Georegistration of airborne hyperspectral image data

A suite of geometric sensor and platform modeling tools has been developed which have achieved consistent subpixel accuracy in orthorectification experiments. Aircraft platforms in turbulent atmospheric conditions present unique challenges and have required creative modeling approaches. The geometric relationship between an image point and a ground object has been modeled by rigorous photogrammetric methods. First and second order Gauss-Markov processes have been used to estimate the platform trajectory. These methods have been successfully applied to HYDICE and HyMap data sets. The most important contributors to the subpixel rectification accuracy have been the first order Gauss-Markov model with control linear features     

Dense optical flow via robust data fusion

Locally optimised differential methods for computing optical flow have the merit of being faster and more reliable under noise when compared to their globally optimised counterparts. However, they produce sparse flow estimates as a result of being unable to deal with local image regions of little texture. They are also particularly inefficient for regions whose change of feature-constancy function is highly nonlinear. In this study, we treat several limitations of the local approach in monochrome images. We present a robust H_∞ data fusion-based framework to propagate flow information from high confidence regions to those suspected of poor quality estimates. The adopted data fusion engine is tolerant towards uncertainty and error inherited in the optical flow computation process. A new integrated confidence measure is also presented to predict the accuracy of the recovered flow across the image enabling the data fusion engine to work as an intelligent filling-in effect, not only when the intensity is problematic but also for other uncertain regions. Results demonstrate the significance of the proposed method.     

Registering Landsat images by point matching

Image registration of Landsat Thematic Mapper scenes with translational and rotational differences is studied. Two major steps of image registration, control-point selection and control-point matching, are emphasized. In control-point selection, the properties that a good control point should satisfy are defined. Several potential control-point candidates are suggested, and methods are discussed for extracting them from the input image. In control-point matching, a relaxation algorithm proposed in the literature is improved by reducing its time complexity from O(n⁴) to O(n³ ), where n is the number of control points. The matching algorithm also uses a two-way matching concept which utilizes the inherent symmetry property of the point-matching problem. The robustness of the proposed algorithm was demonstrated through simulation experiments by evaluating a matching index. Experimental results on Landsat images show that the proposed method produces results comparable to those obtained by an experienced photointerpreter     

基于小波多尺度表示的图像匹配研究

根据小波变换系数对信号平移步长的变化规律,提出一种基于小波金字塔结构的、遍历式的图像匹配方法,这种方法消除了由于小波变换对平移的敏感性所引起的误匹配,在匹配策略上,以小波分解高频分量的匹配为主。实验证实,本匹配方法对实时图和参考图的局部灰度反转不敏感,具有一定抗几何失真的能力,优于经典的灰度相关匹配。     

一种鲁棒的SAR图像匹配方法

针对合成孔径雷达（Synthetic Aperture Radar,SAR）图像匹配中虚假匹配和鲁棒性欠缺的问题,提出了一种鲁棒的SAR图像匹配方法。首先通过正射校正来进行预处理,从而消除SAR图像的旋转;然后利用相关匹配方法初步提取同名点;最后根据同名点的空间位置来筛选同名点并计算仿射矩阵进行匹配,提高了SAR图像匹配的精度和鲁棒性。实验证明了所提方法的有效性。     

一种改进的快速鲁棒性特征匹配算法

针对光照变化、噪声、局部遮挡等在图像配准技术中对配准精度有重要影响,提出了一种在多尺度空间下点预测快速鲁棒性不变特征的匹配算法。针对在探测对图像的尺度、旋转,仿射具有不变性的斑状特征极值点过程中计算复杂度较高的问题,提出一种特征点预测方法降低了描述子提取的复杂度,增强了对外部环境光照变化、噪声以及局部遮挡的适应能力;并在KD(KD Tree)树基础上,提出了一种动态平衡KD树(DBKD-Tree)快速搜索匹配算法,有效克服了KD树可能存在的病态划分,采用条件约束最邻近搜索,提升匹配效率,实现特征点高精度匹配。通过对在不同光照条件、噪声环境的仿射变换图像特征匹配测试,在加入20%的高斯噪声后,均能100%地完成重复特征检测,达到亚像素定位精度,误配率降低为零。     

Robust object tracking based on adaptive templates matching via the fusion of multiple features

