基于卷积神经网络模型的遥感图像分类

研究了遥感图像的分类,针对遥感图像的支持向量机(SVM)等浅层结构分类模型特征提取困难、分类精度不理想等问题,设计了一种卷积神经网络(CNN)模型,该模型包含输入层、卷积层、全连接层以及输出层,采用Soft Max分类器进行分类。选取2010年6月6日Landsat TM5富锦市遥感图像为数据源进行了分类实验,实验表明该模型采用多层卷积池化层能够有效地提取非线性、不变的地物特征,有利于图像分类和目标检测。针对所选取的影像,该模型分类精度达到94.57%,比支持向量机分类精度提高了5%,在遥感图像分类中具有更大的优势。     

遥感图像中飞机的改进YOLOv3实时检测算法

针对遥感图像中的飞机目标, 本文提出一种遥感图像飞机的改进YOLOv3实时检测算法。首先, 针对单一的遥感图像飞机目标, 提出一种有49个卷积层的卷积神经网络。其次, 在提出的卷积神经网络上应用密集相连模块进行改进, 并提出使用最大池化加强密集连接模块间的特征传递。最后, 针对遥感图像中飞机多为小目标的现实, 提出将YOLOv3的3个尺度检测增加至4个并以密集相连融合不同尺度模块特征层的信息。在本文设计的遥感飞机测试集上进行训练和测试, 实验表明, 该算法的检测精度达到96.26%、召回率达到93.81%。     

基于卷积神经网络的印刷品颜色缺陷检测

目的 提取样本图像颜色直方图特征对卷积神经网络进行训练,达到快速、高准确率检测图像颜色缺陷的目的.方法 将标准图像从RGB颜色空间转换至HSV颜色空间,通过改变图像H,S,V三分量值获取训练样本和测试样本;在HSV颜色空间中非均匀量化图像的颜色直方图,得到所有训练样本和测试样本的颜色直方图特征;利用样本图像颜色直方图特征训练卷积神经网络,然后对测试样本进行检测,研究检测的速度、准确率,并将该检测方法与逐像素、超像素、BP神经网络和支持向量机方法进行对比.结果 对于图片尺寸为512×512的彩色图像,卷积神经网络检测单幅图片的平均检测时间约为57.66 ms,训练样本图像为50000张时,卷积神经网络方法对10000张测试样本进行检测的准确率为99.77％.结论 卷积神经网络方法在保证高准确率的前提下大幅提高检测精度,对于印刷品色差缺陷在线检测具有良好的应用价值.     

基于CT图像卷积神经网络处理的新冠肺炎检测

为增强对新冠肺炎与普通肺炎的区分能力,协助医护人员对肺炎患者进行胸部CT检测,在人工智能图像分析的基础上提出了一种基于CT图像卷积神经网络处理新冠肺炎的检测方法.首先,搭建一个卷积神经网络模型,通过评估模型深度对检测结果的影响,以选择最佳的网络结构;其次,提出了一种禁忌遗传算法,用以获取网络模型中最优的超参数组合,增强...     

基于卷积神经网络的绘画图像分类研究

绘画作品的数字化对有效使用绘画资源具有重要意义,传统图像分类方法并未考虑绘画作品主观特性,且大部分特征需要人工提取,存在细节特征丢失等问题.在此提出基于卷积神经网络的绘画图像分类方法,分析了卷积核大小、卷积神经网络结构宽度、训练样本数量对分类结果的影响,以优化网络结构和参数.实验结果表明,该方法对绘画图像分类的有效性,在不同绘画图像数据集的分类实验上也得到了较好的分类结果.     

卷积神经网络的多尺度行人检测

行人检测在智能监控、自动驾驶、辅助驾驶、智能机器人等研究领域有着广泛的应用.传统的行人检测方法大多使用滑动窗口遍历图片的方式,导致计算量大,检测速度受到限制.目前基于深度学习的行人检测方法进入了一个快速的发展阶段,但是还存在例如小尺寸行人漏检等很多问题.现提出基于卷积神经网络的多尺度行人检测方法,分析了增加检测层、并联卷积层与改变卷积核尺寸对行人检测性能的影响.在KITTI数据集上的实验结果表明,该方法可以实现较好的行人检测效果.     

基于宽光谱光学遥感图像的细分光谱光学遥感图像的模拟

本文分析了现有遥感图像模拟方法的特点,介绍了地物波谱知识库的建立情况与应用意义,论述了光学传感器的成像特点及地面实测数据与遥感反演数据之间的转换关系.本文在对TM图像的光谱波段进行细划分的基础上,提出利用波谱知识库的数据支持来模拟获取具有更高光谱分辨率的细分光谱光学遥感图像的方法.本方法依据光谱数据库等先验知识的支持,通过遥感成像时辐射传输的模拟运算,在宽光谱光学遥感数据提供的图像纹理信息和地类信息的基础上,获取细分光谱波段的模拟光学遥感图像,同时避免了难以接受的计算量.细分光谱模拟图像的实验结果表明,本方法能够较为可靠的模拟出真实高光谱分辨率图像的信息,模拟方法可信.     

