發(fā)布時(shí)間：2019-07-09 07:49

【摘要】：高光譜遙感技術(shù)是20世紀(jì)末發(fā)展起來的，是融合電磁學(xué)、光學(xué)、信號(hào)處理等多學(xué)科交叉領(lǐng)域的新興學(xué)科。與傳統(tǒng)遙感技術(shù)相比，高光譜遙感技術(shù)在獲取地面信息的同時(shí)，還可獲取豐富的地物光譜信息，使其在農(nóng)業(yè)、林業(yè)、地質(zhì)、環(huán)境、軍事等不同領(lǐng)域得到了廣泛的應(yīng)用。隨著空間分辨力和譜間分辨力的不斷提高，高光譜遙感圖像的數(shù)據(jù)量呈指數(shù)量級(jí)增長，給傳輸和存儲(chǔ)帶來了巨大的壓力。因此研究高光譜圖像壓縮算法對(duì)高光譜遙感技術(shù)的發(fā)展有著至關(guān)重要的意義。 本文針對(duì)高光譜遙感圖像在實(shí)際應(yīng)用中面臨的數(shù)據(jù)量龐大，信息獲取和數(shù)據(jù)傳輸之間的矛盾日益加劇等一系列問題，對(duì)基于冗余字典的高光譜遙感圖像的稀疏表示和壓縮算法進(jìn)行了深入研究。主要研究內(nèi)容如下： (1)實(shí)現(xiàn)了基于冗余字典的高光譜遙感圖像的稀疏表示。該方法能夠以較少的數(shù)據(jù)量更好地描述高光譜圖像中的特征信息，是一種有效的高光譜圖像表示方法。 (2)研究了一種基于稀疏表示的高光譜遙感圖像的壓縮方法。該方法在對(duì)高光譜遙感圖像進(jìn)行稀疏表示的情況下，，采用比特平面編碼對(duì)稀疏表示系數(shù)進(jìn)行壓縮，獲得了較高的壓縮比。 (3)完成了對(duì)高光譜遙感圖像的重建，并獲得了良好的重建效果。 本文進(jìn)行了大量仿真實(shí)驗(yàn)，實(shí)驗(yàn)結(jié)果表明本文算法能夠取得良好的壓縮效果和良好的重構(gòu)效果，在不同的高光譜圖像庫中具有較好的通用性。
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[Abstract]:Hyperspectral remote sensing technology was developed at the end of the 20th century. It is a new subject which integrates electromagnetics, optics, signal processing and other interdisciplinary fields. Compared with traditional remote sensing technology, hyperspectral remote sensing technology can not only obtain ground information, but also obtain rich spectral information of ground objects, which has been widely used in agriculture, forestry, geology, environment, military and other fields. With the continuous improvement of spatial resolution and inter-spectral resolution, the amount of data of hyperspectral remote sensing images increases in the order of magnitude, which brings great pressure to transmission and storage. Therefore, it is of great significance to study hyperspectral image compression algorithm for the development of hyperspectral remote sensing technology. In order to solve a series of problems, such as huge amount of data and increasing contradiction between information acquisition and data transmission, the sparse representation and compression algorithm of hyperspectral remote sensing image based on redundant dictionary is deeply studied in this paper. The main research contents are as follows: (1) the sparse representation of hyperspectral remote sensing images based on redundant dictionaries is realized. This method can better describe the feature information in hyperspectral images with less data, and it is an effective hyperspectral image representation method. (2) A compression method of hyperspectral remote sensing image based on sparse representation is studied. In this method, the sparse representation coefficient is compressed by bit plane coding under the condition of sparse representation of hyperspectral remote sensing images, and a high compression ratio is obtained. (3) the reconstruction of hyperspectral remote sensing image is completed, and good reconstruction effect is obtained. In this paper, a large number of simulation experiments are carried out, and the experimental results show that the algorithm can achieve good compression effect and good reconstruction effect, and has good generality in different hyperspectral image libraries.
【學(xué)位授予單位】：河北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

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本文編號(hào)：2511991