A Color Consistency Processing Method for HY-1C Images of Antarctica
"> Figure 1
<p>Example of an original HY-1C image of Antarctica. There is uneven lighting in the image.</p> "> Figure 2
<p>Example mosaic result for a group of HY-1C images of Antarctica.</p> "> Figure 3
<p>The statistics of two adjacent images randomly selected from the dataset. (<b>a</b>) Mosaicked image. (<b>b</b>) The upper image. (<b>c</b>) The lower image. (<b>d</b>) Histogram of the three color channels of the upper image. (<b>e</b>) Histogram of the three color channels of the lower image. (<b>f</b>) The mean and standard deviation values.</p> "> Figure 4
<p>The processing of HY-1C images of Antarctica in the color consistency framework. CZI, Coastal Zone Imager.</p> "> Figure 5
<p>The process of single-view color consistency processing. (<b>a</b>) The process flow. (<b>b</b>) The data flow according to the process flow.</p> "> Figure 6
<p>A comparison between the proposed method and the standard Gaussian filtering method. (<b>a</b>) Original image. (<b>b</b>) The feature map extracted by the standard Gaussian filter. (<b>c</b>) Single-view color consistency processing results using the standard Gaussian filter. (<b>d</b>) The auxiliary mask of the sample image. (<b>e</b>) The feature map extracted by the proposed filter. (<b>f</b>) Single-view color consistency processing results using the proposed filter.</p> "> Figure 7
<p>Example of a “no light” image.</p> "> Figure 8
<p>The flowchart of multi-view color consistency processing.</p> "> Figure 9
<p>Comparisons between the original images and the result of the single-view color consistency method.</p> "> Figure 9 Cont.
<p>Comparisons between the original images and the result of the single-view color consistency method.</p> "> Figure 10
<p>The sub-region locations and ranges. The three images shown are named F1, F2, and F3. The rectangles are the sub-regions of each image, labeled A, B, C, and D, from left to right.</p> "> Figure 11
<p>Statistics of the original images and the results after processing. The mean, standard deviation, and average gradient of F1, F2, and F3 are shown separately.</p> "> Figure 11 Cont.
<p>Statistics of the original images and the results after processing. The mean, standard deviation, and average gradient of F1, F2, and F3 are shown separately.</p> "> Figure 12
<p>Comparison of the mosaic results for the group images processed by the Wallis method [<a href="#B12-remotesensing-12-01143" class="html-bibr">12</a>] and the proposed approach. (<b>a</b>) Mosaic results for the original images. (<b>b</b>) Mosaic results for the images processed by the Wallis method. (<b>c</b>) Mosaic results for the images processed by the proposed method.</p> "> Figure 13
<p>The details of the mosaic boundaries with (<b>a</b>) the original images, (<b>b</b>) the Wallis method, and (<b>c</b>) the proposed method.</p> "> Figure 14
<p>Statistical results for the color consistency processing between images. (<b>a</b>) The standard deviation values of the mosaic results of the original images, the Wallis method results, and the results of the proposed method. (<b>b</b>) The standard deviation values of the mosaic results of the details of the original images and the details of the results of the proposed method. (<b>c</b>) The mean values and the difference values of the upper and lower part of the details of the original images and the details of the results of the proposed method. (<b>d</b>) The histogram similarity values between the upper part and the lower part of the mosaic result of the details of the original images and the mosaic result of the details of the proposed method.</p> ">
Abstract
:1. Introduction
2. Data Preprocessing and Analysis
2.1. Study Dataset
2.2. Data Analysis
2.2.1. Uneven Lighting
2.2.2. Inconsistent Colors
3. Consistency Processing Method
3.1. Color Consistency Framework
3.1.1. Single-view Consistency Process
- Feature map extraction
- Uneven light removal
- Calculation of Artificial Lighting Information
- Information overlay
3.1.2. Multi-View Consistency Process
- to color space
- Feature calculation
- Feature normalization
- Results visualization
4. Results and Discussion
4.1. Single-View Consistency Evaluation
4.1.1. Subjective Evaluation
4.1.2. Quantitative Evaluation
4.2. Multi-View Color Consistency Evaluation
4.2.1. Subjective Evaluation
4.2.2. Quantitative Evaluation
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
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Li, Z.; Zhu, H.; Zhou, C.; Cao, L.; Zhong, Y.; Zeng, T.; Liu, J. A Color Consistency Processing Method for HY-1C Images of Antarctica. Remote Sens. 2020, 12, 1143. https://doi.org/10.3390/rs12071143
Li Z, Zhu H, Zhou C, Cao L, Zhong Y, Zeng T, Liu J. A Color Consistency Processing Method for HY-1C Images of Antarctica. Remote Sensing. 2020; 12(7):1143. https://doi.org/10.3390/rs12071143
Chicago/Turabian StyleLi, Zhijiang, Haonan Zhu, Chunxia Zhou, Liqin Cao, Yanfei Zhong, Tao Zeng, and Jianqiang Liu. 2020. "A Color Consistency Processing Method for HY-1C Images of Antarctica" Remote Sensing 12, no. 7: 1143. https://doi.org/10.3390/rs12071143