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高分辨率光学遥感图像特征提取方法研究
王国利
学位类型工学博士
导师潘春洪
2017-05
学位授予单位中国科学院大学
学位授予地点北京
关键词高分辨率光学遥感图像 特征提取 旋转不变 特征编码 深度学习
摘要

高分辨率光学遥感图像的特征提取是遥感领域的研究热点,是实现目标检测、场景分类等计算机视觉应用的基础。随着计算机视觉和模式识别等学科领域的发展,高分辨率光学遥感图像的特征提取研究已经取得了长足的进步。然而,高分辨率遥感图像中存在复杂多变的地物目标外貌、剧烈的光照变化、地物目标的多方向旋转、部分遮挡和有限的标注数据等问题,提取有判别力的特征仍然是一个难题。
本文针对高分辨率光学遥感图像的目标检测和场景分类问题,将先验知识与特征编码、深度学习等新方法有机结合,研究高分辨率光学遥感图像的特征提取方法。论文的主要贡献如下:
1. 提出一种新的基于序金字塔的特征汇聚方法,对高分辨率遥感图像中的目标进行旋转不变特征描述。该方法将图像区域内的像素点按照其径向梯度方向(或者径向梯度角)的大小进行排序,并采用金字塔式的划分策略将排序后的像素点由粗到细地均匀划分为若干个群组,分别计算、汇聚各群组内像素点的局部特征。这种划分方式可以在保持旋转不变性的同时编码径向梯度方向(或者径向梯度角)的分布信息,并提高对像素点径向梯度方向(或径向梯度角)顺序漂移的鲁棒性。最终的特征描述由各群组的汇聚特征串联获得。为了保持该方法的旋转不变性,使用理论上旋转不变的 Fourier HOG 特征作为像素点的局部特征,并使用 Fisher Vector 编码技术进一步提升其判别能力。论文在高分辨率遥感图像的飞机检测和车辆检测实验中论证了该方法的有效性。
2. 提出一种新的旋转不变矩阵视觉特征描述方法,可以在包含径向空间信息的同时编码角度空间信息,并能保持提取特征的旋转不变性。基于旋转不变矩阵特征,进一步提出了一种更具有判别能力的旋转不变特征提取方法。具体地,首先使用旋转不变矩阵特征描述图像内稠密的、相互重叠的子区域;然后使用 Fisher Vector 对所提特征进一步编码;最后,为了在保持旋转不变性的同时编码更丰富的空间信息,使用环金字塔的划分策略将 Fisher Vectors由粗到细地划分为多个群组,并分别对其进行特征汇聚。这些精心设计的步骤,使得该方法在高分辨率遥感图像的飞机检测和车辆检测中表现出良好的性能。
3. 提出一种基于卷积神经网络的混合分辨率编码特征提取方法。该方法使用局部聚合描述符分别编码卷积神经网络的底层和中层的卷积特征,并使用主成分分析对这些编码特征降维,生成层次化的全局特征;同时,该方法对卷积神经网络中各全连接特征分别进行平均池化和归一化处理,并在其基础上生成新的全局特征。混合分辨率编码特征是上述所有全局特征的串联,它可以同时包含卷积神经网络的底层、中层和高层特征。由于局部聚合描述符和平均池化策略的使用,所提方法可以处理不同尺寸的图像。在两个国际公开的UC-Merced 数据集和 Brazilian Coffee Scenes 数据集上的场景分类实验论证了所提方法的有效性。

其他摘要Feature extraction for high-resolution optical remote sensing images is a hot topic in the remote sensing community. It is the basis of many computer vision applications in high-resolution remote sensing images, such as, object detection, scene classification, etc. With the development of computer vision and pattern recognition, feature extraction has made great progress in high-resolution optical remote sensing images. However, due to complicated and variable object appearances, dramatic illumination changes, objects with arbitrary orientations, partial occlusions and limited annotation data, extracting discriminative features from high-resolution remote sensing images is still a challenge problem.
By combining prior knowledge with new methods in feature encoding and deep learning, this dissertation aims to develop feature extraction methods for object detection and scene classification in high-resolution optical remote sensing images. The main contributions can be summarized as follows.
1. A novel ordinal pyramid pooling method is proposed to extract rotation invariant features for objects in high-resolution remote sensing images. It first sorts pixels in the described region according to their radial gradient orientations (or radial gradient angles). Then a pyramid strategy is used to hierarchically partition these sorted pixels into even subgroups and local features of pixels are accumulated in each subgroup. This partition strategy can encode radial gradient orientation (or radial gradient angle) distribution information while maintain the rotation invariance, and improve the robustness to ordinal shift. The final representation is the concatenation of all pooled features from all subgroups. To maintain the rotation invariance, the proposed method uses Fourier HOG, a theoretically rotation invariant feature, as each pixel’s local feature and further improves its discriminative ability by Fisher Vector. Experiments on airplane detection and car detection in high-resolution remote sensing images demonstrate the effectiveness of the proposed method.
2. A novel visual feature description way named as rotation invariant matrix is proposed. It can incorporate angular spatial information in addition to radial spatial information, and maintain the rotation invariance. Based on the rotation invariant matrix, a rotation invariant feature extraction method with stronger discriminant ability can be proposed. Specifically, this method densely extracts rotation invariant matrix features from overlapping image subregions; then these extracted features are encoded into Fisher Vectors; finally, a pyramid pooling strategy that hierarchically accumulates Fisher vectors in ring subgroups is used to encode richer spatial information while maintaining the rotation invariance. These aborative designs help the proposed method achieve good experimental results on airplane detection and car detection in high-resolution remote sensing images.
3. A novel mixed-resolution encoded feature extraction method based on convolutional neutral networks is proposed. The low-level and middle-level intermediate convolutional features are respectively encoded by vector of locally aggregated descriptors (VLAD) and then reduced by principal component analysis to obtain hierarchical global features; meanwhile, the fully-connected features are average pooled and subsequently normalized to form new global features. The proposed encoded mixed-resolution representation is the concatenation of all the above global features. It can incorporate features from low-level, middle-level, and high-level simultaneously. Due to the usage of encoding strategies (VLAD and average pooling), the proposed method can deal with images of different sizes. Comparative experiments on two  international public remote sensing scene classification datasets, UC-Merced and Brazilian Coffee Scenes, validate the effectiveness of the proposed method.
文献类型学位论文
条目标识符http://ir.ia.ac.cn/handle/173211/14832
专题毕业生_博士学位论文
作者单位中国科学院自动化研究所
推荐引用方式
GB/T 7714
王国利. 高分辨率光学遥感图像特征提取方法研究[D]. 北京. 中国科学院大学,2017.
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