Moving object tracking under complex scenes remains to be a challenging problem because the appearance of a target object can be drastically changed due to several factors, such as occlusions, illumination, pose, scale change and deformation. This study proposes an adaptive multi–feature fusion strategy, in which the target appearance is modeled based on timed motion history image with HSV color histogram features and edge orientation histogram features. The variances based on the similarities between the candidate patches and the target templates are used for adaptively adjusting the weight of each feature. Double templates matching, including online and offline template matching, is adopted to locate the target object in the next frame. Experimental evaluations on challenging sequences demonstrate the accuracy and robustness of the proposed algorithm in comparison with several state-of-the-art algorithms.     

Fast algorithm for robust template matching with M-estimators

We propose a fast algorithm for speeding up the process of template matching that uses M-estimators for dealing with outliers. We propose a particular image hierarchy called the p-pyramid that can be exploited to generate a list of ascending lower bounds of the minimal matching errors when a nondecreasing robust error measure is adopted. Then, the set of lower bounds can be used to prune the search of the p-pyramid, and a fast algorithm is thereby developed in this paper. This fast algorithm ensures finding the global minimum of the robust template matching problem in which a nondecreasing M-estimator serves as an error measure. Experimental results demonstrate the effectiveness of our method.     

Layered video coding based on displaced frame difference prediction and multiresolution block matching

This paper presents a new video-coding technique based on displaced frame difference (DFD) prediction and multiresolution block matching for layered transmission. Starting from the base layer, the algorithm encodes one layer at a time until the design of the top layer is completed. For the encoding of each layer, the DFD prediction and multistage motion-vector quantization (MSMVQ) techniques are employed so that the results of DFD encoding and block matching at previous layers can be used to enhance the coding efficiency at that layer. Numerical results show that the algorithm is effective for the applications where high rate-distortion performance and low computational complexity for layered video transmission are required.     

Object matching algorithms using robust Hausdorff distance measures

A Hausdorff distance (HD) is one of commonly used measures for object matching. This work analyzes the conventional HD measures and proposes two robust HD measures based on m-estimation and least trimmed square (LTS) which are more efficient than the conventional HD measures. By computer simulation, the matching performance of the conventional and proposed HD measures is compared with synthetic and real images.     

A bayesian approach to classification of multiresolution remote sensing data

Several earth observation satellites acquire image bands with different spatial resolutions, e.g., a panchromatic band with high resolution and spectral bands with lower resolution. Likewise, we often face the problem of different resolutions when performing joint analysis of images acquired by different satellites. This work presents models and methods for classification of multiresolution images. The approach is based on the concept of a reference resolution, corresponding to the highest resolution in the dataset. Prior knowledge about the spatial characteristics of the classes is specified through a Markov random field model at the reference resolution. Data at coarser scales are modeled as mixed pixels by relating the observations to the classes at the reference resolution. A Bayesian framework for classification based on this multiscale model is proposed. The classification is realized by an iterative conditional modes (ICM) algorithm. The parameter estimation can be based both on a training set and on pixels with unknown class. A computationally efficient scheme based on a combination of the ICM and the expectation-maximization algorithm is proposed. Results obtained on simulated and real satellite images are presented.     

Context-aware features and robust image representations

Local image features are often used to efficiently represent image content. The limited number of types of features that a local feature extractor responds to might be insufficient to provide a robust image representation. To overcome this limitation, we propose a context-aware feature extraction formulated under an information theoretic framework. The algorithm does not respond to a specific type of features; the idea is to retrieve complementary features which are relevant within the image context. We empirically validate the method by investigating the repeatability, the completeness, and the complementarity of context-aware features on standard benchmarks. In a comparison with strictly local features, we show that our context-aware features produce more robust image representations. Furthermore, we study the complementarity between strictly local features and context-aware ones to produce an even more robust representation.     