基于U型全卷积神经网络的路面裂缝检测

路面裂缝检测是道路运营和维护的一项重要工作,由于裂缝没有固定形状而且纹理特征受光照影响大,基于图像的精确裂缝检测是一项巨大的挑战。本文针对裂缝图像的特点,提出了一种U型结构的卷积神经网络UCrackNet。首先在跳跃连接中加入Dropout层来提高网络的泛化能力;其次,针对上采样中容易产生边缘轮廓失真的问题,采用池化索引对图像边界特征进行高保真恢复;最后,为了更好地提取局部细节和全局上下文信息,采用不同扩张系数的空洞卷积密集连接来实现感受野的均衡,同时嵌入多层输出融合来进一步提升模型的检测精度。在公开的道路裂缝数据集CrackTree206和AIMCrack上测试表明,该算法能有效地检测出路面裂缝,并且具有一定的鲁棒性。     

一种基于遥感图像的机场检测方法

目的 以SPOT（Satellite Positioning and Tracking)图像为例，介绍一种基于遥感图像通过检测机场跑道来确认机场的存在与否的机场检测方法。方法 介绍了该系统的机场检测原理工作流程，着重讨论了机场跑道通用模型的描述及感光趣区域的确定。结果和结论 实验结果证明本文所推荐的方法具有较好的检测效果。     

基于卷积神经网络的超分辨率失真控制图像重构研究

目的 解决超分辨率图像重构模型中存在的功能单元之间关联性差,图像色度特征提取完整性不强、超分辨率重构失真控制和采样过程残差控制偏弱等问题。方法 通过在卷积神经网络模型引入双激活函数,提高模型中各功能单元之间的兼容连接性;引用密集连接卷积神经网络构建超分辨率失真控制单元,分别实现对4个色度分量进行卷积补偿运算;将残差插值函数应用于上采样单元中,使用深度反投影网络规则实现超分辨率色度特征插值运算。结果 设计的模型集联了内部多个卷积核,实现了超分辨率色度失真补偿,使用了统一的处理权值,确保了整个模型内部组成单元的有机融合。结论 相关实验结果验证了本文图像重构模型具有良好可靠性、稳定性和高效性。     

Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly. To detect vehicles, the presented AEODCNN-VD model employs single shot detector (SSD) with Inception network as a baseline model. In addition, Multiway Feature Pyramid Network (MFPN) is used for handling objects of varying sizes in RSIs. The features from the Inception model are passed into the MFPN for multiway and multiscale feature fusion. Finally, the fused features are passed into bounding box and class prediction networks. For enhancing the detection efficiency of the AEODCNN-VD approach, AEO based hyperparameter optimizer is used, which is stimulated by the energy transfer strategies such as production, consumption, and decomposition in an ecosystem. The performance validation of the presented method on benchmark datasets showed promising performance over recent DL models.     

DDoS Attack Detection via Multi-Scale Convolutional Neural Network

Distributed Denial-of-Service (DDoS) has caused great damage to the network in the big data environment. Existing methods are characterized by low computational efficiency, high false alarm rate and high false alarm rate. In this paper, we propose a DDoS attack detection method based on network flow grayscale matrix feature via multiscale convolutional neural network (CNN). According to the different characteristics of the attack flow and the normal flow in the IP protocol, the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary. Based on the network flow grayscale matrix feature (GMF), the convolution kernel of different spatial scales is used to improve the accuracy of feature segmentation, global features and local features of the network flow are extracted. A DDoS attack classifier based on multi-scale convolution neural network is constructed. Experiments show that compared with correlation methods, this method can improve the robustness of the classifier, reduce the false alarm rate and the missing alarm rate.     

Resampling Factor Estimation via Dual-Stream Convolutional Neural Network

The estimation of image resampling factors is an important problem in image forensics. Among all the resampling factor estimation methods, spectrumbased methods are one of the most widely used methods and have attracted a lot of research interest. However, because of inherent ambiguity, spectrum-based methods fail to discriminate upscale and downscale operations without any prior information. In general, the application of resampling leaves detectable traces in both spatial domain and frequency domain of a resampled image. Firstly, the resampling process will introduce correlations between neighboring pixels. In this case, a set of periodic pixels that are correlated to their neighbors can be found in a resampled image. Secondly, the resampled image has distinct and strong peaks on spectrum while the spectrum of original image has no clear peaks. Hence, in this paper, we propose a dual-stream convolutional neural network for image resampling factors estimation. One of the two streams is gray stream whose purpose is to extract resampling traces features directly from the rescaled images. The other is frequency stream that discovers the differences of spectrum between rescaled and original images. The features from two streams are then fused to construct a feature representation including the resampling traces left in spatial and frequency domain, which is later fed into softmax layer for resampling factor estimation. Experimental results show that the proposed method is effective on resampling factor estimation and outperforms some CNN-based methods.     

