EVALUATION OF ADVANCED DATA MINING ALGORITHMS IN LAND USE/LAND COVER MAPPING

Watershed Land Cover/Land Use Mapping Using Remote Sensing and Data Mining in Gorganrood, Iran

The Gorganrood watershed (GW) is experiencing considerable environmental change in the form of natural hazards and erosion, as well as deforestation, cultivation and development activities. As a result of this, different types of Land Cover/Land Use (LCLU) change are taking place on an intensive level in the area. This research study investigates the LCLU conditions upstream of this watershed f...

Evaluation of Data Mining Algorithms for Detection of Liver Disease

Background and Aim: The liver, as one of the largest internal organs in the body, is responsible for many vital functions including purifying and purifying blood, regulating the body's hormones, preserving glucose, and the body. Therefore, disruptions in the functioning of these problems will sometimes be irreparable. Early prediction of these diseases will help their early and effective treatm...

Comparison of Neural Classification Algorithms Applied to Land Cover Mapping

We compared the performance of several supervised classification algorithms on multi-source remotely sensed images. Apart from the Multi-Layer Perceptron, K-Nearest-Neighbour and Radial Basis Function network approaches, we looked more in detail at the Support Vector Machine classifier, which recently showed promising results in our setting. In particular, it is able to provide meaningful answe...

Evaluation of Sentinel-1 Interferometric SAR Coherence efficiency for Land Cover Mapping

In this study, the capabilities of Interferometric Synthetic Aperture Radar (InSAR) time series data and machine learning have been evaluated for land cover mapping in Iran. In this way, a time series of Sentinel-1 SAR data (including 16 SLC images with approximately 24 days time interval) from 2018 to 2020 were used for a region of Ahvaz County located in Khuzestan province. Using InSAR proces...

Land-cover and Land-use Mapping