Mapping of Hydrographic Networks from Multispectral Imagery using Neural Networks and Principal Curves

2006 ◽  
Author(s):  
Marek Zaremba ◽  
Dianne Richardson
Keyword(s):  
Neural Networks ◽  
Multispectral Imagery ◽  
Principal Curves

Mine discrimination using multispectral imagery with feedforward neural networks

1995 ◽  
Author(s):  
Taher Daud ◽  
Tuan A. Duong ◽  
Harry Langenbacher ◽  
Helen Tsu ◽  
Anilkumar P. Thakoor
Keyword(s):  
Neural Networks ◽  
Feedforward Neural Networks ◽  
Multispectral Imagery

scholarly journals On-Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagery

2021 ◽  
Vol 13 (17) ◽  
pp. 3479
Author(s):  
Maria Pia Del Rosso ◽  
Alessandro Sebastianelli ◽  
Dario Spiller ◽  
Pierre Philippe Mathieu ◽  
Silvia Liberata Ullo
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Neural Networks ◽  
Volcanic Eruption ◽  
Low Cost ◽  
Volcanic Eruptions ◽  
Raspberry Pi ◽  
Processing Unit ◽  
Multispectral Imagery

In recent years, the growth of Machine Learning (ML) algorithms has raised the number of studies including their applicability in a variety of different scenarios. Among all, one of the hardest ones is the aerospace, due to its peculiar physical requirements. In this context, a feasibility study, with a prototype of an on board Artificial Intelligence (AI) model, and realistic testing equipment and scenario are presented in this work. As a case study, the detection of volcanic eruptions has been investigated with the objective to swiftly produce alerts and allow immediate interventions. Two Convolutional Neural Networks (CNNs) have been designed and realized from scratch, showing how to efficiently implement them for identifying the eruptions and at the same time adapting their complexity in order to fit on board requirements. The CNNs are then tested with experimental hardware, by means of a drone with a paylod composed of a generic processing unit (Raspberry PI), an AI processing unit (Movidius stick) and a camera. The hardware employed to build the prototype is low-cost, easy to found and to use. Moreover, the dataset has been published on GitHub, made available to everyone. The results are promising and encouraging toward the employment of the proposed system in future missions, given that ESA has already moved the first steps of AI on board with the Phisat-1 satellite, launched on September 2020.

scholarly journals DEEP PHENOTYPING CONSIDERING TILE DRAINAGE FROM UAS-BASED MULTISPECTRAL IMAGERY BY CONVOLUTIONAL NEURAL NETWORKS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 417-421
Author(s):  
M. Herrero-Huerta ◽  
S. R. Rahmani ◽  
K. M. Rainey
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Convolutional Neural Networks ◽  
Critical Role ◽  
Rooting Depth ◽  
Plant Phenotyping ◽  
Multispectral Imagery ◽  
Deep Convolutional Neural Networks ◽  
Aerial Systems ◽  
Deep Phenotyping

Abstract. Subsurface agriculture tile lines can greatly impact plant phenotypic characteristics through spatial variation of soil moisture, plant nutrient, and plant rooting depth. Therefore, location of subsurface tile lines plays a critical role in supporting the above ground plant phentoyping and needs to be considered in plant phenotyping analysis. Unnamed Aerial Systems (UAS) imagery together with deep learning methods can develop strong relations between the vegetation spectra and soil parameters.Here, we consider the capability of deep convolutional neural networks (CNN) to evaluate crop quality based on biomass production derived from soil moisture differences by using UAS-based multispectral imagery over soybean breeding fields. Results are still being evaluated, with particular attention to the temporal and spatial resolution of the data required to apply our approach.

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