Data Augmentation Using Contour Image for Convolutional Neural Network

With the development of artificial intelligence-related technologies such as deep learning, various organizations, including the government, are making various efforts to generate and manage big data for use in artificial intelligence. However, it is difficult to acquire big data due to various social problems and restrictions such as personal information leakage. There are many problems in introducing technology in fields that do not have enough training data necessary to apply deep learning technology. Therefore, this study proposes a mixed contour data augmentation technique, which is a data augmentation technique using contour images, to solve a problem caused by a lack of data. ResNet, a famous convolutional neural network (CNN) architecture, and CIFAR-10, a benchmark data set, are used for experimental performance evaluation to prove the superiority of the proposed method. And to prove that high performance improvement can be achieved even with a small training dataset, the ratio of the training dataset was divided into 70%, 50%, and 30% for comparative analysis. As a result of applying the mixed contour data augmentation technique, it was possible to achieve a classification accuracy improvement of up to 4.64% and high accuracy even with a small amount of data set. In addition, it is expected that the mixed contour data augmentation technique can be applied in various fields by proving the excellence of the proposed data augmentation technique using benchmark datasets.     

卷积神经网络在印刷品缺陷检测的应用

目的 为了改善传统机器检测印刷产品缺陷存在误费率高的不足。方法 提出以卷积神经网络为控制核心的印刷品缺陷检测系统。设计可在实际检测中应用的卷积神经网络,设计在线印刷质量检测系统的硬件结构。结果 对结构相同而训练次数、学习率不同的卷积神经网络进行了缺陷检测的性能对比,验证了该卷积神经网络在学习率小于0.01时,可以获得较好的识别效果;在学习率大于0.05时,网络不容易收敛。网络训练次数越多,精度越高,相应的训练时间也较长。在满足快速性和精确度的条件下,确定了适应某印刷品的缺陷检验网络训练次数为50,学习率为0.005,此时的识别率为90%。结论 经过实验证明,该检测系统具有良好的缺陷识别能力,缺陷类型的分类准确率较高。该系统具有一定的实用价值。     

卷积神经网络在无纺布缺陷分类检测中的应用

目的针对传统无纺布缺陷分类检测中人工依赖性强、效率低等问题,提出一种能够满足工厂要求的卷积神经网络分类检测方法。方法首先建立包括脏点、褶皱、断裂、缺纱和无缺陷等5种共计7万张无纺布图像样本库,其次构造一个具有不同神经元个数的卷积层和池化层的神经网络,然后采用反向传播算法逐层更新权值,通过梯度下降法最小化损失函数,最后利用Softmax分类器实现无纺布的缺陷分类检测。结果构建了12层的卷积神经网络,通过2万张样本进行测试实验,无缺陷样本准确率可以达到100%,缺陷样本分类准确率均在95%以上,检测时间在35 ms以内。结论该方法能够满足工业生产线中对于无纺布缺陷实时分类检测的要求。     

遥感图像的云分类和云检测技术研究

为了有效减小云层遮盖对遥感图像数据利用率的影响,提出了一种基于灰度特性的算法,实现了遥感图像高效自动的云分类及云检测.该方法首先将大幅遥感图像切分成小块子图,然后统计子图灰度值的均值和方差,在此基础上将云分成无云、薄云和厚云三类,最后通过边缘检测算法,实现了对厚云影响范围的有效标记.对100幅典型水域遥感图像的实验测试结果表明：正确云分类判别率达到97％,误判率小于4％,漏判率小于2％,基本满足实时性需求,证明了该算法的有效性.     

基于卷积神经网络的印刷套准识别方法

目的 针对目前印刷套准识别方法依赖于经验人工设计特征提取的问题,提出一种不需要人工提取图像特征的卷积神经网络模型,实现印刷套准状态的识别.方法 采用图像增强技术实现不均衡训练集的均衡化,增加训练集图像的数量,提高模型的识别准确率.设计基于AlexNet网络结构的印刷套准识别模型的结构参数,分析批处理样本数量和基础学习率对模型性能的影响规律.结果 文中方法获得的总印刷套准识别准确率为0.9860,召回率为1.0000,分类准确率几何平均数为0.9869.结论 文中方法能自动提取图像特征,不依赖于人工设计的特征提取方法.在构造的数据集上,文中方法的分类性能优于实验中的支持向量机方法.