Abstract: In order to help the expert to validate association rules
extracted from data, some quality measures are proposed in the
literature. We distinguish two categories: objective and subjective
measures. The first one depends on a fixed threshold and on data
quality from which the rules are extracted. The second one consists
on providing to the expert some tools in the objective to explore and
visualize rules during the evaluation step. However, the number of
extracted rules to validate remains high. Thus, the manually mining
rules task is very hard. To solve this problem, we propose, in this
paper, a semi-automatic method to assist the expert during the
association rule's validation. Our method uses rule-based
classification as follow: (i) We transform association rules into
classification rules (classifiers), (ii) We use the generated classifiers
for data classification. (iii) We visualize association rules with their
quality classification to give an idea to the expert and to assist him
during validation process.
Abstract: One of the most important tasks in urban remote
sensing is the detection of impervious surfaces (IS), such as roofs and
roads. However, detection of IS in heterogeneous areas still remains
one of the most challenging tasks. In this study, detection of concrete
roof using an object-based approach was proposed. A new rule-based
classification was developed to detect concrete roof tile. This
proposed rule-based classification was applied to WorldView-2
image and results showed that the proposed rule has good potential to
predict concrete roof material from WorldView-2 images, with 85%
accuracy.