging complications. Evaluating a larger electromagnetic spectrum is one way to solve these challenges. In this study, the infrared (IR) emissivity as a materialspecific property is investigated regarding its suitability for increasing the material classification reliability. Predictions of a deep learning model are combined with engineered features from IR data. This approach increases the overall accuracy and helps to differentiate between materials that visually appear similar. The solution is verified using real data from the field of automatized disinfection processes.

show abstract

Section: Introductionmentioning

confidence: 99%

Material Classification with a 7th Sense; Boosting Recognition Ability with Thermal Imagery

Großmann

Horn

Niggemann

2022

Preprint

View full text Add to dashboard Cite

show abstract

Improving remote material classification ability with thermal imagery

Großmann

Horn

Niggemann

2022

Sci Rep

View full text Add to dashboard Cite

Material recognition using optical sensors is a key enabler technology in the field of automation. Nowadays, in the age of deep learning, the challenge shifted from (manual) feature engineering to collecting big data. State of the art recognition approaches are based on deep neural networks employing huge databases. But still, it is difficult to transfer these latest recognition results into the wild—various lighting conditions, a changing image quality, or different and new material classes are challenging complications. Evaluating a larger electromagnetic spectrum is one way to master these challenges. In this study, the infrared (IR) emissivity as a material specific property is investigated regarding its suitability for increasing the material classification reliability. Predictions of a deep learning model are combined with engineered features from IR data. This approach increases the overall accuracy and helps to differentiate between materials that visually appear similar. The solution is verified using real data from the field of automatized disinfection processes.

show abstract

Deep Intelligent Neural Network for Medical Geographic Small-target Intelligent Satellite Image Super-resolution

Wei¹,

Liu²

2021

jist

View full text Add to dashboard Cite

In the field of single-image super-resolution (SISR) research, neural networks and deep learning methods are gradually being widely used by researchers. Over time, the fields of application have expanded in scope. The SISR method is also applied in the field of intelligent satellite imagery. In recent years, research applications based on intelligent satellite images have mostly focused on imaging, classification, and segmentation. They have rarely been used in actual observation problems. This article proposes a new intelligent neural network model, the Laplacian pyramid residual dense network, for the super-resolution of hyperspectral satellite medical geographic small-targets. This study proceeds in three steps. First, the three-layer Laplacian pyramid structure is designed to increase the depth of the image at the feature extraction stage. Second, the residual mode is improved and updated; a new residual block is proposed for constructing the residual dense network to enhance the feature details of the image during the training process. In the third step, an end-to-end network is established directly through the residual structure for eliminating unnecessary visualization during the process and for ease of training. According to the experimental results, it has been proved that the deep intelligent neural network method proposed here has achieved good results in the application for super-resolution of medical geographic small-target intelligent satellite images.

show abstract

Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer

Cited by 3 publications

References 30 publications

Material Classification with a 7th Sense; Boosting Recognition Ability with Thermal Imagery

Material Classification with a 7th Sense; Boosting Recognition Ability with Thermal Imagery

Improving remote material classification ability with thermal imagery

Deep Intelligent Neural Network for Medical Geographic Small-target Intelligent Satellite Image Super-resolution

Contact Info

Product

Resources

About