TY - JOUR
T1 - Change detection using adaptive fuzzy neural networks
T2 - Environmental damage assessment after the Gulf War
AU - Abuelgasim, A. A.
AU - Ross, W. D.
AU - Gopal, S.
AU - Woodcock, C. E.
N1 - Funding Information:
Abdelgadir Abuelgasim was funded through a grant from the Kuwait Foundation for the Advancement of Sciences. William D. Ross was funded in part by the Whitaker Foundation. Sucharita Gopal and Curtis Woodcock were funded by National Science Foundation Grant No. SBR-9513889. We would like to express our thanks to the funding institutions and also to Soren Ryherd and Ahmad Al-Dosari for the field data collection.
PY - 1999/11
Y1 - 1999/11
N2 - This article introduces an adaptive fuzzy neural network classifier for environmental change detection and classification applied to monitor landcover changes resulting from the Gulf War. In this study, landcover change is treated as a qualitative shift between landcover categories. The Change Detection Adaptive Fuzzy (CDAF) network learns fuzzy membership functions for each landcover class present at the first image date based on a sample of the image data. An image from a later date is then classified using this network to recognize change among familiar classes as well as change to unfamiliar landcover classes. The CDAF network predicts landcover change with 86% accuracy representing an improvement over both a standard multidate K-means technique which performed at 70% accuracy and a hybrid approach using a maximum likelihood classifier (MLC)/K-means which achieved 65% accuracy. In this study, we developed a hybrid classifier based on conventional statistical methods (MLC/K-means classifier) for comparison purposes to help evaluate the performance of the CDAF network. The CDAF compared with existing change detection methodology has two features that lead to significant performance improvements: 1) new landcover types created by a change event automatically lead to the establishment of new landcover categories through an unsupervised learning strategy, and 2) for each pixel the distribution of fuzzy membership values across possible categories are compared to determine whether a significant change has occurred.
AB - This article introduces an adaptive fuzzy neural network classifier for environmental change detection and classification applied to monitor landcover changes resulting from the Gulf War. In this study, landcover change is treated as a qualitative shift between landcover categories. The Change Detection Adaptive Fuzzy (CDAF) network learns fuzzy membership functions for each landcover class present at the first image date based on a sample of the image data. An image from a later date is then classified using this network to recognize change among familiar classes as well as change to unfamiliar landcover classes. The CDAF network predicts landcover change with 86% accuracy representing an improvement over both a standard multidate K-means technique which performed at 70% accuracy and a hybrid approach using a maximum likelihood classifier (MLC)/K-means which achieved 65% accuracy. In this study, we developed a hybrid classifier based on conventional statistical methods (MLC/K-means classifier) for comparison purposes to help evaluate the performance of the CDAF network. The CDAF compared with existing change detection methodology has two features that lead to significant performance improvements: 1) new landcover types created by a change event automatically lead to the establishment of new landcover categories through an unsupervised learning strategy, and 2) for each pixel the distribution of fuzzy membership values across possible categories are compared to determine whether a significant change has occurred.
UR - http://www.scopus.com/inward/record.url?scp=0033231223&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0033231223&partnerID=8YFLogxK
U2 - 10.1016/S0034-4257(99)00039-5
DO - 10.1016/S0034-4257(99)00039-5
M3 - Article
AN - SCOPUS:0033231223
SN - 0034-4257
VL - 70
SP - 208
EP - 223
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
IS - 2
ER -