Browsing by Author "Musaoglu, Nebiye"

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MTF Driven Adaptive Multiscale Bilateral Filtering for Pansharpening
(Taylor & Francis Ltd, 2019) Kaplan, Nur Huseyin; Erer, Isin; Ozcan, Orkan; Musaoglu, Nebiye
The recently proposed Bilateral Filter Luminance Proportional (BFLP) method extracts the high-frequency details from panchromatic (Pan) image via a multiscale bilateral filter and adds them proportionally to the multispectral (MS) image. Although this approach seems similar to other multiresolution (MRA) based schemes such as Additive Wavelet proportional Luminance (AWLP) or Generalized Laplacian (GLP) methods, multiscale bilateral filter obtains the detail planes to be injected to MS image by the combination of two Gaussian kernels controlling the transfer of details and performing successively in spatial and range domains, thus it has two parameters to be defined, namely spatial and range parameters. Since the parameter determination step considerably affects the efficiency of the method, in this paper we propose a single parameter bilateral filter by approximating the Gaussian kernel with the bicubic kernel of a trous wavelet transform (ATWT) or modulation transfer function (MTF). Moreover, we adopt an adaptive injection scheme where the range parameter is determined adaptively so as to follow the statistics of the images to be fused. The pansharpening results are compared with ATWT-based methods, as well as some state-of-the-art methods and BFLP. The visual and quantitative comparisons for Systeme Pour l'Observation de la Terre 7 (SPOT 7) and Pleiades 1A images, field studies supported with UAV (Unmanned Aerial Vehicle) images and digitization results of the chosen areas in Istanbul Technical University (ITU) Maslak campus confirm the superiority of the proposed detail injection approach.
Target Detection in Multispectral Images Via Detail Enhanced Pansharpening
(IEEE, 2022) Tarverdiyev, Vazirkhan; Erer, Isin; Kaplan, Nur Huseyin; Musaoglu, Nebiye
Object detection in high resolution satellite images has recently become a major concern in new geospatial information methods. The higher spatial resolution with spectral information provides better detection results. Therefore, increasing the image resolution prior to the object detection is important. For this purpose, pansharpening, which uses complementary information from MS and PAN images, is gaining popularity as it helps to increase spatial resolution while preserving the spectral information. This study proposes a detailed enhanced scheme for pansharpening to improve the detection results. Several deep learning models are trained on raw dataset, as well as on the detail enhanced pansharpened images. It is shown that the training stage using proposed detail enhanced scheme provides better detection results compared to classical pansharpening or raw data based training for different deep networks.