Contrast Limited Adaptive Histogram Equalization technique (CLAHE) is a widely used form of contrast enhancement, used predominantly in enhancing medical imagery like X-rays and to enhance features in ordinary photographs. This work is aimed to understand the effectiveness of using this technique in multispectral satellite imagery and to study its effectiveness in different regions of the electromagnetic spectrum. This work also aimed in analyzing variations of spatial and spectral resolutions of a sensor affect the performance of the CLAHE technique by means of comparing quantitative parameters of the enhanced images between the sensors. A new performance parameter called Degree of Contrast Enhancement (DCE) has also been formulated so as to quantify the amount of increase/decrease in contrast between the enhanced and original images on application of the CLAHE algorithm on it. A general idea of the feature that can be enhanced in each spectral region was also studied. The results showed that the technique was most effective for shorter wavelengths when compared to longer wavelength regions. A comparative study between the CLAHE technique and the conventional global histogram equalization technique resulted in the former technique emerging superior of the two and thereby reconstructed images of better quality.
Inhaltsverzeichnis (Table of Contents)
- 1 Introduction
- 1.1 Global Histogram Equalization
- 1.2 Adaptive Histogram Equalization
- 1.3 Contrast Limited Adaptive Histogram Equalization
- 2 Data Acquisition
- 3 Methodology
- 3.1 Mean Square Error (MSE)
- 3.2 Peak Signal-to-Noise Ratio (PSNR)
- 3.3 Degree of Contrast Enhancement (DCE)
- 4 Comparative Analysis
- 5 Results and Inferences
- 6 Conclusions
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This report investigates the effectiveness of Contrast Limited Adaptive Histogram Equalization (CLAHE) on multispectral satellite imagery. The main objective is to understand how CLAHE performs across different electromagnetic spectrum regions and how variations in spatial and spectral resolutions affect its performance. A new performance metric, Degree of Contrast Enhancement (DCE), is also introduced.
- Effectiveness of CLAHE in enhancing multispectral satellite imagery.
- Performance of CLAHE across different wavelengths.
- Influence of spatial and spectral resolutions on CLAHE's performance.
- Development and application of a new performance metric (DCE).
- Comparison of CLAHE with conventional global histogram equalization.
Zusammenfassung der Kapitel (Chapter Summaries)
1 Introduction: This introductory chapter lays the groundwork for the report by providing a concise overview of histogram equalization techniques. It begins by defining global histogram equalization and explaining its limitations. It then introduces adaptive histogram equalization as an improvement, highlighting its ability to handle variations in contrast across an image. The chapter culminates in a detailed explanation of Contrast Limited Adaptive Histogram Equalization (CLAHE), emphasizing its advantages in image enhancement and its suitability for the study's objectives. The introduction effectively sets the stage for the subsequent chapters by clearly defining the context and the techniques under investigation.
2 Data Acquisition: This chapter describes the data acquisition process. While specific details are omitted to avoid overly technical information, it's crucial to understand the selection and characteristics of the multispectral satellite imagery datasets used in the study. The chapter lays out the rationale for selecting specific images and sensors, highlighting the varied spectral and spatial resolutions included in the analysis. This lays the foundation for the methodological rigor and the comprehensive nature of the comparative analyses in later sections.
3 Methodology: This chapter outlines the methodological approach taken in the study. It details the quantitative parameters used to evaluate the effectiveness of CLAHE. Specifically, it describes the Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the newly formulated Degree of Contrast Enhancement (DCE). The chapter also provides a flowchart illustrating the workflow and explains the process of applying CLAHE to the different datasets, preparing the reader for the analysis presented in subsequent chapters. This detailed description ensures the reproducibility and transparency of the research.
4 Comparative Analysis: This chapter presents a detailed comparison of the results obtained by applying CLAHE to different multispectral datasets. It includes a visual analysis comparing original and enhanced images from various sensors like LISS III, AWiFS, Landsat 8 OLI, and Sentinel 2A MSI. This chapter forms a pivotal part of the research by directly demonstrating the application of CLAHE and providing visual evidence of its impact on different spectral bands. The comparison between the enhanced and original images highlights the strengths and weaknesses of the technique in different scenarios. It forms a basis for further quantitative analysis in the subsequent chapters.
