Improving the quality of remote sensing images using a universal reconstruction method (original) (raw)

A MAP-Based Approach for Hyperspectral Imagery Super-resolution

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 2018

In this study, we propose a novel single image Bayesian super-resolution (SR) algorithm where the hyperspectral image (HSI) is the only source of information. The main contribution of the proposed approach is to convert the ill-posed SR reconstruction (SRR) problem in the spectral domain to a quadratic optimization problem in the abundance map domain. In order to do so, Markov Random Field (MRF) based energy minimization approach is proposed and proved that the solution is quadratic. The proposed approach consists of five main steps. First, the number of endmembers in the scene is determined using virtual dimensionality. Second, the endmembers and their low resolution abundance maps are computed using simplex identification via the splitted augmented Lagrangian (SISAL) and fully constrained least squares (FCLS) algorithms. Third, high resolution (HR) abundance maps are obtained using our proposed maximum a posteriori (MAP) based energy function. This energy function is minimized sub...

Remotely Sensed Image Inpainting With MNLTV Model

—Image processing is an significant component of modern technologies as it provides the perfection in pictorial information for human interpretation and processing of image data for storage, transmission and representation. In remotely sensed images because of poor atmospheric condition and sensor malfunction (Instrument error such as SLC-OFF failure on may13,2003 the scan line corrector (SLC) of LANDSAT7 Enhanced Thematic Mapper Plus (ETM+) sensor failed permanently causing around 20% of pixel not scanned which become called dead pixels) there is usually great deal of missing information which reduce utilization rate. Remotely sensed images often suffer from strip noise, random dead pixels. The techniques to recover good image from contaminated one are called image destriping for strips and image inpainting for dead pixels, therefore reconstruction of filling dead pixels and removing uninteresting object is an important issue in remotely sensed images. In past decades, missing information reconstruction of remote sensing data has become an active research field and large number of algorithms have been developed. This paper presented to solve image destriping, image inpainting and removal of uninteresting object based on multichannel nonlocal total variation. In this algorithm nonlocal method considered, which has superior performance in dealing with textured images. To optimize variation model a Bregmanized-operator-splitting algorithm is employed. Furthermore proposed inpainting algorithm is used for text removal, scratch removal, pepper and salt noise removal, object removal etc. The proposed inpainting algorithm was tested on simulated data.

Universal reconstruction method for radiometric quality improvement of remote sensing images

International Journal of Applied Earth Observation and Geoinformation, 2010

In order to improve signal-to-noise ratio ͑SNR͒ and contrastto-noise ratio, we introduces a novel tunable forward-and-backward ͑TFAB͒ diffusion approach for image restoration and edge enhancement. In the TFAB algorithm, an alternative forward-and-backward ͑FAB͒ diffusion process is presented, where it is possible to better modulate all aspects of the diffusion behavior and it shows better algorithm behavior compared to the existing FAB diffusion approaches. In addition, there is no necessity to laboriously determine the value of the gradient threshold. We believe the TFAB diffusion to be an adaptive mechanism for image restoration and enhancement. Qualitative experiments, based on various general digital images and a magnetic resonance image, show significant improvements when the TFAB diffusion algorithm is used versus the existing anisotropic diffusion and the previous FAB diffusion algorithms for enhancing edge features and improving image contrast. Quantitative analyses, based on peak SNR and the universal image quality index, confirm the superiority of the proposed algorithm.

A Survey on Image Inpainting Techniques to Reconstitute Remotely Sensed Images

2016

Modification of digital image is general practice with various intentions now days because of extensive availability of sophisticated image editing tools. Image inpainting is one of the approaches to recuperate lost or damaged part of image in order to make it visually plausible and restore its unity. Nonresponsive pixel (dead pixel) or removing unwanted objects from digital image is frequently preferred in the application of remote sensing. Remotely sensed images are suffered due to stripping and dead pixels. To solve this problem many image inpainting techniques are proposed to recover damaged or stripping images. In this paper we provide a survey of different techniques used for remotely sensed image inpainting like Exampler based, PDE based, Structure based, Texture

Decimation Estimation and Super-Resolution Using Zoomed Observations

2006

We propose a technique for super-resolving an image from several observations taken at different camera zooms. From the set of these images, a super-resolved image of the entire scene (least zoomed) is obtained at the resolution of the most zoomed one. We model the super-resolution image as a Markov Random Field (MRF). The cost function is derived using a Maximum a posteriori (MAP) estimation method and is optimized by using gradient descent technique. The novelty of our approach is that the decimation (aliasing) matrix is obtained from the given observations themselves. Results are illustrated with real data captured using a zoom camera. Application of our technique to multiresolution fusion in remotely sensed images is shown.

