Title
Interactive classification of remote sensing images by using optimum-path forest and genetic programming
Abstract
The use of remote sensing images as a source of information in agribusiness applications is very common. In those applications, it is fundamental to know how the space occupation is. However, identification and recognition of crop regions in remote sensing images are not trivial tasks yet. Although there are automatic methods proposed to that, users very often prefer to identify regions manually. That happens because these methods are usually developed to solve specific problems, or, when they are of general purpose, they do not yield satisfying results. This work presents a new interactive approach based on relevance feedback to recognize regions of remote sensing. Relevance feedback is a technique used in content-based image retrieval (CBIR) tasks. Its objective is to aggregate user preferences to the search process. The proposed solution combines the Optimum-Path Forest (OPF) classifier with composite descriptors obtained by a Genetic Programming (GP) framework. The new approach has presented good results with respect to the identification of pasture and coffee crops, overcoming the results obtained by a recently proposed method and the traditional Maximimun Likelihood algorithm.
Year
DOI
Venue
2011
10.1007/978-3-642-23678-5_35
CAIP (2)
Keywords
Field
DocType
aggregate user preference,coffee crop,genetic programming,proposed solution,optimum-path forest,agribusiness application,interactive classification,automatic method,relevance feedback,new approach,new interactive approach
Computer vision,Relevance feedback,General purpose,Know-how,Computer science,Remote sensing,Image retrieval,Genetic programming,Artificial intelligence,Classifier (linguistics),Machine learning
Conference
Volume
ISSN
Citations 
6855
0302-9743
6
PageRank 
References 
Authors
0.41
8
6