本文選題：遙感影像 + 多分類　； 參考：《浙江大學(xué)》2017年碩士論文

【摘要】：近年來隨著各種新型的傳感器不斷涌現(xiàn),遙感技術(shù)的提升,我國(guó)的高分辨率遙感衛(wèi)星技術(shù)取得了飛速地發(fā)展,高分辨率遙感影像的數(shù)據(jù)級(jí)別趨于海量化發(fā)展,數(shù)據(jù)類型也越來越多樣化。海量的遙感影像數(shù)據(jù)帶來更多信息的同時(shí)也給快速處理帶來了很大的挑戰(zhàn)。由于衛(wèi)星周期性旋轉(zhuǎn)的特點(diǎn),同一個(gè)地區(qū)在不同時(shí)間將會(huì)被衛(wèi)星拍攝到很多次。通過檢測(cè)同一地區(qū)不同時(shí)間影像發(fā)生的變化,有利于發(fā)現(xiàn)該地區(qū)地面覆蓋變化情況。變化檢測(cè)算法根據(jù)影像分析的層次不同可以分為像素級(jí)、特征級(jí)和目標(biāo)級(jí)這三類,根據(jù)數(shù)據(jù)分析的機(jī)理,變化檢測(cè)算法可以分為有監(jiān)督和無監(jiān)督兩類。傳統(tǒng)的變化檢測(cè)都是基于單機(jī)來處理,面對(duì)遙感影像數(shù)據(jù)量較小的場(chǎng)景,處理比較方便,但是面對(duì)高分辨率遙感影像,處理速度較慢甚至不能處理。本文主要針對(duì)高分辨率遙感影像多時(shí)相變化檢測(cè)問題,利甩Spark這一基于內(nèi)存計(jì)算的計(jì)算引擎來處理,加快檢測(cè)速度。本文采用有監(jiān)督的像素級(jí)變化檢測(cè)方法。面對(duì)高分辨率遙感影像數(shù)據(jù),首先利用降維算法,找出主成分,然后利用手動(dòng)標(biāo)記的方法,找出各地區(qū)的樣本點(diǎn),根據(jù)像素點(diǎn)信息及空間信息訓(xùn)練出多分類模型,根據(jù)模型將原始遙感影像劃分成不同區(qū)域,然后對(duì)比不同區(qū)域的變化得出變化分析報(bào)表。
[Abstract]:In recent years, with the continuous emergence of various new sensors and the improvement of remote sensing technology, the high-resolution remote sensing satellite technology in China has made rapid development, and the data level of high-resolution remote sensing image tends to the development of sea quantification. Data types are also becoming more and more diverse. The massive remote sensing image data bring more information, but also bring a great challenge to the fast processing. Because of the periodic rotation of the satellite, the same area will be photographed many times at different times. By detecting the changes of different time images in the same area, it is helpful to find out the change of ground cover in the same area. According to the different levels of image analysis, change detection algorithms can be divided into pixel level, feature level and target level. According to the mechanism of data analysis, change detection algorithms can be divided into two categories: supervised and unsupervised. The traditional change detection is based on a single machine. It is more convenient to deal with the scene where the data of remote sensing image is small, but in the face of high resolution remote sensing image, the processing speed is slow or even can not be processed. Aiming at the problem of multi-temporal change detection of high-resolution remote sensing images, this paper is aimed at removing Spark, which is a computing engine based on memory computing, to speed up the detection. A supervised pixel level change detection method is used in this paper. In the face of high resolution remote sensing image data, we first use dimension reduction algorithm to find out the principal components, then use manual marking method to find out the sample points of each region, and then train the multi-classification model according to pixel information and spatial information. According to the model, the original remote sensing image is divided into different regions, and then the change analysis report is obtained by comparing the changes of different regions.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751

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