Image fusion is one of the most commonly used image enhancement techniques for improving the spatial quality of the source image with minimal spectral distortion in remote sensing. Until now, data fusion algorithms were developed and applied to improve the spatial resolution of the multispectral images and also their performances were evaluated depending on the source images such as Landsat Enhanced Thematic Mapper Plus (ETM+), Landsat Multispectral (MS)/Panchromatic (PAN), Satellite pour l'Observation de la Terre (SPOT) XS/PAN and IKONOS MS/PAN datasets. This paper assesses whether hyperspectral images, having very narrow bands compared to multispectral images, can be fused with high spatial resolution panchromatic images using common and current new algorithms including Intensity‐Hue Saturation (IHS), Principal Component Substitution (PCS), Gram Schmidt Transformation (GST), Smoothing Filter‐based Intensity Modulation (SFIM), Discrete Wavelet Transform (DWT), wavelet‐based IHS (DWT‐IMS) and PCS (DWT‐PCS) and Fast Fourier Transform (FFT)‐enhanced IHS. We also examine the performance of the fused hyperspectral images with respect to the fused multispectral images. For this purpose, two different source datasets (EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN) were used. Some qualitative and quantitative analyses were implemented to assess the spatial and spectral quality of the fused images. The results show that it was possible to carry out the fusion of a narrow‐band hyperspectral image and a high spatial resolution panchromatic image. The fusion of EO1 Hyperion/ALI PAN and EO1 ALI MS/PAN datasets using the SFIM, DWT‐PCS, DWT‐IHS and FFT‐IHS algorithms produces better results than other techniques. Also, the results show that the fusion methods behaved for both datasets in the same performances except the DWT algorithm. The DWT method has a lower performance for the hyperspectral image compared to the multispectral image. Therefore the DWT algorithm should be further studied to improve the spectral qualities of a fused hyperspectral image based on wavelet transformation.

This study aims to compare classification accuracies of land cover/use maps created from Sentinel-2 and Landsat-8 data. Istanbul metropolitan city of Turkey, with a population of around 14 million, having different landscape characteristics was selected as study area. Water, forest, agricultural areas, grasslands, transport network, urban, airport- industrial units and barren land- mine land cover/use classes adapted from CORINE nomenclature were used as main land cover/use classes to identify. To fulfil the aims of this research, recently acquired dated 08/02/2016 Sentinel-2 and dated 22/02/2016 Landsat-8 images of Istanbul were obtained and image pre-processing steps like atmospheric and geometric correction were employed. Both Sentinel-2 and Landsat-8 images were resampled to 30m pixel size after geometric correction and similar spectral bands for both satellites were selected to create a similar base for these multi-sensor data. Maximum Likelihood (MLC) and Support Vector Machine (SVM) supervised classification methods were applied to both data sets to accurately identify eight different land cover/ use classes. Error matrix was created using same reference points for Sentinel-2 and Landsat-8 classifications. After the classification accuracy, results were compared to find out the best approach to create current land cover/use map of the region. The results of MLC and SVM classification methods were compared for both images.