A. Ben Hamida

High spectral quality pansharpening approach based on MTF-matched filter banks

Pansharpening consists in merging a low-resolution multispectral image (MS) with a high spatial resolution panchromatic image (PAN) to produce a high resolution pansharpened MS image. It consists in enhancing spatially the low-resolution MS image by injecting the missing details provided by the high-resolution PAN image. In this paper, we propose a novel pansharpening approach based on decomposition/reconstruction processing using low-pass and high-pass filter banks. On the one hand, the low-pass approximation (taking into account the imaging system modulation transfer function MTF) of the pansharpened MS image is assumed to be equal to the original MS image in order to preserve the spectral quality. On the other hand, the high-pass filter allowing us to extract the high-frequency PAN details is designed as complementary filter to the low-pass one in order to provide perfect reconstruction in the ideal case. Quantitative assessment performed on reduced and full-resolution images are used to validate the proposed technique and compare it to state-of-art. Experimental results using Pléaides and GeoEye-1 data show that our proposed fusion schema outperforms the pre-existing methods visually as well as quantitatively.

Multispectral image adaptive pansharpening based on wavelet transformation and NMDB approaches

In remote sensing, satellite images acquired from sensors provide either high spectral or high spatial resolution. The pansharpening framework is applied to remote-sensing systems to enhance the spatial quality of coarse-resolution multispectral (MS) images using information from panchromatic imagery. A multidecomposition pansharpening approach combining MS and panchromatic (PAN) images is proposed in this paper in order to bring the resolution of the low-resolution MS imagery up to that of the panchromatic images. In particular, multilevel wavelet decomposition is applied to the luminance-chrominance (YUV) space transformation (taking into account the red green and blue (RGB) bands) or extended-YUV transformation (taking into account the near infrared (NIR) band in addition to RGB) of the original MS channels, where geometrical details from the panchromatic image are introduced into the MS ones. Our approach contains a preprocessing step that consists of homogenizing the luminance, Y, and the panchromatic image reflectance, which are, respectively, a value integrated over a wavelength spectrum and simply a linear combination of some values in the same spectrum. Hence, as the panchromatic image reflectance and luminance reflectance correspond to different measurements, they do not correspond to the same physical information, which results in a difference between their histograms. Therefore, simple histogram matching is traditionally applied to panchromatic data to fit it to the luminance to avoid colour distortion after fusion. However, as the transformation concerns just the details of the panchromatic and MS images, a new scheme for matching the images which ignores the divergence between their approximations and maximizes the resemblance between their details is proposed in this work. After that, the fusion approach is applied, and in contrast to the original approach where the details of the fused MS luminance are set equal to the PAN luminance, we propose an adaptive approach in which just a part of the PAN details proportional to the similarity between the luminance and lowered PAN image is taken. Indeed, high-resolution geometrical details cannot be similar if the low-resolution details are not in good agreement. Besides, as the agreement between PAN and MS images depends on the occupation class, we have created a segmentation map and then computed separately the correlation in each region. Finally, the evaluation is done based on QuickBird and Pleiades-1A data sets showing rural and suburban areas. When compared to recent methods, our approach provides better results.

Spatial Resolution Improvement of EEG Source Reconstruction Using swLORETA

Electroencephalography (EEG) and magnetic resonance imaging (MRI) are noninvasive neuro-imaging modalities largely used in neurology explorations. MRI is considered as a static modality and could be so important for anatomy by its high spatial resolution. EEG, on the other hand, is an important tool permitting to image temporal dynamic activities of the human brain. Fusion of these two essential modalities would be hence a so emerging research domain targeting to explore brain activities with the MRI static modality. Our present research investigates a sophisticated approach for localization of the cerebral activity that could be involved by the dynamic EEG modality and carefully illustrated within MRI static modality. Such careful cerebral activity localization would be first based on an advanced methodology yielding therefore a singular value decomposition-based lead field weighting to sLORETA method formalism, for solving in fact the inverse problem in the EEG. The conceived method for source localization, carried out on different cases of simulated dipoles experiments, showed satisfactory results. Different cases of simulated dipoles experiments and metrics were used to confirm the reliability of the proposed method. The experimental results confirm that our method presents a flexible and robust tool for EEG source imaging.

