随着卫星遥感技术的不断发展，基于内容的遥感图像检索技术越来越受到关注。目前该方向的研究主要集中在对遥感图像中不同特征的提取和融合方面，这些方法普遍忽略了这样一个事实：对于不同类型的检索目标，特征应该是不同的。另外，小样本问题也是遥感图像检索中一个较为突出的问题。基于以上两方面考虑，本文提出一种基于特征选择和半监督学习的遥感图像检索新方法，该方法主要包括4个方面：1）利用最小描述长度准则自动确定聚类数目；2）结合聚类方法和适当的聚类有效性指标选择最能表示检索目标的特征，在计算聚类有效性指数时，针对遥感图像检索特点对原有的Davies-Bouldin指数进行了改进；3）动态确定最优颜色特征和最优纹理特征之间的权重；4）根据最优颜色特征和最优纹理特征的权重自动确定半监督学习方法，并进行遥感图像的检索。实验结果表明，与相关反馈方法的检索效果相比，该算法在土壤侵蚀区域检索以及其他一般地表覆盖目标检索中均获得了相近的检索效果，但不需要用户多次反馈。

Conventional relevance feedback (RF) schemes improve the performance of content-based image retrieval (CBIR) requiring the user to annotate a large number of images. To reduce the labeling effort of the user, this paper presents a novel active learning (AL) method to drive RF for retrieving remote sensing images from large archives in the framework of the support vector machine classifier. The proposed AL method is specifically designed for CBIR and defines an effective and as small as possible set of relevant and irrelevant images with regard to a general query image by jointly evaluating three criteria: uncertainty; diversity; and density of images in the archive. The uncertainty and diversity criteria aim at selecting the most informative images in the archive, whereas the density criterion goal is to choose the images that are representative of the underlying distribution of data in the archive. The proposed AL method assesses jointly the three criteria based on two successive steps. In the first step, the most uncertain (i.e., ambiguous) images are selected from the archive on the basis of the margin sampling strategy. In the second step, the images that are both diverse (i.e., distant) to each other and associated to the high-density regions of the image feature space in the archive are chosen from the most uncertain images. This step is achieved by a novel clustering-based strategy. The proposed AL method for driving the RF contributes to mitigate problems of unbalanced and biased set of relevant and irrelevant images. Experimental results show the effectiveness of the proposed AL method.

In this paper, we present the results of applying global morphological texture descriptors to the problem of content-based remote sensing image retrieval. Specifically, we explore the potential of recently developed multiscale texture descriptors, namely, the circular covariance histogram and the rotation-invariant point triplets. Moreover, we introduce a couple of new descriptors, exploiting the Fourier power spectrum of the quasi-flat-zone-based scale space of their input. The descriptors are evaluated with the UC Merced Land Use-Land Cover data set, which has been only recently made public. The proposed approach is shown to outperform the best known retrieval scores, despite its shorter feature vector length, thus asserting the practical interest of global content descriptors as well as of mathematical morphology in this context.

海量遥感图像的自动查询和选择，迫切需要有效的基于内容的图像检索方法。鉴于单一视觉特征不能很好地表达图像内容，为此提出一种基于五叉树分解的线性加权颜色和纹理特征距离的检索新方法。该方法首先采用五叉树分解法分解图像，然后在利用多通道Gabor滤波器与图像做卷积得到滤波能量值的基础上，提取各子图像滤波能量纹理特征，最后通过计算子图像的颜色均值和均方差来对查询图像和与其大小相当的数据库子图像进行颜色和纹理特征线性加权距离相似性测度。将该方法用于高分辨率卫星和航空遥感图像数据库检索的实验结果证明，该方法是有效的。

This paper investigates local invariant features for geographic (overhead) image retrieval. Local features are particularly well suited for the newer generations of aerial and satellite imagery whose increased spatial resolution, often just tens of centimeters per pixel, allows a greater range of objects and spatial patterns to be recognized than ever before. Local invariant features have been successfully applied to a broad range of computer vision problems and, as such, are receiving increased attention from the remote sensing community particularly for challenging tasks such as detection and classification. We perform an extensive evaluation of local invariant features for image retrieval of land-use/land-cover (LULC) classes in high-resolution aerial imagery. We report on the effects of a number of design parameters on a bag-of-visual-words (BOVW) representation including saliency-versus grid-based local feature extraction, the size of the visual codebook, the clustering algorithm used to create the codebook, and the dissimilarity measure used to compare the BOVW representations. We also perform comparisons with standard features such as color and texture. The performance is quantitatively evaluated using a first-of-its-kind LULC ground truth data set which will be made publicly available to other researchers. In addition to reporting on the effects of the core design parameters, we also describe interesting findings such as the performance-efficiency tradeoffs that are possible through the appropriate pairings of different-sized codebooks and dissimilarity measures. While the focus is on image retrieval, we expect our insights to be informative for other applications such as detection and classification.

High resolution remote sensing image captured by the satellites or the aircraft is of great help for military and civilian applications. In recent years, with an increasing amount of high resolution remote sensing images, it becomes more and more urgent to find a way to retrieve them. In this case, a few methods based on the statistical information of the local features are proposed, which have achieved good performances. However, most of the methods do not take the topological structure of the features into account. In this paper, we propose a new method to represent these images, by taking the structural information into consideration. The main contributions of this paper include: (1) mapping the features into a manifold space by a Lipschitz smooth function to enhance the representation ability of the features; (2) training an anchor set with several regularization constrains to get the intrinsic manifold structure. In the experiments, the method is applied to two challenging remote sensing image datasets: UC Merced land use dataset and Sydney dataset. Compared to the state-of-the-art approaches, the proposed method can achieve a more robust and commendable performance.