Decimation Estimation and Linear Model-Based Super-Resolution Using Zoomed Observations

2007

In this paper we present a model based approach for super-resolving an image from a sequence of zoomed observations. From a set of images taken at different camera zooms, we super-resolve the least zoomed image at the resolution of the most zoomed one. Novelty of our approach is that decimation matrix is estimated from the given observations themselves. We model the most zoomed image as an autoregressive (AR) model, learn the parameters and use in regularization to super-resolve the least zoomed image. The AR model is computationally less intensive as compare to Markov Random Field (MRF) model hence the approach can be employed in real-time applications. Experimental results on real images with integer zoom settings are shown. We also show how the learning of AR parameters in subblocks using Panchromatic (PAN) image gives better results for the multiresolution fusion process in remote sensing applications.

Parameter estimation in Bayesian reconstruction of multispectral images using super resolution techniques

… Processing, 2006 IEEE …, 2006

In this paper we present a new super resolution Bayesian method for pansharpening of multispectral images which: a) incorporates prior knowledge on the expected characteristics of the multispectral images, b) uses the sensor characteristics to model the observation process of both panchromatic and multispectral images, and c) performs the estimation of all the unknown parameters in the model. Using real data, the pansharpened multispectral images are compared with the images obtained by other parsharpening methods and their quality is assessed both qualitatively and quantitatively.

Variational posterior distribution approximation in Bayesian super resolution reconstruction of multispectral images

Applied and Computational …, 2008

In this paper we present a super resolution Bayesian methodology for pansharpening of multispectral images. By following the hierarchical Bayesian framework, and by applying variational methods to approximate probability distributions this methodology is able to: (a) incorporate prior knowledge on the expected characteristics of the multispectral images, (b) use the sensor characteristics to model the observation process of both panchromatic and multispectral images, (c) include information on the unknown parameters in the model in the form of hyperprior distributions, and (d) estimate the parameters of the hyperprior distributions on the unknown parameters together with the unknown parameters, and the high resolution multispectral image. Using real data, the pansharpened multispectral images are compared with the images obtained by other pansharpening methods and their quality is assessed both qualitatively and quantitatively.

Super-Resolution of Remotely Sensed Images With Variable-Pixel Linear Reconstruction

IEEE Transactions on Geoscience and Remote Sensing, 2007

This paper describes the development and applications of a super-resolution method, known as Super-Resolution Variable-Pixel Linear Reconstruction. The algorithm works combining different lower resolution images in order to obtain, as a result, a higher resolution image. We show that it can make significant spatial resolution improvements to satellite images of the Earth's surface allowing recognition of objects with size approaching the limiting spatial resolution of the lower resolution images. The algorithm is based on the Variable-Pixel Linear Reconstruction algorithm developed by Fruchter and Hook, a well-known method in astronomy but never used for Earth remote sensing purposes. The algorithm preserves photometry, can weight input images according to the statistical significance of each pixel, and removes the effect of geometric distortion on both image shape and photometry. In this paper, we describe its development for remote sensing purposes, show the usefulness of the algorithm working with images as different to the astronomical images as the remote sensing ones, and show applications to: 1) a set of simulated multispectral images obtained from a real Quickbird image; and 2) a set of multispectral real Landsat Enhanced Thematic Mapper Plus (ETM+) images. These examples show that the algorithm provides a substantial improvement in limiting spatial resolution for both simulated and real data sets without significantly altering the multispectral content of the input low-resolution images, without amplifying the noise, and with very few artifacts.

Hierarchical Bayesian super resolution reconstruction of multispectral images

2006 European Signal …, 2006

In this paper we present a super resolution Bayesian methodology for pansharpening of multispectral images which: a) incorporates prior knowledge on the expected characteristics of the multispectral images, b) uses the sensor characteristics to model the observation process of both panchromatic and multispectral images, c) includes information on the unknown parameters in the model, and d) allows for the estimation of both the parameters and the high resolution multispectral image. Using real data, the pansharpened multispectral images are compared with the images obtained by other parsharpening methods and their quality assessed both qualitatively and quantitatively.