Author
R. J. Bhiwani
Bio: R. J. Bhiwani is an academic researcher from Sant Gadge Baba Amravati University. The author has contributed to research in topics: Image fusion & Panchromatic film. The author has an hindex of 2, co-authored 6 publications receiving 72 citations.
Papers
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TL;DR: This paper is an honest attempt to collectively discuss all possible algorithms along with quality metrics following two assessment procedures i.e. at full and reduced scale resolutions to evaluate performance of these algorithms.
Abstract: Major technical constraints like minimum data storage at satellite platform in space, less bandwidth for communication with earth station, etc. limits the satellite sensors from capturing images with high spatial and high spectral resolutions simultaneously. To overcome this limitation, image fusion has proved to be a potential tool in remote sensing applications which integrates the information from combinations of panchromatic, multispectral or hyperspectral images; intended to result in a composite image having both higher spatial and higher spectral resolutions. The research in this area cites date back to last few decades, but the diverse approaches proposed so far by different researchers have been rarely discussed at one place. This paper is an honest attempt to collectively discuss all possible algorithms along with quality metrics following two assessment procedures i.e. at full and reduced scale resolutions to evaluate performance of these algorithms.
73 citations
01 Jul 2019
TL;DR: Comparing performances of the most popular image fusion algorithms using Component Substitution approach is compared and will surely help in evaluation of the best possible trade-off in between spectral resolution and spatial resolution for the fused image by selected algorithms.
Abstract: The fusion of multispectral and panchromatic images acquired on common location to form resultant image that features higher spectral as well as higher spatial resolutions is also referred to as ‘Pansharpening’. In the recent past, many such fusion algorithms belonging to various approaches have been proposed. In this paper, the authors compare performances of the most popular image fusion algorithms using Component Substitution approach. Selected algorithms are applied to two real datasets, acquired by WorldView-3 and WorldView-4 satellite sensors. The performances are evaluated based on popular image quality metrics. This performance analysis will surely help in evaluation of the best possible trade-off in between spectral resolution and spatial resolution for the fused image by selected algorithms.
7 citations
TL;DR: In this article, the morphological operator-based image fusion algorithm featuring nonlinear decomposition is proposed for remotely sensed image processing applications, where the use of morphologically operator based spatial filtering is successfully demonstrated for efficiency enhancement of the proposed and a few of the sophisticated image fusion algorithms based on Component Substitution (CS), Multi-Resolution Analysis (MRA), and state-of-the-art deep learning approach.
5 citations
01 Nov 2019
TL;DR: The authors attempt to validate use of image segmentation in pansharpening and compare performance of fusion process with other most recent algorithms.
Abstract: Fusion of multispectral to panchromatic image obtained for the same location, that is ultimately producing new multispectral image with added details of spatial resolution is best known as ‘Pansharpening’. Developing such algorithms for image fusion and assessing quality by objective metrics are the most debated topics of research from the last few decades. In this paper, the authors attempt to validate use of image segmentation in pansharpening and compare performance of fusion process with other most recent algorithms. Fusion algorithms are tested on two datasets, acquired by QuickBird-2 and WorldView-2 respectively. Evaluations of corresponding results are based on computations of popular image quality metrics. The analysis will surely help to know the best possible trade-off between spatial and spectral resolutions for desired fused image.
4 citations
TL;DR: A pansharpening algorithm based on morphological extended-half-gradient that offers improved image fusion than using the morphological half-gradient is proposed and proves the potential of morphological image processing operations to be useful in the achievement of efficient panshARPening.
Abstract: Pansharpening refers to the fusion of remotely sensed multispectral and panchromatic images which are characterized by different levels of spectral–spatial resolutions and acquired for the same location by optical remote sensing satellite sensors. In this paper, we propose a pansharpening algorithm based on morphological extended-half-gradient. Popular quality metrics employing two assessment methods, namely reduced resolution assessment and full resolution assessment, are used for performance measurement. For validating the efficiency of the proposed algorithm, we compare its performance with that of morphological half-gradient-based fusion procedure and a few other popular image fusion algorithms. We also propose the best possible bias factor in the formulation of the proposed algorithm by experimentation on varied values. Three real datasets acquired by WorldView-4, SPOT-6 and QuickBird-2 are used in the experimentation. The results affirm that the proposed algorithm offers improved image fusion than using the morphological half-gradient. This successful demonstration of the proposed algorithm proves the potential of morphological image processing operations to be useful in the achievement of efficient pansharpening. This work also underlines the need for more computational efficiency in image fusion.
