本文選題：高分辨 + 特征提取　； 參考：《哈爾濱工業(yè)大學》2017年碩士論文

【摘要】：艦船檢測一直是國家海岸帶安全的傳統(tǒng)任務,我國具有很長的海岸線,通過遙感手段對海岸線進行監(jiān)控布防,可以大大提高近海地區(qū)的防御能力,以及海運的管理、調(diào)度能力。目前,隨著光學分辨能力的提高,遙感圖像的空間分辨能力獲得了質(zhì)的飛升�？臻g分辨率的提升不僅僅是信息量的提升,同時帶來了復雜的處理問題,由于圖像細節(jié)更加豐富,紋理更加復雜,對圖像的分析變得更加困難,通過肉眼獲取艦船目標的成本越來越高。而人工設計的特征檢測器效果也不盡人意,迫切需要一種合適艦船檢測的方法來有效利用大量的數(shù)據(jù)從而獲得更加精確的檢測效果。本文從高分辨率遙感圖像的多尺度特征出發(fā),研究了深度學習艦船目標檢測方法,包括三部分:基于定位框的層次化卷積神經(jīng)網(wǎng)絡方法,結(jié)合超像素的超像素卷積神經(jīng)網(wǎng)絡方法,以及進一步的目標分割的方法,具體內(nèi)容如下:首先,本文針對一般卷積神經(jīng)網(wǎng)絡目標檢測方法沒有有效利用各層特征以及多層卷積神經(jīng)網(wǎng)絡由于平移不變性帶來的定位精度低的缺點。研究了不同層特征對艦船檢測的影響,構(gòu)建了一個定位精度高的單層卷積神經(jīng)網(wǎng)絡,并應用遷移學習的方法構(gòu)建了一個可以精確區(qū)分背景與目標的多層卷積神經(jīng)網(wǎng)絡。在分析了兩種網(wǎng)絡的優(yōu)勢與劣勢的基礎上,利用強化學習的思想,結(jié)合單層卷積網(wǎng)絡與多層卷積網(wǎng)絡的優(yōu)點提出了一種層次化的多尺度深度學習檢測方法,獲得比標準卷積神經(jīng)網(wǎng)絡更加精確的定位精度以及檢測結(jié)果。之后,本文針對多尺度滑動窗口計算量大、難以實施的缺點,研究了結(jié)合超像素的卷積神經(jīng)網(wǎng)絡目標檢測方法,對當前深度學習目標檢測主流的對象預提取方法進行分析,研究了通過單一超像素方法獲取艦船目標的待選對象的方法,并使用卷積神經(jīng)網(wǎng)絡獲得更準確的檢測結(jié)果。該方法同時具有有監(jiān)督方法的準確性以及無監(jiān)督方法的快速性,在不損失圖像分辨率的前提下,實現(xiàn)了一種相對精度較高且快速的檢測方法。最后,研究了目標分割(像素級檢測)方法,采用逐像素取窗的方法獲得對圖像中每個像素臨近像素的準確描述,并區(qū)分該像素是否落在目標內(nèi),以達到對艦船目標進行分割的目的。并研究了使用超像素中心對區(qū)域進行表示的方法,提出了結(jié)合超像素表示方法與卷積神經(jīng)網(wǎng)絡方法對艦船目標進行快速的像素級檢測的方法,實現(xiàn)了了艦船目標快速的分割提取。
[Abstract]:Ship detection has always been a traditional task of national coastal zone security. Our country has a very long coastline, remote sensing to monitor and control the coastline, can greatly improve the defense capability of offshore areas, as well as the management of maritime transport, scheduling capacity. At present, with the improvement of optical resolution, the spatial resolution of remote sensing images has been improved. The enhancement of spatial resolution is not only the improvement of information content, but also brings about complex processing problems. Because the details of the image are more abundant and the texture is more complex, the analysis of the image becomes more difficult. It is becoming more and more expensive to obtain ship targets with the naked eye. However, the effect of the artificial feature detector is not satisfactory, it is urgent to use a suitable method of ship detection to effectively use a large number of data to obtain a more accurate detection effect. Based on the multi-scale features of high-resolution remote sensing images, this paper studies the method of deep-learning ship target detection, which consists of three parts: hierarchical convolution neural network method based on location frame. The hyperpixel convolution neural network method combined with super-pixel, as well as the method of further target segmentation, are as follows: first of all, In this paper, the general target detection method based on convolutional neural networks does not utilize the features of each layer effectively and the localization accuracy of multi-layer convolutional neural networks is low due to the translation invariance. In this paper, the influence of different layer features on ship detection is studied, a single-layer convolution neural network with high positioning accuracy is constructed, and a multi-layer convolution neural network is constructed by using migration learning method to accurately distinguish the background from the target. Based on the analysis of the advantages and disadvantages of the two networks, a hierarchical multi-scale depth learning detection method is proposed by combining the advantages of single-layer convolution network and multi-layer convolutional network by using the idea of reinforcement learning. Compared with the standard convolution neural network, the location accuracy and detection results are obtained. Then, aiming at the shortcomings of multi-scale sliding window, which is difficult to implement, this paper studies the convolution neural network target detection method combined with super-pixel, and analyzes the main method of object pre-extraction in depth learning target detection. In this paper, a single super-pixel method is studied to obtain the target to be selected from a ship target, and a more accurate detection result is obtained by using convolution neural network. This method has the accuracy of the supervised method and the rapidity of the unsupervised method. Without losing the resolution of the image, a high accuracy and fast detection method is realized. Finally, the method of target segmentation (pixel level detection) is studied. The method of pixel by pixel window is used to obtain the accurate description of each pixel adjacent to the pixel in the image, and to distinguish whether the pixel falls in the target or not. In order to achieve the purpose of ship target segmentation. The method of using the super-pixel center to represent the region is studied, and the method of fast pixel level detection of ship target is proposed by combining the super-pixel representation method and convolution neural network method. The fast segmentation and extraction of ship target is realized.
【學位授予單位】：哈爾濱工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751;TP18

【參考文獻】

中國期刊全文數(shù)據(jù)庫 前4條

1 歐陽穎卉;林，

本文編號：1785264