A Novel Metric Approach Evaluation For The Spatial Enhancement Of Pan-Sharpened Images

Various and different methods can be used to produce high-resolution multispectral images from high-resolution panchromatic image (PAN) and low-resolution multispectral images (MS), mostly on the pixel level. The Quality of image fusion is an essential determinant of the value of processing images fusion for many applications. Spatial and spectral qualities are the two important indexes that used to evaluate the quality of any fused image. However, the jury is still out of fused image's benefits if it compared with its original images. In addition, there is a lack of measures for assessing the objective quality of the spatial resolution for the fusion methods. So, an objective quality of the spatial resolution assessment for fusion images is required. Therefore, this paper describes a new approach proposed to estimate the spatial resolution improve by High Past Division Index (HPDI) upon calculating the spatial-frequency of the edge regions of the image and it deals with a comparison of various analytical techniques for evaluating the Spatial quality, and estimating the colour distortion added by image fusion including: MG, SG, FCC, SD, En, SNR, CC and NRMSE. In addition, this paper devotes to concentrate on the comparison of various image fusion techniques based on pixel and feature fusion technique.Comment: arXiv admin note: substantial text overlap with arXiv:1110.497

Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation

International audienceIn various applications of remote sensing, when high spatial resolution is required in addition with classification results, sensor fusion is a solution. From a set of images with different spatial and spectral resolutions, the aim is to synthesize images with the highest spatial resolution available in the set and with an appropriate spectral content. Several sensor fusion methods exist; most of them improve the spatial resolution but with a poor quality of the spectral content of the resulting image. Based on a multiresolution modeling of the information, the ARSIS concept (from its French name "Amélioration de la Résolution Spatiale par Injection de Structures") was designed in the aim of improving the spatial resolution together with a high-quality in the spectral content of the synthesized images. The general case of application of this concept is described. A quantitative comparison of all presented methods is achieved for a SPOT image. Another example of the fusion of SPOT XS (20 m) and KVR-1000 (2 m) images is given. Practical information for the implementation of the wavelet transform, the multiresolution analysis, and the ARSIS concept by practitioners is given with particular relevance to SPOT and Landsat imagery

Extraction of Information from Multispectral and PAN of Landsat Image for Land Use Classification in the Case of Sodozuria Woreda, Wolaita Sodo, Ethiopia

High-resolution and multispectral remote sensing images are an important data source for acquiring geospatial information for a variety of applications. The satellite images at different spectral and spatial resolutions with the aid of image processing techniques can improve the quality of information. More specifically, image fusion is very helpful to extract the spatial information from two images of different spatial and spectral images of same area. The Image fusion techniques are also helpful in providing classification accurately. In order to improve the information contents of the remote sensing satellite images at a specific spatial resolution, the different resolution image fusion techniques like Wavelet, PC and IHS have been used to combine panchromatic and multispectral datasets of Landsat ETM+ for the purpose of information extraction. The image under study has been used to identify existing Land use types and perform supervised classification. It has then been identified that forest land, farm land, bare land and built-up area are the most dominant land uses in the study area. Based on the supervised classification, classification accuracy assessment has indicated that Original image (MS) produced 83.33% overall accuracy and 0.7500 Kappa coefficient, PC fused image produced 91.67% overall accuracy and 0.875 Kappa coefficient, IHS fused image produced 86.67% overall accuracy and 0.800 Kappa coefficient, Wavelet-PC based transformation produced 91.67% overall accuracy  and   0.875 Kappa coefficient and Wavelet-HIS based  transformation produced 98.33% overall accuracy and 0.975 Kappa coefficient. Moreover, Wavelet-HIS based transformation method has produced relatively higher accuracy. Generally, based on the overall accuracy and kappa coefficient, fused images in terms of classification accuracy at the expense of information content perform by far better than the original image.

Fusion of SPOT5 multispectral and Ikonos panchromatic images

International audienceThe offer of high spectral and high spatial resolutions images has grown in the last decade. It is know possible to obtain data from different sources with different spatial and spec-tral resolutions. The field of data fusion of remotely sensed data grown also very fast in the last years. In this paper, an algorithm allowing the merging of SPOT 5 images and Ikonos images are proposed. This algorithm is based on the ARSIS concept and presents an implementation for a ratio of spatial resolution equal to 10. The ARSIS concept is first detailed. Then, the way of de-fining a new implementation based on this concept is presented, allowing to understand how to define new implementations and to develop new solutions based on this concept. The proposed algorithm is developed, describing the different steps for building a fused product from a SPOT 5 multispectral data at 10 m and from a IKONOS panchromatic data at 1 m. Some other methods are proposed. The evaluation of the quality of the different methods is achieved using a set of quantitative quality parameters. The visual quality of the products are evaluated by a set of inter-preters. Conclusions are drawn on the quality of the proposed products

A hybrid pan-sharpening approach using maximum local extrema

Mixing or combining different elements for getting enhanced version, is practiced across various areas in real life. Pan-sharpening is a similar technique used in the digital world; a process to combine two images into a fused image that comprises more detailed information. Images referred herein are Panchromatic (PAN) and Multispectral (MS) images. This paper presents a pansharpening algorithm which integrates multispectral and panchromatic images to generate an improved multispectral image. This technique merges the Discrete wavelet transform (WT) and Intensity-Hue-Saturation (IHS) through separate fusing criterion for choosing an approximate and detail sub-images. Whereas the maximal local extrema are used for merging detail sub-images and finally merged high-resolution image is reconstructed through inverse transform of wavelet and IHS. The proposed fusion approach enhances the superiority of the resultant fused image is demonstrated by quality measures like CORR, RMSE, PFE, SSIM, SNR and PSNR with the help of satellite Worldview-II images. The proposed algorithm is correlated with the other fusion techniques through empirical outcomes proves the superiority of the final merged image in terms of resolutions than the others