Engineering topics

Miller, Jerry

Publications and source records attributed to Miller, Jerry.

3 records

NASA NTRS2005

Automated Wildfire Detection Through Artificial Neural Networks

Wildfires have a profound impact upon the biosphere and our society in general. They cause loss of life, destruction of personal property and natural resources and alter the chemistry of the atmosphere. In response to the concern over the consequences of wildland fire and to support the fire management community, the National Oceanic and Atmospheric Administration (NOAA), National Environmental Satellite, Data and Information Service (NESDIS) located in Camp Springs, Maryland gradually developed an operational system to routinely monitor wildland fire by satellite observations. The Hazard Mapping System, as it is known today, allows a team of trained fire analysts to examine and integrate, on a daily basis, remote sensing data from Geostationary Operational Environmental Satellite (GOES), Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensors and generate a 24 hour fire product for the conterminous United States. Although assisted by automated fire detection algorithms, N O M has not been able to eliminate the human element from their fire detection procedures. As a consequence, the manually intensive effort has prevented NOAA from transitioning to a global fire product as urged particularly by climate modelers. NASA at Goddard Space Flight Center in Greenbelt, Maryland is helping N O M more fully automate the Hazard Mapping System by training neural networks to mimic the decision-making process of the frre analyst team as well as the automated algorithms.

Miller, Jerry↗

NASA NTRS2005

Automated Wildfire Detection Through Artificial Neural Networks

We have tested and deployed Artificial Neural Network (ANN) data mining techniques to analyze remotely sensed multi-channel imaging data from MODIS, GOES, and AVHRR. The goal is to train the ANN to learn the signatures of wildfires in remotely sensed data in order to automate the detection process. We train the ANN using the set of human-detected wildfires in the U.S., which are provided by the Hazard Mapping System (HMS) wildfire detection group at NOAA/NESDIS. The ANN is trained to mimic the behavior of fire detection algorithms and the subjective decision- making by N O M HMS Fire Analysts. We use a local extremum search in order to isolate fire pixels, and then we extract a 7x7 pixel array around that location in 3 spectral channels. The corresponding 147 pixel values are used to populate a 147-dimensional input vector that is fed into the ANN. The ANN accuracy is tested and overfitting is avoided by using a subset of the training data that is set aside as a test data set. We have achieved an automated fire detection accuracy of 80-92%, depending on a variety of ANN parameters and for different instrument channels among the 3 satellites. We believe that this system can be deployed worldwide or for any region to detect wildfires automatically in satellite imagery of those regions. These detections can ultimately be used to provide thermal inputs to climate models.

Miller, Jerry↗

NASA NTRS2003

Validation of On-board Cloud Cover Assessment Using EO-1

The purpose of this NASA Earth Science Technology Office funded effort was to flight validate an on-board cloud detection algorithm and to determine the performance that can be achieved with a Mongoose V flight computer. This validation was performed on the EO-1 satellite, which is operational, by uploading new flight code to perform the cloud detection. The algorithm was developed by MIT/Lincoln Lab and is based on the use of the Hyperion hyperspectral instrument using selected spectral bands from 0.4 to 2.5 microns. The Technology Readiness Level (TRL) of this technology at the beginning of the task was level 5 and was TRL 6 upon completion. In the final validation, an 8 second (0.75 Gbytes) Hyperion image was processed on-board and assessed for percentage cloud cover within 30 minutes. It was expected to take many hours and perhaps a day considering that the Mongoose V is only a 6-8 MIP machine in performance. To accomplish this test, the image taken had to have level 0 and level 1 processing performed on-board before the cloud algorithm was applied. For almost all of the ground test cases and all of the flight cases, the cloud assessment was within 5% of the correct value and in most cases within 1-2%.

Mandl, Dan↗