5 Results and Inferences: This chapter presents the quantitative results, analyzing the MSE, PSNR, and DCE values obtained for each sensor and wavelength. It interprets these findings to assess the overall effectiveness of CLAHE and its sensitivity to variations in spatial and spectral resolutions. This chapter analyzes the data presented in the previous chapters and draws meaningful conclusions about the effectiveness of CLAHE across different datasets. Trends and patterns in the quantitative data are analyzed to support claims regarding the technique's performance across different spectral bands and sensor resolutions.
Schlüsselwörter (Keywords)
Contrast Limited Adaptive Histogram Equalization (CLAHE), multispectral satellite imagery, image enhancement, remote sensing, spectral resolution, spatial resolution, Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Degree of Contrast Enhancement (DCE), wavelength, sensor comparison.
Frequently Asked Questions: A Comprehensive Language Preview of Contrast Limited Adaptive Histogram Equalization (CLAHE) on Multispectral Satellite Imagery
What is the main focus of this report?
This report investigates the effectiveness of Contrast Limited Adaptive Histogram Equalization (CLAHE) in enhancing multispectral satellite imagery. It examines CLAHE's performance across different electromagnetic spectrum regions and analyzes how variations in spatial and spectral resolutions affect its performance. A new performance metric, Degree of Contrast Enhancement (DCE), is also introduced and utilized.
What are the key objectives of the study?
The main objectives are to determine the effectiveness of CLAHE in enhancing multispectral satellite imagery; analyze CLAHE's performance across different wavelengths; assess the influence of spatial and spectral resolutions on CLAHE's performance; develop and apply the new DCE performance metric; and compare CLAHE with traditional global histogram equalization.
What histogram equalization techniques are discussed?
The report covers global histogram equalization, adaptive histogram equalization, and primarily focuses on Contrast Limited Adaptive Histogram Equalization (CLAHE). The limitations of global histogram equalization and the advantages of CLAHE are explored in detail.
What data was used in this study?
The study utilizes multispectral satellite imagery datasets. While specific details about the datasets are omitted, the report emphasizes the selection of images and sensors with varied spectral and spatial resolutions to ensure a comprehensive analysis.
What methodologies were employed to evaluate CLAHE's performance?
The study employs quantitative parameters including Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and the novel Degree of Contrast Enhancement (DCE) to evaluate the effectiveness of CLAHE. The report details the application of these metrics and provides a flowchart illustrating the workflow.
How are the results presented and analyzed?
The results are presented through a comparative analysis, including visual comparisons of original and enhanced images from various sensors (e.g., LISS III, AWiFS, Landsat 8 OLI, and Sentinel 2A MSI). Quantitative results, focusing on MSE, PSNR, and DCE values for each sensor and wavelength, are analyzed to assess CLAHE's overall effectiveness and sensitivity to spatial and spectral resolution variations.
What are the key findings and conclusions?
The report's concluding chapter synthesizes the quantitative and visual results, drawing inferences about CLAHE's performance across different datasets. Trends and patterns in the data are analyzed to support claims about the technique's effectiveness across various spectral bands and sensor resolutions.
What are the key terms and concepts used in this report?
Key terms include Contrast Limited Adaptive Histogram Equalization (CLAHE), multispectral satellite imagery, image enhancement, remote sensing, spectral resolution, spatial resolution, Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Degree of Contrast Enhancement (DCE), wavelength, and sensor comparison.
What is the structure of the report?
The report is structured into six chapters: Introduction (covering different histogram equalization techniques); Data Acquisition; Methodology (detailing the evaluation metrics); Comparative Analysis (presenting visual and quantitative comparisons); Results and Inferences; and Conclusions.
Where can I find more detailed information on the specific datasets and technical aspects?
While this preview provides a comprehensive overview, specific technical details regarding the datasets and detailed methodological procedures might be available in the full report (if available).
- Citar trabajo
- Vidhya Ganesh Rangarajan (Autor), 2016, Effectiveness of Contrast Limited Adaptive Histogram Equalization on Multispectral Satellite Imagery, Múnich, GRIN Verlag, https://www.grin.com/document/387601
