Object Extraction From Very High-Resolution Images Using a Convolutional Neural Network Based on a Noisy Large-Scale Dataset

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, immunohistochemistry images of different TFs in chorionic villus were obtained by a high-resolution scanner. Next, we used a convolutional neural network and machine learning method to infer gene interaction networks of human placenta from these images based on the transfer learning technique. The experimental results show that deep learning models reveals regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. Conclusions: We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

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High Resolution Satellite Images for Instantaneous Shoreline Extraction Using New Enhancement Algorithms

Geosciences ◽

10.3390/geosciences9030123 ◽

2019 ◽

Vol 9 (3) ◽

pp. 123 ◽

Cited By ~ 5

Author(s):

Donatella Dominici ◽

Sara Zollini ◽

Maria Alicandro ◽

Francesca Della Torre ◽

Paolo Buscema ◽

...

Keyword(s):

High Resolution ◽

Large Scale ◽

Satellite Images ◽

Human Life ◽

Essential Element ◽

Action Planning ◽

High Resolution Satellite Images ◽

High Resolution Images ◽

Territorial Government ◽

Very High

Knowledge of a territory is an essential element in any future planning action and in appropriate territorial and environmental requalification action planning. The current large-scale availability of satellite data, thanks to very high resolution images, provides professional users in the environmental, urban planning, engineering, and territorial government sectors, in general, with large amounts of useful data with which to monitor the territory and cultural heritage. Italy is experiencing environmental emergencies, and coastal erosion is one of the greatest threats, not only to the Italian heritage and economy, but also to human life. The aim of this paper is to find a rapid way of identifying the instantaneous shoreline. This possibility could help government institutions such as regions, civil protection, etc., to analyze large areas of land quickly. The focus is on instantaneous shoreline extraction in Ortona (CH, Italy), without considering tides, using WorldView-2 satellite images (50-cm resolution in panchromatic and 2 m in multispectral). In particular, the main purpose of this paper is to compare commercial software and ACM filters to test their effectiveness.

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A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2020.102086 ◽

2020 ◽

Vol 88 ◽

pp. 102086 ◽

Cited By ~ 8

Author(s):

Chenxiao Zhang ◽

Peng Yue ◽

Deodato Tapete ◽

Boyi Shangguan ◽

Mi Wang ◽

...

Keyword(s):

Neural Network ◽

Remote Sensing ◽

High Resolution ◽

Land Cover ◽

Convolutional Neural Network ◽

Land Cover Classification ◽

Remote Sensing Images ◽

Object Based ◽

Multi Level ◽

Very High

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Tree Crown Delineation Algorithm Based on a Convolutional Neural Network

Remote Sensing ◽

10.3390/rs12081288 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1288 ◽

Cited By ~ 5

Author(s):

José R. G. Braga ◽

Vinícius Peripato ◽

Ricardo Dalagnol ◽

Matheus P. Ferreira ◽

Yuliya Tarabalka ◽

...

Keyword(s):

Neural Network ◽

High Resolution ◽

Convolutional Neural Network ◽

Tropical Forests ◽

Large Scale ◽

Satellite Images ◽

Tree Level ◽

Tree Crown ◽

Individual Tree ◽

High Resolution Satellite Images

Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the R e c a l l , P r e c i s i o n , and F 1 score values obtained were were 0.81 , 0.91 , and 0.86 , respectively. In the study site, the total of tree crowns delineated was 59,062 . These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions.

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