Iterative scheme for MS image pansharpening based on the combination of multi-resolution decompositions

ABSTRACT Image pansharpening in the remote-sensing domain may be defined as the technique of extracting high-resolution details from the panchromatic (PAN) image and injecting them into the multispectral (MS) one in a way to preserve the spectral signature and improve the spatial resolution. In this article, the authors propose an image fusion framework that tries to derive sharpened MS image such that: (i) when decimated taking into account the imagery system Modulation Transfer Function (MTF), it equals the original MS image; (ii) when decomposed using discrete wavelet transform (DWT), its geometrical details are those of the PAN image weighted by the compatibility PAN/MS. Indeed, MS sharpening is carried out in two steps. First, pre-pansharpened MS image is obtained using inverse DWT taking as approximations those of the upsampled original MS image and as details those of PAN (to reduce spectral distortion, PAN detail injection is performed proportionally to the similarity PAN/MS). Second, to satisfy (i) and to remove the PAN-MS disagreement, an iteration algorithm (alternatively corrects approximations and details) has been proposed. The proposed approach is designed in two versions inspired by the Generalized Laplacian Pyramid (GLP) and the Gram–Schmidt (GS) transformation, respectively. To validate our approach, Pléiades-1A, Geoeye-1, and Landsat Enhanced Thematic Mapper Plus (ETM+) images are tested. The results of qualitative and quantitative scores are presented and discussed. Compared to well-known techniques, our approach shows generally better results, particularly the one based on GLP formalism.

Deep learning for semantic segmentation of remote sensing images with rich spectral content

With the rapid development of Remote Sensing acquisition techniques, there is a need to scale and improve processing tools to cope with the observed increase of both data volume and richness. Among popular techniques in remote sensing, Deep Learning gains increasing interest but depends on the quality of the training data. Therefore, this paper presents recent Deep Learning approaches for fine or coarse land cover semantic segmentation estimation. Various 2D architectures are tested and a new 3D model is introduced in order to jointly process the spatial and spectral dimensions of the data. Such a set of networks enables the comparison of the different spectral fusion schemes. Besides, we also assess the use of a “noisy ground truth” (i.e. outdated and low spatial resolution labels) for training and testing the networks.

Towards a new numbering-combinations-based coder for image compression and transmission over a noisy channel

The image compression has always been one of the competitive fields of studies. It aims to represent an image in a minimal size in bytes without compromising the image quality. Our method; the numbering combinations method, consists on calculating all the possible combinations of the pixels and associate an index to our image through all the mentioned combinations. This index is a unique identifier of the image. However, with real images, the index of an image is a big number and it is difficult to manipulate it or to talk about image compression. The idea is to implement our algorithm as an image compression technique and test it in transmission to judge its performances.

Analysis of P band radar signal potential to retrieve soil moisture profile

In this paper, we propose an analysis of P band radar signal potential to retrieve soil moisture root profile. Our analysis is based on two electromagnetic models, the small perturbation method and the small slope approximation. There models consider electromagnetic scattering from three-dimensional layered structures with an arbitrary number of rough surfaces. Simulations are proposed for different types of moisture profiles, for different hydrological conditions.

Effect of soil roughness on backscattered P-band radar signals

In the present paper, the potential use of P-band radar signals for the estimation of soil roughness parameters is analyzed. The (IEM) Integral Equation Model is applied to investigate the sensitivity to soil surface parameters of backscattered P-band signals. A new parameter for roughness referred to as Zp, combining the root mean square surface height and the correlation length, is proposed to describe the behavior of P-band of the radar signals as a function of soil roughness. The IEM model is validated using real data covering a large area of roughness values, derived from experimental airborne P-band SAR campaigns made over agricultural fields. Discrepancies between the measurements and simulations led to the analysis of the impact of low frequency roughness structures on backscattering simulations. The analysis of the behavior of P-band radar signals as a function of multi-scale soil roughness (micro topography and large roughness structures) reveals the complexity of using P-band data for the analysis of bare surface soil parameters.