【摘要】：云量不僅是影響地氣系統(tǒng)輻射收支平衡的重要參數(shù),同時也是研究大氣環(huán)流及氣候變化的重要指標。云量計算又與云檢測息息相關,衛(wèi)星云圖分類方法的檢測精度直接影響著云量計算的準確率。在衛(wèi)星云圖云檢測處理的實際應用中,擴大訓練集是提升分類精度途經(jīng)之一。然而,大量的已標記數(shù)據(jù)集需要耗費大量的人力和物力成本。在遙感領域,現(xiàn)代高分辨率傳感器技術的飛速發(fā)展,使得收集未標記數(shù)據(jù)變得更加容易和經(jīng)濟。因此,通過少量已標記訓練樣本和大量未標記樣本提高算法的檢測性能就顯得很有意義。本文基于機器學習理論,將主動學習與極限學習機相結合,充分挖掘衛(wèi)星云圖分類中大量樣本的有用信息,用以少量已標記樣本,快速提高分類器的性能,提高檢測的精度,減少人工標記成本。論文完成的主要工作如下:(1)研究極限學習機的樣本不確定性評估策略,用于主動在線極限學習機,并通過與極限學習機、主動支持向量機以及主動極限學習機在4種不同公共數(shù)據(jù)下的性能表現(xiàn),證明了所提出的主動在線極限學習機的有效性。(2)運用主動在線極限學習機進行云檢測,對原始衛(wèi)星云圖進行樣本提取、預處理后,已極限學習機作為基本分類器,采用非確定性抽樣提取信息豐富的樣本,進行主動在線學習,實現(xiàn)薄云、厚云、晴空以及薄云和厚云交界的檢測。在不降低分類器性能的前提下,減少樣本人工標注成本,縮減分類器訓練時間。通過與閾值法、主動支持向量機、ELM實驗比較,驗證本文提出的方法在處理衛(wèi)星云圖數(shù)據(jù)時的有效性。(3)將檢測后的衛(wèi)星云圖,利用“空間相關法”在云檢測的基礎上進行云量計算,并與4種不同的算法進行對比實驗,最后通過與專家標定的標準數(shù)據(jù)庫進行對比分析,改進并完善衛(wèi)星云圖云量計算模型。
[Abstract]:Cloud cover is not only an important parameter to influence the balance of radiation budget in terrestrial atmosphere system, but also an important index to study atmospheric circulation and climate change. Cloud calculation is closely related to cloud detection. The accuracy of satellite cloud image classification directly affects the accuracy of cloud calculation. In the practical application of cloud detection and processing of satellite cloud image, expanding the training set is one of the ways to improve the classification accuracy. However, a large number of marked data sets require a lot of human and material costs. In the field of remote sensing, the rapid development of modern high-resolution sensor technology makes it easier and more economical to collect unlabeled data. Therefore, it is significant to improve the detection performance of the algorithm through a small number of labeled training samples and a large number of unlabeled samples. Based on the theory of machine learning, this paper combines active learning with extreme learning machine to fully mine the useful information of a large number of samples in satellite cloud image classification, so as to quickly improve the performance of classifier and improve the accuracy of detection by using a small number of labeled samples. Reduce the cost of manual marking. The main work of this paper is as follows: (1) the sample uncertainty evaluation strategy of LLM is studied, which is used for active online LLM, and through LLM, The performance of active support vector machine and active extreme learning machine under four kinds of common data proves the effectiveness of the proposed active online extreme learning machine. (2) Cloud detection is carried out by using active online extreme learning machine. After preprocessing, the extreme learning machine is used as the basic classifier, the samples with abundant information are extracted by non-deterministic sampling, and active online learning is carried out to realize the thin cloud and thick cloud. Clear skies and the detection of the boundary between thin clouds and thick clouds. Without reducing the performance of classifier, the cost of manual labeling of samples is reduced, and the training time of classifier is reduced. Comparing with the threshold method and the ELM experiment of active support vector machine, the validity of the proposed method in the processing of satellite cloud image data is verified. (3) the detected satellite cloud image will be obtained. The spatial correlation method is used to calculate cloud amount on the basis of cloud detection, and compared with four different algorithms. Finally, it is compared and analyzed with the standard database calibrated by experts. The cloud volume calculation model of satellite cloud image is improved and improved.
【學位授予單位】：南京信息工程大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：P412.27;TP18

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