本文選題：目標(biāo)識(shí)別 + 特征提取��； 參考：《北方工業(yè)大學(xué)》2014年碩士論文

【摘要】：隨著遙感技術(shù)的迅速發(fā)展,如何從遙感圖像中有效提取目標(biāo)特征,并對(duì)其目標(biāo)特征進(jìn)行挖掘成為遙感圖像處理的一個(gè)重要課題。由于道路的重要性,道路目標(biāo)的特征提取和特征挖掘在社會(huì)發(fā)展的各個(gè)方面具有重大的意義。本文主要的研究工作如下： 圖像在獲取或傳輸過程中不可避免地存在一定程度的噪聲干擾,噪聲惡化了圖像質(zhì)量,給圖像分割、分析判斷等工作帶來了困難。為此,提出了一種基于希爾伯特黃變換的圖像去噪方法,采用二維經(jīng)驗(yàn)?zāi)Ｊ椒纸鈱?duì)遙感圖像進(jìn)行多尺度分解,分解出各個(gè)本征模式函數(shù),對(duì)本征模式函數(shù)進(jìn)行二維希爾伯特變換,保留并加強(qiáng)圖像細(xì)節(jié)信息、削弱噪聲,最終實(shí)現(xiàn)圖像的去噪,并利用客觀評(píng)價(jià)指標(biāo)評(píng)價(jià)去噪效果。 在圖像特征提取階段,提取了圖像的顏色、紋理和形狀特征。在邊緣特征提取時(shí),本文提出了用Canny算子與小波變換相結(jié)合的邊緣提取算法。該方法結(jié)合了Canny算子提取邊緣的優(yōu)點(diǎn)和小波變換在檢測(cè)圖像突變點(diǎn)中的優(yōu)點(diǎn),避免了Canny算子提取邊緣道路邊緣不連續(xù)現(xiàn)象和小波變換提取邊緣時(shí)出現(xiàn)的虛假的邊緣信息,有效地對(duì)圖像進(jìn)行邊緣檢測(cè)。 利用BP神經(jīng)網(wǎng)絡(luò)對(duì)圖像的特征進(jìn)行訓(xùn)練,由于BP神經(jīng)網(wǎng)絡(luò)有其自己的缺陷,當(dāng)給一個(gè)訓(xùn)練好的BP神經(jīng)網(wǎng)絡(luò)提供新的學(xué)習(xí)記憶模式時(shí),將使已有的連接權(quán)值被打亂,導(dǎo)致已記憶的學(xué)習(xí)模式的信息消失,使得訓(xùn)練效果一般。在對(duì)BP神經(jīng)網(wǎng)絡(luò)優(yōu)化時(shí),主要的方法就是添加動(dòng)量因子,或者改變其激勵(lì)函數(shù),在本文中,利用BP神經(jīng)網(wǎng)絡(luò)與模擬退火法相結(jié)合,提高了神經(jīng)網(wǎng)絡(luò)學(xué)習(xí)效果,縮短了陷入局部極小值的時(shí)間。
[Abstract]:With the rapid development of remote sensing technology, how to extract target features effectively from remote sensing images and mine them has become an important task in remote sensing image processing. Because of the importance of road, feature extraction and feature mining of road goals are of great significance in all aspects of social development. The main research work of this paper is as follows: there is inevitably a certain degree of noise interference in the process of image acquisition or transmission, which results in the deterioration of image quality and brings difficulties to image segmentation, analysis and judgment. In this paper, a new image denoising method based on Hilbert-Huang transform is proposed, in which two dimensional empirical mode decomposition is used to decompose the remote sensing image at multiple scales, and the intrinsic mode functions are decomposed. The intrinsic mode function is transformed into two dimensional Hilbert transform, which preserves and strengthens the detail information of the image, weakens the noise, finally realizes the denoising of the image, and evaluates the denoising effect by using the objective evaluation index. The color, texture and shape features of the image are extracted. In this paper, an edge extraction algorithm based on Canny operator and wavelet transform is proposed. This method combines the advantages of Canny operator in edge detection and wavelet transform in detecting image mutation points, and avoids the discontinuity of edge road edge by Canny operator and false edge information when wavelet transform is used to extract edge. The BP neural network is used to train the image features. Because BP neural network has its own defects, when a trained BP neural network is provided with a new learning and memory mode, It will cause the existing connection weights to be disturbed, resulting in the loss of the information of the learning mode, which makes the training effect less effective. In the optimization of BP neural network, the main method is to add momentum factor or change its excitation function. In this paper, BP neural network is combined with simulated annealing method to improve the learning effect of neural network. Shortens the time to get into a local minimum.
【學(xué)位授予單位】：北方工業(yè)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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