【摘要】：艦船目標(biāo)檢測(cè)技術(shù)是遙感衛(wèi)星圖像處理與分析領(lǐng)域非常重要的課題,尤其對(duì)于高分辨率的光學(xué)遙感圖像,其海量數(shù)據(jù)雖然提供了更加豐富的細(xì)節(jié)信息,但又嚴(yán)重制約了艦船目標(biāo)的檢測(cè)效率。因此,如何快速準(zhǔn)確地獲取艦船目標(biāo)的位置信息已成為一個(gè)熱點(diǎn)話題。針對(duì)光學(xué)遙感圖像艦船目標(biāo)的檢測(cè)問(wèn)題,本文重點(diǎn)研究了艦船目標(biāo)候選區(qū)域提取和艦船目標(biāo)的鑒別技術(shù),使艦船目標(biāo)檢測(cè)的精度和效率得到了提高。本文主要工作包括以下幾個(gè)方面:1、基于光學(xué)遙感圖像的預(yù)處理,該環(huán)節(jié)主要包括圖像的濾波、光照均衡化和去云霧干擾等步驟,其中重點(diǎn)研究了圖像的光照均衡化處理和去云霧干擾等算法。該步驟旨在減弱噪聲、光照不均勻和云霧等不利因素的干擾,更加突出目標(biāo)信息。2、研究了艦船目標(biāo)候選區(qū)域的提取方法,并提出了一種改進(jìn)的PQFT艦船目標(biāo)候選區(qū)域快速提取法。本文引入了PQFT方法,并在原有的PQFT算法中加入了小波變換,從不同尺度分析圖像的顯著特征,從中選擇顯著度最好的圖像。實(shí)驗(yàn)表明,改進(jìn)的PQFT顯著性方法顯著度要好于原尺度的效果,運(yùn)算時(shí)間也得到了相應(yīng)減少。3、為了判斷所提取候選區(qū)域的目標(biāo)是否為艦船目標(biāo),本文分別提取了艦船目標(biāo)的形狀特征、灰度特征、紋理特征和梯度方向直方圖特征。并提出了一種改進(jìn)的LBP特征提取算法,其抗干擾能力變強(qiáng)、計(jì)算復(fù)雜度降低,增加了LBP特征提取的可控性。通過(guò)融合艦船目標(biāo)的多特征信息,能夠更加準(zhǔn)確地判別艦船目標(biāo)和非艦船目標(biāo)。4、將改進(jìn)的極限學(xué)習(xí)機(jī)(Extreme Learning Machine,ELM)算法用于艦船目標(biāo)的分類識(shí)別。ELM是一種神經(jīng)網(wǎng)絡(luò)算法,其特點(diǎn)為:網(wǎng)絡(luò)是單隱藏層、隱藏層結(jié)點(diǎn)數(shù)人為設(shè)置、輸入權(quán)值和隱藏層偏置隨機(jī)產(chǎn)生,因此該方法具有計(jì)算時(shí)間短、泛化能力強(qiáng)和不易陷入局部最優(yōu)等特點(diǎn)。而傳統(tǒng)ELM算法的激活函數(shù),如Sigmoid函數(shù)、sin函數(shù)和tanh函數(shù)等存在過(guò)飽和的缺點(diǎn),本文提出了一種非線性修正的ELM算法,最后將本文改進(jìn)的ELM算法用于艦船目標(biāo)分類識(shí)別中�；谝陨涎芯�,運(yùn)用MATLAB平臺(tái)進(jìn)行仿真驗(yàn)證,實(shí)驗(yàn)結(jié)果證明了以上改進(jìn)算法的有效性,能夠提高檢測(cè)精度和檢測(cè)效率。
[Abstract]:Ship target detection technology is a very important subject in the field of remote sensing satellite image processing and analysis, especially for high-resolution optical remote sensing image, its massive data provide more detailed information. However, the efficiency of ship target detection is seriously restricted. Therefore, how to obtain the position information of ship target quickly and accurately has become a hot topic. Aiming at the problem of ship target detection in optical remote sensing image, this paper focuses on the extraction of ship target candidate region and the identification technology of ship target, which improves the accuracy and efficiency of ship target detection. The main work of this paper includes the following aspects: 1. Based on the preprocessing of optical remote sensing image, this link mainly includes image filtering, illumination equalization and cloud and fog removal, etc. The algorithms of image illumination equalization and cloud-free interference are studied. The purpose of this step is to attenuate the interference of unfavorable factors, such as noise, uneven illumination, cloud and fog, and to highlight the target information. 2. An improved PQFT ship target candidate region extraction method is proposed. In this paper, the PQFT method is introduced, and wavelet transform is added to the original PQFT algorithm to analyze the salient features of the image from different scales and select the image with the best saliency. The experimental results show that the improved PQFT saliency method is more significant than the original scale, and the computational time is reduced accordingly. In order to judge whether the target of the candidate region extracted is a ship target, In this paper, the shape feature, grayscale feature, texture feature and gradient direction histogram feature of ship target are extracted. An improved LBP feature extraction algorithm is proposed, which has the advantages of strong anti-interference ability and low computational complexity, and increases the controllability of LBP feature extraction. By integrating the multi-feature information of ship target, we can distinguish ship target from non-ship target more accurately. 4. The improved extreme learning machine (Extreme Learning Machine, will be improved. ELM is a neural network algorithm, which is characterized by: the network is a single hidden layer, the number of hidden layer nodes are set, the input weight and the hidden layer bias are generated randomly. Therefore, this method has the advantages of short calculation time, strong generalization ability and difficulty to fall into local optimum. However, the activation function of traditional ELM algorithm, such as Sigmoid function, sin function and tanh function, has the disadvantage of supersaturation. In this paper, a nonlinear modified ELM algorithm is proposed, and the improved ELM algorithm is applied to the classification and recognition of ship targets. Based on the above research, the MATLAB platform is used for simulation verification. The experimental results show that the improved algorithm is effective and can improve the detection accuracy and efficiency.
【學(xué)位授予單位】：東華大學(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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