作为一个十余年来快速发展的崭新领域,深度学习受到了越来越多研究者的关注,它在特征提取和建模上都有着相较于浅层模型显然的优势.深度学习善于从原始输入数据中挖掘越来越抽象的特征表示,而这些表示具有良好的泛化能力.它克服了过去人工智能中被认为难以解决的一些问题.且随着训练数据集数量的显著增长以及芯片处理能力的剧增,它在目标检测和计算机视觉、自然语言处理、语音识别和语义分析等领域成效卓然,因此也促进了人工智能的发展.深度学习是包含多级非线性变换的层级机器学习方法,深层神经网络是目前的主要形式,其神经元间的连接模式受启发于动物视觉皮层组织,而卷积神经网络则是其中一种经典而广泛应用的结构.卷积神经网络的局部连接、权值共享及池化操作等特性使之可以有效地降低网络的复杂度,减少训练参数的数目,使模型对平移、扭曲、缩放具有一定程度的不变性,并具有强鲁棒性和容错能力,且也易于训练和优化.基于这些优越的特性,它在各种信号和信息处理任务中的性能优于标准的全连接神经网络.该文首先概述了卷积神经网络的发展历史,然后分别描述了神经元模型、多层感知器的结构.接着,详细分析了卷积神经网络的结构,包括卷积层、池化层、全连接层,它们发挥着不同的作用.然后,讨论了网中网模型、空间变换网络等改进的卷积神经网络.同时,还分别介绍了卷积神经网络的监督学习、无监督学习训练方法以及一些常用的开源工具.此外,该文以图像分类、人脸识别、音频检索、心电图分类及目标检测等为例,对卷积神经网络的应用作了归纳.卷积神经网络与递归神经网络的集成是一个途径.为了给读者以尽可能多的借鉴,该文还设计并试验了不同参数及不同深度的卷积神经网络来分析各参数间的相互关系及不同参数设置对结果的影响.最后,给出了卷积神经网络及其应用中待解决的若干问题.

遥感图像数据的海量性、多样性和复杂性等特点对遥感图像检索的速度和精度提出了更高的要求,其中特征提取是影响遥感图像检索效果的关键。本文方法首先对遥感图像进行预处理,然后基于稀疏自动编码的方法在大量未标注的遥感图像上进行特征学习得到特征字典,基于卷积神经网络的思想,使用训练出来的特征字典对遥感图像进行卷积和池化得到每幅图像的特征图;接下来使用特征图训练Softmax分类器;最后对待检索图像分类,在同一类别中计算特征间的距离,进而实现遥感图像的检索。实验结果表明,该方法能够有效提高遥感图像检索的速度和准确度。

In this paper we present an extensive evaluation of visual descriptors for the content-based retrieval of remote sensing (RS) images. The evaluation includes global hand-crafted, local hand-crafted, and Convolutional Neural Network (CNNs) features coupled with four different Content-Based Image Retrieval schemes. We conducted all the experiments on two publicly available datasets: the 21-class UC Merced Land Use/Land Cover (LandUse) dataset and 19-class High-resolution Satellite Scene dataset (SceneSat). The content of RS images might be quite heterogeneous, ranging from images containing fine grained textures, to coarse grained ones or to images containing objects. It is therefore not obvious in this domain, which descriptor should be employed to describe images having such a variability. Results demonstrate that CNN-based features perform better than both global and and local hand-crafted features whatever is the retrieval scheme adopted. Features extracted from SatResNet-50, a residual CNN suitable fine-tuned on the RS domain, shows much better performance than a residual CNN pre-trained on multimedia scene and object images. Features extracted from NetVLAD, a CNN that considers both CNN and local features, works better than others CNN solutions on those images that contain fine-grained textures and objects.

Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the content complexity of remote sensing images. In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNN) for high-resolution remote sensing image retrieval (HRRSIR). To this end, two effective schemes are proposed to generate powerful feature representations for HRRSIR. In the first scheme, the deep features are extracted from the fully-connected and convolutional layers of the pre-trained CNN models, respectively; in the second scheme, we propose a novel CNN architecture based on conventional convolution layers and a three-layer perceptron. The novel CNN model is then trained on a large remote sensing dataset to learn low dimensional features. The two schemes are evaluated on several public and challenging datasets, and the results indicate that the proposed schemes and in particular the novel CNN are able to achieve state-of-the-art performance.

Learning efficient image representations is at the core of the scene classification task of remote sensing imagery. The existing methods for solving the scene classification task, based on either feature coding approaches with low-level hand-engineered features or unsupervised feature learning, can only generate mid-level image features with limited representative ability, which essentially prevents them from achieving better performance. Recently, the deep convolutional neural networks (CNNs), which are hierarchical architectures trained on large-scale datasets, have shown astounding performance in object recognition and detection. However, it is still not clear how to use these deep convolutional neural networks for high-resolution remote sensing (HRRS) scene classification. In this paper, we investigate how to transfer features from these successfully pre-trained CNNs for HRRS scene classification. We propose two scenarios for generating image features via extracting CNN features from different layers. In the first scenario, the activation vectors extracted from fully-connected layers are regarded as the final image features; in the second scenario, we extract dense features from the last convolutional layer at multiple scales and then encode the dense features into global image features through commonly used feature coding approaches. Extensive experiments on two public scene classification datasets demonstrate that the image features obtained by the two proposed scenarios, even with a simple linear classifier, can result in remarkable performance and improve the state-of-the-art by a significant margin. The results reveal that the features from pre-trained CNNs generalize well to HRRS datasets and are more expressive than the low- and mid-level features. Moreover, we tentatively combine features extracted from different CNN models for better performance.