Remote sensing and landsat image enhancement using multiobjective PSO based local detail enhancement

Malik, Rahul; Dhir, Renu; Mittal, S. K.

doi:10.1007/s12652-018-1082-y

Cited by 18 publications

(7 citation statements)

References 22 publications

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“…δ = (N GP − N M P ) divided (N GP − N M P ) undivided X 100 (7) where NGP means the amount of terrain points and NMP is the amount of ground points that are mis-classified. Table 4 shows the latest metric described above, using various ISPRS datasets.…”

Section: B Evaluation Of Boundary Error Correctionmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

et al. 2020

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There is a need for improvement of tools to deal with large volumes of multimedia data effectively. In particular, real-time data processing is one of the major problems for multimedia data computing in remote sensing systems. Such big data systems have to offer effective management and computational efficiency for applications in real-time. In this paper, we propose a large-scale geological processing method for aerial Light Detection and Ranging (LiDAR) clouds containing multimedia data that ensures mobility and timeliness. By utilizing Spark and Cassandra, our proposed approach can significantly reduce the execution time of the time-consuming process. We investigate fast ground-only raster generation from huge LiDAR datasets. We observed that filtered cloud data ensuing from impartial consideration of neighboring zones could lead to classification errors on the boundaries. Therefore, an integrated approach is proposed to correct these errors to improve the classification consistency, achieve faster processing time, provide automatic error correction, obtain Digital Terrain Models (DTM), and minimize user intervention. These features can provide a framework for an on-demand DTM output and scalable application services. Furthermore, the proposed approach can expect to benefit other real-time applications in LiDAR systems.

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Section: B Evaluation Of Boundary Error Correctionmentioning

confidence: 99%

Notice of Violation of IEEE Publication Principles: Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

et al. 2020

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“…For AMBE, the lower value indicates better results, however, the larger values of PSNR, Entropy and SSIM represent good results. For fair comparison, we have implemented the methods proposed in [36]- [43]. In [36], the authors used sigmoid function to increase the contrast.…”

Section: Methodsmentioning

confidence: 99%

“…For fair comparison, we have implemented the methods proposed in [36]- [43]. In [36], the authors used sigmoid function to increase the contrast. Afterward, the multi-objective PSO and gamma correction is applied to maximize the information content and preserve the image intensity respectively.…”

Section: Methodsmentioning

confidence: 99%

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

et al. 2019

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Contrast enhancement is important and plays vital role in many applications. Histogram equalization-based techniques are widely used techniques for contrast enhancement. However, it faces the contrast over-stretching, which in return causes the loss of details and unnatural look to the target image. To address this issue, this work presents a novel scheme for image contrast enhancement. The contribution of the proposed scheme is twofold. First, the image can lose many important information when an image size is decreased. For that, the image is transformed from spatial to wavelet domain so that the multi-resolution can be achieved. Second, Gamma correction is a proven technique that produces natural look and preserves mean brightness of an image with the choice of optimal gamma values. Here, Particle Swarm Optimization (PSO) is utilized to select the optimal gamma values. In this study, an effective fitness function is proposed to maximize the performance of PSO. Experimental findings show that the proposed approach improve the image contrast up to a greater extent without introducing any artifacts.

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“…Exponential center symmetric inertia weight based PSO [233], Multi-objective PSO [254] PSO with blind deconvolution technique [255] Modified PSO [256], Hybridized PSO with mutation operator [257], adaptive PSO with fuzzy logic [258], PSO-GA [259] Enhanced PSO [260], improved PSO [230] PSO with Haar wavelet transform [234] Multi-dimensional PSO [235], guided dynamic PSO [261] Electrical Power Systems Economic dispatch Improved quantum PSO [237], evolutionary PSO [238], improved random drift PSO [239], hybrid PSO and gravitational search algorithm [130], improved PSO with a biogeography learning strategy [264], cultural quantum-behaved PSO [265], modified PSO with time varying acceleration coefficients (MPSO-TVAC) [134], hybrid double-weighted PSO [266] Optimal Power flow Modified PSO [240], hybrid PSO-dragonfly algorithm [241], hybrid PSO-artificial physics optimization [242], hybrid PSO-pattern search algorithm [267], enhanced PSO with stochastic weight and chaotic mutation [268].…”

Section: Image Watermarkingmentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

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Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

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Remote sensing and landsat image enhancement using multiobjective PSO based local detail enhancement

Cited by 18 publications

References 22 publications

Notice of Violation of IEEE Publication Principles: Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

Notice of Violation of IEEE Publication Principles: Reversible Data Hiding and Smart Multimedia Computing Using Big Data in Remote Sensing Systems

An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

Particle Swarm Optimization: A Comprehensive Survey

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