1 citations
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TL;DR: A comprehensive survey of multi-scale and non-multi-scale decomposition-based image fusion methods in detail is demonstrated and would form basis for stimulating and nurturing advanced research ideas in image fusion.
Abstract: Image fusion is a well-recognized and a conventional field of image processing. Image fusion provides an efficient way of enhancing and combining pixel-level data resulting in highly informative data for human perception as compared with individual input source data. In this paper, we have demonstrated a comprehensive survey of multi-scale and non-multi-scale decomposition-based image fusion methods in detail. The reference-based and non-reference-based image quality evaluation metrics are summarized together with recent trends in image fusion. Several image fusion applications in various fields have also been reported. It has been stated that though a lot of singular fusion techniques seemed to have given optimum results, the focus of researchers is shifting toward amalgamated or hybrid fusion techniques, which could harness the attributes of both multi-scale and non-multi-scale decomposition methods. Toward the end, the review is concluded with various open challenges for researchers. Thus, the descriptive study in this paper would form basis for stimulating and nurturing advanced research ideas in image fusion.
127 citations
TL;DR: It is concluded that there is scope for further research of fusion of SAR and optical images due to various microwave and optical sensors with the improved resolution being launched regularly.
121 citations
TL;DR: The Sentinel-2 satellite currently provides freely available multispectral bands at relatively high spatial resolution but does not acquire the panchromatic band as discussed by the authors, which is needed to improve the resolution.
Abstract: The Sentinel-2 satellite currently provides freely available multispectral bands at relatively high spatial resolution but does not acquire the panchromatic band. To improve the resolution ...
86 citations
TL;DR: Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images and a wide range of algorithms are investigated, including 41 methods investigated, which indicate that MRA-based methods performed better in terms of spectral quality, whereas most Hybrid-based method had the highest spatial quality and CS- based methods had the lowest results both spectrally and spatially.
Abstract: Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images. Selecting an appropriate algorithm that maintains the spectral and spatial information content of input images is a challenging task. This review paper investigates a wide range of algorithms, including 41 methods. For this purpose, the methods were categorized as Component Substitution (CS-based), Multi-Resolution Analysis (MRA), Variational Optimization-based (VO), and Hybrid and were tested on a collection of 21 case studies. These include images from WorldView-2, 3 & 4, GeoEye-1, QuickBird, IKONOS, KompSat-2, KompSat-3A, TripleSat, Pleiades-1, Pleiades with the aerial platform, and Deimos-2. Neural network-based methods were excluded due to their substantial computational requirements for operational mapping purposes. The methods were evaluated based on four Spectral and three Spatial quality metrics. An Analysis Of Variance (ANOVA) was used to statistically compare the pan-sharpening categories. Results indicate that MRA-based methods performed better in terms of spectral quality, whereas most Hybrid-based methods had the highest spatial quality and CS-based methods had the lowest results both spectrally and spatially. The revisited version of the Additive Wavelet Luminance Proportional Pan-sharpening method had the highest spectral quality, whereas Generalized IHS with Best Trade-off Parameter with Additive Weights showed the highest spatial quality. CS-based methods generally had the fastest run-time, whereas the majority of methods belonging to MRA and VO categories had relatively long run times.
75 citations
TL;DR: In this paper, the authors examined the history and progress of various geospatial techniques applied to monitor and evaluate karst vegetation conditions and reviewed the techniques used to as well.
Abstract: The karst region in southwestern China, one of the largest continuous karst areas in the world, is special for its high landscape heterogeneity, unique hydrology, high endemism among vegetation species and high intensity of human disturbance. The region had experienced severe degradation through karst rocky desertification (KRD) between the 1950s and 1990s. Starting in the late 1990s, various levels of the Chinese government conducted several ecological projects to recover degraded karst ecosystems. It was reported that the implementation of these projects had been successful in facilitating the recovery of karst vegetation in many areas. However, global climate changes may compromise the efficacy of recovery. Geospatial techniques had been employed to map and monitor karst ecosystem conditions during the recovery process. We examined the history and progress of the various geospatial techniques applied to monitor and evaluate karst vegetation conditions. In addition, we reviewed the techniques used to as...
52 citations