Ana Carolina Siravenha

Evaluating Inpainting Methods to the Satellite Images Clouds and Shadows Removing

This paper presents the evaluation of two approaches widely used in the inpainting literature, applied in the context of atmospheric noise removal, such as fog, dense and sparse clouds and shadows, which often occurs in remote sensing images. One approach uses the technique of nearest neighbor interpolation for the information dissemination by a DCT-based smoothing method, and the other is based on second-order partial differential equations methods that uses the heat diffusion and thin-plate spline methods, achieving their solutions by using the finite-difference method. Finally, the evaluation uses the Kappa coefficient and the PSNR index. The metrics indicate the effectiveness of the nearest neighbor interpolation strategy, which produces higher quality images, specially when comparing the results obtained by the use of differential equations modeled by thin-plate spline.

The use of high-pass filters and the inpainting method to clouds removal and their impact on satellite images classification

This paper proposes a new technique to smooth undesirable elements of the atmosphere, such as fogs, clouds and shadows, which damage and lead to loss of image data. In our approach, an efficient way to detect clouds and shadows is presented. The method applies constants related to such undesirable elements, as well as a High boost Filter in the homomorphic filtering for scattered clouds removal. We highlight the use of the Inpainting method, which replaces contaminated pixels using a nearest neighbor interpolation. Beside this, the proposed algorithm adopts a morphologic opening of the image that aims to suppress some isolated occurrences in the scene. The results are evaluated by Kappa coefficient and PSNR index, proving the good performance of the method.

Comparing Different High-Pass Filters to Improve the Accuracy of Classification of Satellite Imagery Obstructed by Clouds and Fog

This work has as main objective to overcome a frequent problem in remote sensing, which is the undesirable presence of atmospheric constituents as scattered clouds, fogs and mists. The presence of such elements can affect the urban and environmental monitoring, as well as subsequent steps of the digital image processing such as segmentation and classification, main responsible for extracting information of the image. Therefore, is presented a technique to detect these elements, which uses statistical measures and morphological filters. To the removal or smoothing these atmospheric elements is applied a homomorphic filter. Motivating the present work, is presented a comparative analysis of the widely used high-pass filters in homomorphic filtering, Ideal and Butterworth, with the alternative High-boost filter. The results are evaluated by the Kappa coefficient and PSNR index, pointing to the High-boost filter as the best approach to use.

Analysing environmental changes in the neighbourhood of mines using compressed change vector analysis: case study of Carajas Mountains, Brazil

ABSTRACT Remote sensing image databases and geographic information system have the potential to act as accurate tools for environmental monitoring. Carajas Mountains are an important mineral deposit in Brazil and as environmental laws protect a great portion of this region, they have been at the core of conflicts involving human and nature interests. The biggest mining project in Brazil is active in this region (Carajas project) and this analysis aims at identifying the environmental impact caused directly or indirectly by this activity using state-of-the-art methods. This study collects information of land-use and land-coverage from an area larger than 111,000 km2 including five municipalities, aiming at observing the landscape intervention from a large-scale perspective as a counterpoint to other studies which are focused on a particular region, such as watersheds. Therefore, employing the resultant products of the multispectral approach called Compressed change vector analysis analyses both the environmental changes in each studied municipality of the Carajas Mountains and the environmental counterpart of the company that runs the mining activity. The results show that in general the vegetative coverage was replaced by pasture lands, which in turn were replaced by urban occupations. The comparison with official statistics indicates good accuracy of the present study in the estimation of vegetative cover, although the authors claim that the official methodology can produce inaccurate percentages, probably due to the shortcoming of classification of degraded forests and forest in regeneration process. The presence of environmentally protected areas has prevented the increase of deforestation in the mountains, in which the observed change rates were at least 15% lower than non-protected regions.

The Development of a Hybrid Solution to Replacement of Clouds and Shadows in Remote Sensing Images

Nowadays, many works are dedicated to improve the research results, previously achieved manually, by computational solutions. On light of this, the presented work aims to overcome a common problem in many satellite images, which is the presence of undesirable atmospheric components such as clouds and shadows at the time of scene capture. The presence of such elements hinders the identification of meaningful information for applications like urban and environmental monitoring, exploration of natural resources, etc. Thus, it is presented a new way to perform a hybrid approach toward removal and replacing of these elements. The authors propose a method of regions decomposition using a nonlinear median filter, in order to map regions of structure and texture. These types of regions will explain which method will be applied to region redefinition. At structure region, will be applied the method of inpainting by a smoothing based on DCT, and at texture one, will be applied the exemplar-based texture synthesis. To measure the effectiveness of this proposed technique, a qualitative assessment was presented, at the same time that a discussion about quantitative analysis was made.

A fuzzy segmentation tool for remote sensing data

Remote sensing data are an important source of information for a variety of applications, such as coastal mapping applications, monitor land use, and chart wildlife habitats, for example. One of the most important task for these data analysis is the segmentation. Segmentation means the action of merging neighbouring pixels into segments (or regions), based on their homogeneity or heterogeneity parameters. Traditional image segmentation methods looks for delineating discrete image objects with sharp edges, which cannot be always possible, mainly considering that many geographic objects, both natural and man-made, may not appear clearly bounded in remotely sensed images. A fuzzy approach seems natural in order to capture the structure of objects in the image and takes into account the fuzziness of the real world and the ambiguity of remote sensing imagery. The main goal of this work is define boundaries of objects in an image. This proposal aims to be faster than other segmentation approaches inside the TerraLib tools by considering only the neighbourhood of a selected pixel. This work proposes the use of images tone and colour to select and define objects in remote scenes based on fuzzy rules. The fuzzy set is defined by an input tolerance level, which can be adjustable according to the desired granularity of the selection. The proposal methodology is not limited by the selection of only one object, that is, the mask can be designed by a set of objects with different features and tolerances. The algorithm also returns the objects size and proportion. The quality of the individual segmentation results is evaluated based on multi-spectral Landsat 5-TM, Landsat 7-ETM+ and CBERS data. This is done by visual comparison, which is supplemented by a detailed investigation using visual interpreted reference areas.