Toward reduction of artifacts in fused images

Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e.artifacts, in the resulting fusedimages. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied through the fusion methodology based on the discrete wavelet transform (DWT), using the à trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to the roughness and shape of the regions present in the image to be fused. This improves the quality of the fusedimages and their classification results when compared with the original WAT metho

Compressive Sensing for PAN-Sharpening

Based on compressive sensing framework and sparse reconstruction technology, a new pan-sharpening method, named Sparse Fusion of Images (SparseFI, pronounced as sparsify), is proposed in [1]. In this paper, the proposed SparseFI algorithm is validated using UltraCam and WorldView-2 data. Visual and statistic analysis show superior performance of SparseFI compared to the existing conventional pan-sharpening methods in general, i.e. rich in spatial information and less spectral distortion. Moreover, popular quality assessment metrics are employed to explore the dependency on regularization parameters and evaluate the efficiency of various sparse reconstruction toolboxes

High Resolution Satellite Images to Reconstruct Recent Evolution of Domitian Coastline

In the last decades, combinations of natural and human factors have resulted in extensive morphological changes to our coastlines and in many cases have amplified erosion. In order to limit these changes and their impact on coastal zone, it is important to plan specific actions; for this purpose detailed cognizance of coastal zone is necessary. Different and heterogeneous data such as historical and recent maps, remotely sensed images and topographic survey result very useful to reconstruct temporal shoreline changes. In this study the attention is focalized on Domitian coastal zone (Italy), which is one of the most emblematic examples of coastal erosion in Europe. Study of the shoreline evolution in this area between 1876 and 2005 was used as the starting point of the present paper that investigates over a span of seven years (2005 to 2012), by using remotely sensed data. The aim is to adapt and integrate geomatics techniques to transform very high resolution satellite images in powerful tools to analyse coastline changes. So, in order to identify eroded and added areas, IKONOS-2 (2005), GeoEye-1 (2011) and WorldView-2 (2012) imageries are compared. These data-sets were re-georeferred to improve the positional accuracy. More over Normalized Difference Water Index (NDWI) was applied to pan-sharpened multispectral images to facilitate coastline vectorising at the same geometric resolution of panchromatic data. In addition, variance propagation was considered to establish the accuracy of the reconstruction of coastal evolution. Added and eroded areas were defined and related to the impact of the defence structures that were built in this zone in 2011

MAP Estimation for Hyperspectral Image Resolution Enhancement Using an Auxiliary Sensor

This paper presents a novel maximum a posteriori (MAP) estimator for enhancing the spatial resolution of an image using co-registered high spatial-resolution imagery from an auxiliary sensor. Here we focus on the use of high-resolution panchomatic data to enhance hyperspectral imagery. However, the estimation framework developed allows for any number of spectral bands in the primary and auxiliary image. The proposed technique is suitable for applications where some correlation, either localized or global, exists between the auxiliary image and the image being enhanced. To exploit localized correlations, a spatially varying statistical model, based on vector quantization, is used. Another important aspect of the proposed algorithm is that it allows for the use of an accurate observation model relating the “true” scene with the low-resolutions observations. Experimental results with hyperspectral data derived from the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) are presented to demonstrate the efficacy of the proposed estimator

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin