PIXEL-LEVEL IMAGE FUSION FOR ARCHAEOLOGICAL INTERPRETATIVE MAPPING

[EN] This article reports on the current capabilities and future developments of TAIFU, a MATLAB Toolbox for Archaeological Image FUsion. After introducing the need for archaeological image fusion and the benefits it can bring for the interpretation of archaeological image data, the paper briefly explains some of the major fusion methods that are embedded in TAIFU. Afterwards, additional functionality such as metadata tracking and various pre- and post-processing steps are detailed. The paper concludes with a short roadmap of future TAIFU developments.Verhoeven, G.; Nowak, M.; Nowak, R. (2016). PIXEL-LEVEL IMAGE FUSION FOR ARCHAEOLOGICAL INTERPRETATIVE MAPPING. En 8th International congress on archaeology, computer graphics, cultural heritage and innovation. Editorial Universitat Politècnica de València. 404-407. https://doi.org/10.4995/arqueologica8.2016.3765OCS40440

IMAGE FUSION ALGORITHM FOR FUSION OF PANCHROMATIC AND MULTISPECTRAL IMAGES FOR HIGH SPATIAL INFORMATION WHILE PRESERVING SPECTRAL INFORMATION CONTENT

In this paper image fusion algorithm for enhancing spatial quality of the multispectral image while maintaining the spectral quality of the multispectral image is proposed. The fusion algorithm is developed based on high frequency components injection to the multispectral image to improve the spatial quality of the fused image. High frequency component is generated using the Laplacian filter. Construct the saliency map and initial weight map. Finally optimum weight parameter is calculated for each band using the guided filter, using this optimum weight parameter panchromatic and multispectral images are fused to enhance the spatial quality of the multispectral image

Brain Image Fusion Approach based on Side Window Filtering

Brain medical image fusion plays an important role in framing a contemporary image to enhance the reciprocal and repetitive information for diagnosis purposes. A novel approach using kernel-based image filtering on brain images is presented. Firstly, the Bilateral filter is used to generate a high-frequency component of a source image. Secondly, an intensity component is estimated for the first image. Thirdly, side window filtering is employed on several filters, including the guided filter, gradient guided filter, and weighted guided filter. Thereby minimizing the difference between the intensity component of the first image and the low pass filter of the second image. Finally, the fusion result is evaluated based on three evaluation indexes, including standard deviation (STD), features mutual information (FMI), average gradient (AG). The fused image based on this algorithm contains more information, more details, and clearer edges for better diagnosis. Thus, our fused image-based method is good at finding the position and state of the target volume, which leads to keeping away from the healthy parts and ensuring patients’ soundness

A new pulse coupled neural network (PCNN) for brain medical image fusion empowered by shuffled frog leaping algorithm

Recent research has reported the application of image fusion technologies in medical images in a wide range of aspects, such as in the diagnosis of brain diseases, the detection of glioma and the diagnosis of Alzheimer’s disease. In our study, a new fusion method based on the combination of the shuffled frog leaping algorithm (SFLA) and the pulse coupled neural network (PCNN) is proposed for the fusion of SPECT and CT images to improve the quality of fused brain images. First, the intensity-hue-saturation (IHS) of a SPECT and CT image are decomposed using a non-subsampled contourlet transform (NSCT) independently, where both low-frequency and high-frequency images, using NSCT, are obtained. We then used the combined SFLA and PCNN to fuse the high-frequency sub-band images and low-frequency images. The SFLA is considered to optimize the PCNN network parameters. Finally, the fused image was produced from the reversed NSCT and reversed IHS transforms. We evaluated our algorithms against standard deviation (SD), mean gradient (Ḡ), spatial frequency (SF) and information entropy (E) using three different sets of brain images. The experimental results demonstrated the superior performance of the proposed fusion method to enhance both precision and spatial resolution significantly

Mapping soil erosion in a quaternary catchment in Eastern Cape using geographic information system and remote sensing

In South Africa, soil erosion is considered as an environmental and social problem with serious financial implications particularly in some rural areas where this geomorphological phenomenon is widespread. An example is the Umzimvubu Local Municipality, where most households are strongly reliant on agriculture for their livelihood. Sustainable agriculture and proper land management in these rural areas require information relevant to the spatial distribution of soil erosion. This study was therefore aimed at generating such information using Landsat8 Operational Land Imager (OLI)-derived vegetation indices (VIs) including the Normalised Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), as well as Soil and Atmospherically Resistance Vegetation Index (SARVI). Raster calculator in ArcMap10.2 was used to classify soil erosion features based on selected suitable thresholds in each VI. SPOT6/7 (Satellites Pour l’Obsevation de la Terre) multispectral data and Google Earth images were used for ground truth purposes. SAVI achieved the highest overall classification accuracy of 83% and kappa statistics of 64%, followed by NDVI and SARVI with equal overall accuracy of 81% and slightly different kappa statistics of 60% for the former and 59% for the latter. Using these indices, the study successfully mapped the spatial distribution of soil erosion within the study area albeit there were some challenges due to coarser spatial resolution (15mx15m) of Landsat8 image. Due to this setback, image fusion and pan-sharpening of Landsat8 with higher spatial resolution images is strongly suggested as an alternative to improve the Landsat8 spatial resolution.Keywords: Geographic Information System; Remote Sensing; Soil Erosion; Vegetation Indice