1. Optical Remote Sensing

 

 
   
 
   

In the range of 0.4 to 3 micrometers of the electromagnetic spectrum, the possibility of obtaining visible, near-infrared and mid-infrared images is provided by remote sensing. The existence of visible and near-infrared band imaging is an integral part of most non-radar imaging requirements. The importance of imaging in this area is resulted in improved spatial resolution and the advent of satellites with high resolution. Preparation of city and land use maps are the major applications of these images. Also, because of the significance of spectral resolution, hyperspectral remote sensing images play undeniable role in improvement of different land cover maps. The most defined thesis for remote sensing students can concentrate on hyperspectral data processing using new image processing methods and soft computation strategies and their roles in improvement of land use remote sensing products.

 

  1. Thermal Remote Sensing

One of the most important capabilities of remote sensing is image acquisition at night in the thermal infrared range (the range of 3 to 15 micrometers electromagnetic spectrum). According to Planck's law, each object on the earth surface at temperatures above zero degrees Kelvin have thermal radiation that are measured using thermal remote sensing sensors during day and night. Land surface temperature as a thermodynamic quantity was an important indicator of studying energy balance models on earth surface and investigation of greenhouse effects. Also it is the most important parameter for investigation of earth surface reaction and emotions in regional and global scale. For example, the accurate calculation of temperature of snow and ice in the Arctic surface in order to estimation of thermal balance and its relation to climate change on a global scale, is very important. In agriculture, land surface temperature can be used in order to assess the amount of water needed for crops, the drought, and detection of frost in garden. Based on the importance of studying the Earth's surface temperature quantity, in recent years in remote sensing group of Department of Surveying, several theses and dissertations in order to implement and develop new algorithms to extract more accurate sea and surface temperature has been done or are being done. With the advent of new thermal sensor such as LDCM and improvement of spatial, spectral and radiometric resolution of these sensors, new fields for investigation in more accurate temperature reading and basic physical quantities in the field of remote sensing has been provide

 

  1. Microwave Remote Sensing

The range of one millimeter to one meter of electromagnetic spectrum provide the possibility of design, build and launch two active and passive microwave sensors in order to gather earth information. Synthetic Aperture Radar (SAR) because of its specific abilities such as night vision and passing microwaves from the cloud, has been applied in remote sensing researches in recent decades. In the field of remote sensing in three parts: sensors, signal, and applications the following activities are carried out.

 

  • Sensors
    • Feasibility and design of airborne and spaceborne SAR remote sensing sensors and the primary parameter estimation
    • Analysis of ambiguity in designing airborne and spaceborne SAR systems
    • Hardware programming on FPGA in design of a spaceborneSAR system
    • Design and build of Microstrip antenna in an unmanned airborne SAR system
    • Unmanned airborne SAR system simulation (geometry, signal)
    • Spaceborne SAR system simulation (geometry, signal)
  • Signal
    • Design and implementation of radar software to convert raw data to SLC image
    • Compression of SAR signals in different models of collecting signal, in a SAR system.
    • The effect of height (DTM), Foreshortening, Layover in backscatter signals in SAR system
  • Application
    • PolSAR image classification using soft computation remote sensing methods
    • Estimation of soil moisture in flood forecasting using PolSAR images
    • Estimation of runoff from melting snow in flood forecasting using PolSAR images
    • Evaluation of filters for noise reduction in the interferogram to provide DTM and monitoring of displacements
    • Evaluation PolInSAR methods in DEM generation and surface displacement
    • Fusion of PolSAR and optic images in estimation of variables of forested areas
    • Investigation of backscattering models in estimation of  Iran forest biomass using PolSAR data
    • Classification of uniform vegetation areas using PolSAR
    • Investigation of Despeckling filters in effect of extracted features of SAR images to estimate forest biomass
    • Estimation of biomass parameters using InSAR
    • Estimation of forest vegetation parameters in providing tree height map using PolInSAR
    • Identification and extraction of animated objects in SAR images
    • Automatic extraction of linear features using SAR and optical data fusion
    • Object extraction from PolInSAR images
    • Change detection using PolInSAR images
    • Evaluation of geometrical  co-registering methods for PolSAR and PolInSAR
    • Fusion of SAR and optical data in classification
    • Automatic geometrical co-registering  using PolSAR
    • Estimation of soil moisture in agricultural areas using PolSAR
    • Identification of flood areas in forested regions using SAR data