An efficient contextual algorithm to detect subsurface fires with NOAA/AVHRR data

Journal Article · Tue Jul 15 04:00:00 EDT 2008 · IEEE Transactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/TGRS.2008.916631· OSTI ID:21073796

Gautam, R S; Singh, D; Mittal, A ^[1]

---

1. Indian Institute for Technology, Roorkee (India)

This paper deals with the potential application of National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) data to detect subsurface fire (subsurface hotspots) by proposing an efficient contextual algorithm. Although few algorithms based on the fixed-thresholding approach have been proposed for subsurface hotspot detection, however, for each application, thresholds have to be specifically tuned to cope with unique environmental conditions. The main objective of this paper is to develop an instrument-independent adaptive method by which direct threshold or multithreshold can be avoided. The proposed contextual algorithm is helpful to monitor subsurface hotspots with operational satellite data, such as the Jharia region of India, without making any region-specific guess in thresholding. Novelty of the proposed work lies in the fact that once the algorithmic model is developed for the particular region of interest after optimizing the model parameters, there is no need to optimize those parameters again for further satellite images. Hence, the developed model can be used for optimized automated detection and monitoring of subsurface hotspots for future images of the particular region of interest. The algorithm is adaptive in nature and uses vegetation index and different NOAA/AVHRR channel's statistics to detect hotspots in the region of interest. The performance of the algorithm is assessed in terms of sensitivity and specificity and compared with other well-known thresholding, techniques such as Otsu's thresholding, entropy-based thresholding, and existing contextual algorithm proposed by Flasse and Ceccato. The proposed algorithm is found to give better hotspot detection accuracy with lesser false alarm rate.

• OSTI ID: 21073796
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Journal Issue: 7 Vol. 46; ISSN IGRSD2; ISSN 0196-2892
• Country of Publication: United States
• Language: English

Related Subjects

01 COAL, LIGNITE, AND PEAT
ALGORITHMS
AUTOMATION
DETECTION
FIRES
IMAGE PROCESSING
MONITORING
RADIOMETERS
REMOTE SENSING
SATELLITES
UNDERGROUND