Saeid Niazmardi

Saeid Niazmardi
Graduate University of Advanced Technology | kgut · Department of Remote Sensing

Phd

About

22
Publications
4,722
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
208
Citations
Citations since 2016
13 Research Items
184 Citations
2016201720182019202020212022010203040
2016201720182019202020212022010203040
2016201720182019202020212022010203040
2016201720182019202020212022010203040

Publications

Publications (22)
Article
The quality of surface waters plays a key role in the sustainability of ecological systems. Measuring water quality parameters (WQPs) is of high importance in the management of surface water resources. In this paper, contemporary-developed regression analysis was proposed to estimate the hard-to-measure parameters from those that can be measured ea...
Article
Full-text available
Multiplekernel learning (MKL) algorithms are among the most successful classification methods for hyperspectral data. Nevertheless, these algorithms suffer from two main drawbacks of computational complexity and debility to admit to the end-to-end learning paradigm. This article proposed a convolutional kernel classifier (CKC) for hyperspectral rem...
Article
Full-text available
Soil moisture estimation is essential for optimal water and soil resources management. Surface soil moisture is an important variable in the natural water cycle, which plays an important role in the global equilibrium of water and energy due to its impact on hydrological, ecological and meteorological processes. Soil moisture changes due to the var...
Article
Full-text available
Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide precious information for several agricultural applications, such as crop monitoring, yield forecasting, and crop inventory. However, several issues affect the classification performance of SITS data. As one of the most challenging problems, constituent images...
Article
Classification of time-series of vegetation indices (VIs) can be a reliable strategy for identifying and monitoring different crop types. Recently, with the advent of new sensors, the time-series data with high spatial and temporal resolutions have become widely available and used for constructing various VIs time-series. These high-resolution time...
Presentation
Full-text available
This paper aims to compare two state-of-the-art classification algorithms, namely Support Vector Machine (SVM) and Random Forest (RF) algorithms, in terms of accuracy and running time, in order to crop mapping from multi-temporal optical and radar images with limited training samples. The optical data are RapidEye images and the radar data are UAVS...
Article
This paper presents multiple kernel learning (MKL) in the context of remote sensing (RS) image classification problems by illustrating main characteristics of different MKL algorithms and analyzing their properties in RS domain. A categorization of different MKL algorithms is initially introduced, and some promising MKL algorithms for each category...
Article
Full-text available
Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide very valuable information for several agricultural applications, such as crop monitoring, yield estimation, and crop inventory. However, the SITS data classification is not straightforward. Because different images of a SITS data have different levels of info...
Article
Multivariate satellite-image time-series (MSITS) are a valuable source of information for a wide range of agricultural applications. Image classification, one of the main applications of this type of data, is a challenging task. It is mainly because MSITS are generated by a complex interaction among several sources of information, which are known a...
Article
Multiple Kernel Learning (MKL) algorithms have recently demonstrated their effectiveness for classifying the data with numerous features. These algorithms aim at learning an optimal composite kernel through combining the basis kernels constructed from different features. Despite their satisfactory results, MKL algorithms assume that the basis kerne...
Article
Multiple kernel learning (MKL) algorithms are proposed to address the problems associated with kernel selection of the kernel-based classification algorithms. Using a group of kernels rather than one single kernel, the MKL algorithms aim to provide better classification efficiency. This paper presents new similarity-based MKL algorithms to classify...
Conference Paper
A new multiple kernel learning (MKL) framework is presented for classification of satellite remotely sensed time series for agricultural analysis. In this MKL framework, a new composite kernel is constructed with a weighted sum of some predefined kernels. The problem of proper estimation of weights is modeled as an optimization problem of maximizin...
Conference Paper
Full-text available
In recent decades, large margin methods such as Support Vector Machines (SVMs) are supposed to be the state-of-the-art of supervised learning methods for classification of hyperspectral data. However, the results of these algorithms mainly depend on the quality and quantity of available training data. To tackle down the problems associated with the...
Article
Full-text available
Numerous investigations on Urban Heat Island (UHI) show that land cover change is the main factor of increasing Land Surface Temperature (LST) in urban areas. Therefore, to achieve a model which is able to simulate UHI growth, urban expansion should be concerned first. Considerable researches on urban expansion modeling have been done based on cell...
Article
Full-text available
In recent decades, large margin methods such as Support Vector Machines (SVMs) are supposed to be the state-of-the-art of supervised learning methods for classification of hyperspectral data. However, the results of these algorithms mainly depend on the quality and quantity of available training data. To tackle down the problems associated with the...
Conference Paper
Time series of remotely sensed data are usually an important source of information for various agricultural applications. However, modeling and analyzing of time series data is not straightforward. In this paper, a new method for classification of time series data is proposed. The method is a modified Maximum Likelihood (ML) algorithms that uses Su...
Article
Unsupervised classification approaches, also known as “clustering algorithms”, can be considered a solution to problems associated with the supervised classification of remotely sensed image data. The most important of these problems with respect to statistical classification algorithms is the lack of enough high quality training data and high dime...
Article
Full-text available
Hyperspectral sensors, by accurate sampling of object reflectance into numerous narrow spectral bands, can provide valuable information to identify different land-cover classes. Nevertheless, classification of these data has some problems. In particular, one of the most well-known of them is not having adequate training data for learning of classif...
Article
Full-text available
Hyperspectral data classification using supervised approaches, in general, and the statistical algorithms, in particular, need high quantity and quality training data. However, these limitations, and the high dimensionality of these data, are the most important problems for using the supervised algorithms. As a solution, unsupervised or clustering...
Chapter
Full-text available
Thanks to its high spectral resolution, hyperspectral imagery recently has been extremely considered in various remote sensing applications. A fundamental step in the processing of these data is image segmentation through a clustering process. One of the most widely used algorithms for clustering is fuzzy C-Means (FCM). However, the presence of spe...

Questions

Question (1)
Question
I need a simple introduction to active learning (methods, algorithms and applications) in data mining.

Network

Cited By

Projects

Projects (3)
Archived project
Using remotely sensed observation acquired by optical and SAR sensors for mapping land covers and change detection.
Archived project
Algorithm development and enhancement for mapping and detection applications using both satellite and airborne hyperspectral imagery.