The use of remote sensing in archaeological research allows the prospection of sub-surfaces in arid regions non- intrusively before the on-site investigation and excavation. While the actual detection method of expected buried archaeological structures is based on visual interpretation, this work provides a supporting archaeological guidance using remote sensing. The aim is to detect potential archaeological remains underneath the sand. This paper focuses on Saruq Al-Hadid surroundings, which is an archaeologist site discovered in 2002, located about 50 km south-east of Dubai, as archaeologists believe that other archaeological sites are potentially buried in the surroundings. The input data is derived from a combination of wavelength L-band Synthetic Aperture Radar (ALOS PALSAR), which is able to penetrate the sand, and multispectral optical images (Landsat 7). This paper develops a new strategy to help in the detection of suspected buried structures. The data fusion of surface roughness and spectral indices enables tackling the well-known limitation of SAR images and offers a set of pixels having an archaeological signature different from the manmade structures. The potential buried sites are then classified by performing a pixel-level unsupervised classification algorithm such as K-means cluster analysis. To test the performance of the proposed method, the results are compared with those obtained by visual interpretation.
Nowadays, satellite images are used in various governmental applications, such as urbanization and monitoring the environment. Spatial resolution is an element of crucial impact on the usage of remote sensing imagery. As such, increasing the spatial resolution of an image is an important pre-processing step that can improve the performance of various image processing tasks, such as segmentation. Once a satellite is launched, the more practical solution to improve the resolution of its captured images is to use Single Image Super Resolution (SISR) techniques. In the recent years, Deep Convolutional Neural Networks (DCNNs) have been recognized as a highly effective tool to reconstruct a High Resolution (HR) image from its Low Resolution (LR) counterpart, which is an open problem due to the inherent difficulty of estimating the missing high frequency components. The aim of this research paper is to design and implement a satellite image SISR algorithm by estimating high frequency details through training Deep Convolutional Neural Network (DCNNs) with respect to wavelet analysis. The goal is to improve the spatial resolution of multispectral remote sensing images captured by DubaiSat-2 satellite. The accuracy of the proposed algorithm is assessed using several metrics such as Peak Signal-to-Noise Ratio (PSNR), Wavelet-based Signal-to-Noise Ratio (WSNR) and Structural Similarity Index Measurement (SSIM).
Many works and European projects have proven the ability of Permanent Scatterers Synthetic Aperture Radar
Interferometry (PS-InSAR) to measure the slow deformation of the persistent ground objects with a millimetric precision
measurement. Compared to the classical differential SAR Interferometry (DInSAR), PS-InSAR is an approach that
estimates several contributions: atmospheric disturbances, orbital errors, deformation signal as well as topographical
In this paper, we propose to apply PS-InSAR for the analysis of the ground deformation phenomena in a urban context.
For that purpose, the Stanford Method for Permanent Scatterers (StaMPS) is applied using an ERS data archive. StaMPS
was developed in Stanford University by Andy Hooper. The advantages to use StaMPS were that it is free and many
scripts are already available to process the dataset. In first steps of the processing, differential interferograms are
produced using the Delft Object-oriented Radar Interferometric Software (DORIS), developed in Delft University of
Technology . Doris is also a free tool.
StaMPS method is briefly explained. A first experiment on the city of Paris, France, is presented, especially because PSINSAR
and DInSAR results have already been published by several researchers. Therefore, the processing of Nantes
(French city) is carried out. Some important results are shown.