摘要
本文在研究MODIS传感器特点的基础上,对MODIS图像预处理过程中常见的问题：条带噪声,图像弯弓效应,云干扰等问题进行了深入研究,探讨了问题存在的原因,在总结现有研究的基础上提出了相应的解决方法。
主要研究内容与成果如下：1.针对图像中的条带噪声,提出了一种基于分块最佳线性预测器的条带噪声去除方法,该方法优于目前常用的去条带方法,不仅能有效地去除条带噪声,而且保存了图像原有的大部分信息。2.针对图像弯弓效应(Bowtie效应),本文提出了一种快速的基于星历表数据去除Bowtie效应的高效算法,该方法在消除图像Bowtie效应的同时完成了对重叠的经纬度数据的纠正。3.针对薄云对图像的影响,本文提出了一种对MODIS遥感图像薄云检测和消除的有效方法,该方法在检测薄云位置的基础上,仅对有云的区域进行薄云去除,而不改变无云区域的图像。4.为了消除厚云对遥感图像的影响,本文提出了一种自动、有效的厚云去除方法。该方法能够有效的去除厚云对光谱图像的影响,较好地恢复厚云遮挡的地物的光谱信息。
MODIS (moderate resolution imaging spectroradiometer) has high time resolution, which can support the day visible and day/night infrared spectrum of the Earth's surface every one to two days. MODIS data is very suitable for large-scale Eearth's surface observation and available free of charge. As a result, it has been one of the main detectors of global change research in Earth Observing System (EOS) series satellites.
MODIS data supply much useful information, such as land surface, ocean, phytoplankton biogeochemistry, atmospheric water vapor, atmospheric temperature, cloud properties, cloud top temperature, cloud altitude, ozone, which can support important foundations for monitoring and researching of global change. MODIS LIB data is mainly researched in this paper. Although the processing of geolocation and radiometric calibration has been done, there are still some problems in the data which have influences on data application. The main problems affecting the application of MODIS LIB data are researched in this paper, which are described as follows:
(1) Stripe Noise Removal Method in MODIS Imagery Based on Block Optimal Linear Predictor
Stripe noise is one of the most common noises in MODIS data, which is caused by slight errors in the internal calibration system, variation in the response of the detectors and random noise. In MODIS imagery, stripe noise seriously affects image interpretation and information application. In order to effectively use MODIS data, some useful methods should be adopted to remove stripe noise. The stripe noise removal method based on block optimal linear predictor is developed to reduce strip noise effects from MODIS imagery. Through analyzing the correlation between detector images, the original image is divided to several detector images which include stripe noise images and no-stripe noise images. By using the block optimal linear predictor, the new detector images can be obtained with no-stripe noise images, thus the stripe noise can be removed from the original image. Experimental results demonstrate the proposed method can achieve better results than present methods, which effectively remove stripe noise and preserve most information of original image. The method is proved to be perfect for stripe noise removal in MODIS imagery.
(2) A Fast method of eliminating the bowtie effect in MODIS L1B Data
After the process of reflection rectification, there still exists a special 'bowtie effect' phenomenons in MODIS LIB data, which mainly represents that part of data overlapped in the neighboring scan bands. Moreover, the nearer to image edge, the more overlapping data exist. When the scan areas are in line shape, the bowtie effect phenomenon becomes more obvious. For this reason, part of the edge data has poor quality and cannot be used for data post-processing. The Bowtie effect belongs to a kind of geometry distortions of the EOS/MODIS LIB data. Till now, several methods have been proposed to eliminate the Bowtie effect all over the world, but they still exist many limitations in computation efficiency. After comparison and evaluating these methods that can resolve bowtie effect, at the same time, analyzing bowtie effect emergent regulation, with utilizing the ephemeris data, a fast method of eliminating the bowtie effect is proposed in the paper. In this method, the rough positions of overlapping data are first detected. Because of the influence caused by the instrument characters and the earth's curvature, the positions of overlapping data need to be rectified to obtain more precise results. The optimal rectification method used in this article is selected by comparing three methods. By using the optimal method, the rectified MODIS data can be obtained. The experiments demonstrate that the processing time of the proposed method is only about half of that processed by ENVI software for MODIS data with 1km spatial resolution, thus the proposed method is proved faster and more effective.
(3) An effective method for the detection and removal of thin Clouds from MODIS imagery
With the resolution improvement of remote sensing imagery, sensors often receive large amounts of data. Due to the impact of climate, most images have thin clouds or other atmospheric interferers which can severely affect image exploitation. Because of cloud interference, very important information covered by cloud cannot be recovered. Therefore, there is a need to use proper thin cloud removal methods to resolve this problem. In this paper, an effective method is proposed to detect and remove thin clouds from MODIS images. The proposed method involves two processing steps:thin cloud detection and thin cloud removal. As for thin cloud detection, through analyzing the cloud spectral characters in MODIS thirty-six bands, we can draw the conclusion that the spectral reflections of ground and cloud are different in various MODIS band. Hence, the cloud and ground area can be separately identified based on MODIS multi-spectral analysis. Then, a region labeling algorithm is used to separate thin clouds from many candidate objects. After cloud detection processing, thin cloud removal method is used to process each cloud region. In large images, the cloud regions usually occupy small parts, so in order to improve processing speed, the cloud removal processing focuses on the regions given by detected clouds, so it does not need to process the whole image. Compared with traditional methods, the proposed method can realize thin cloud detection and removal effectively. Additionally, the cloud removal processing mainly aims at labeling the cloud region rather than the whole image, so it can improve the processing effectiveness. Experiment results demonstrate that the proposed method can effectively remove thin cloud from MODIS imagery.
(4) The research on automatic thick cloud removal for MODIS remote sensing imagery
Cloud is one of the most common atmospheric interferers in MODIS remote sensing imagery. The existence of clouds not only brings some difficulties for image processing, but also cause image recognition and classification problems. How to effectively reduce or remove the cloud interference is an important procedure in the fields of remote sensing imagery processing and atmosphere correction. In this paper, an automatic method is proposed to realize thick cloud removal for MODIS remote sensing imagery. The proposed method can make full use of MODIS advantages of high temporal resolution and spatial resolution. The overlapping region can be detected by utilizing geographical information of the thick cloud data and no-cloud data, then SIFT detection and feature point matching are applied in the overlapping region, furthermore, the exact matching point pairs can be extracted with proper strategy. Based on these exact matching point pairs and the quadratic polynomial model, the rectified image can be obtained. Meanwhile, thick cloud regions are detected by the algorithm of multi-spectral image analysis, and then the images of thick cloud regions are replaced with the corresponding regions of the rectified image. Finally, radiance differences are eliminated for image visual effect. Experiment results demonstrate that the proposed method can effectively remove thick cloud from MODIS image, which can satisfy the demand of post-processing for remote sensing imagery.
引文
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