Atmospheric effects on Landsat TM thermal IR data

The components of atmospherically attenuated target radiance and the path radiance emitted by the atmosphere are calculated to explain the fact that for certain meteorological conditions, properly calibrated thermal IR (infrared) data gathered from aircraft and spacecraft altitudes provide accurate temperature measurements of surface water bodies even when atmospheric corrections are not applied. Results show that although the 8-14-μm atmospheric window is far from being transparent (<50% transmission), the amount of atmospheric path radiance may be equal to the amount of attenuated target radiance. Errors in remotely sensed temperatures introduced by atmospheric effects are shown to be smaller than or of the same order of magnitude as those errors caused by sensor noise and the effects of applying a cubic convolution during the process of converting the TM (Thematic Mapper) data from A-tape to geometrically corrected P-tape data format     

Automated co-superpixel generation via graph matching

In this paper, a novel ‘co-superpixel’ generation method is proposed via the graph matching. The co-superpixel can capture the common semantic information in coupled images. Therefore, it is significant for various applications in visual pattern recognition. Specifically, we first introduce a superpixel correspondence method based on the graph matching. The main property is that it has the ability to capture the consistent intermediate-level semantic information in coupled images, which can represent the region-based similarity rather than the conventional similarity based on low-level vision features. Second, a new co-superpixel generation method is proposed by the superpixel-merging incorporated with the graph matching cost and the adjacent superpixel appearance similarity in coupled images simultaneously. Furthermore, we extend the proposed co-superpixel method to tackle the object matching problem. The experimental results show that the object matching can be effectively addressed by the co-superpixel. The proposed method is effective for challenging cases in which object appearance changes, deformation and background clutter.     

Landmark matching via large deformation diffeomorphisms

This paper describes the generation of large deformation diffeomorphisms phi:Omega=[0,1](3)right harpoon over left harpoonOmega for landmark matching generated as solutions to the transport equation dphi(x,t)/dt=nu(phi(x,t),t),tin[0,1] and phi(x,0)=x, with the image map defined as phi(.,1) and therefore controlled via the velocity field nu(.,t),tin[0,1]. Imagery are assumed characterized via sets of landmarks {x(n), y(n), n=1, 2, ..., N}. The optimal diffeomorphic match is constructed to minimize a running smoothness cost parallelLnu parallel(2) associated with a linear differential operator L on the velocity field generating the diffeomorphism while simultaneously minimizing the matching end point condition of the landmarks. Both inexact and exact landmark matching is studied here. Given noisy landmarks x(n) matched to y(n ) measured with error covariances Sigma(n), then the matching problem is solved generating the optimal diffeomorphism phi;(x,1)=integral(0)(1 )nu;(phi;(x,t),t)dt+x where nu;(.)argmin(nu(.))integral(1) (1)integral(Omega) parallelLnu(x,t) parallel(2)dxdt +Sigma(n=1)(N)[y(n)-phi(x(n),1)] (T)Sigma(n)(-1)[y(n)-phi(x(n ),1)]. Conditions for the existence of solutions in the space of diffeomorphisms are established, with a gradient algorithm provided for generating the optimal flow solving the minimum problem. Results on matching two-dimensional (2-D) and three-dimensional (3-D) imagery are presented in the macaque monkey.     

基于视觉模糊的LBP鲁棒特征提取与匹配

目的针对目前模糊图像特征提取与匹配方面, 存在特征提取困难、匹配率低、抗噪以及抗尺度变 化能力弱的缺陷。方法提出一种基于SIFT算法与改进的中心对称局部二值模式相结合的精准 、特征识别 率高的匹配算法。首先采用SIFT进行特征的提取,生成多维的描述子,其次采用本文改进的 中心对称局 部二值模式对高维特征描述子进行降维处理,并采用局部特征区域对降维后的描述子进行特 征检测,并生 成纹理特征图像以及信息分布直方图,对特征区域的特征点进行信息量统计,并设置检测阈 值。提取符合 特征信息要求的特征点,并依据Hausdorff距离算法实现图像粗匹配,最后采用RANSAC算法 进行误差匹 配的剔除来改善匹配的精度和鲁棒性。结果测试结果表明,本文所建议的算法是有效的,它 不仅具有良 好的模糊图像分辨能力和抗尺度变化特性,而且具有较强的噪声抑制能力和抗光照变化能力 。结论本文 提出的基于视觉模糊的鲁棒特征匹配算法,不仅考虑到传统特征匹配算法的优缺点,也提出 了算法改进的 新思路,而且较SIFT算法以及LBP算法稳定性和准确度有了明显的提高。     

一种基于吸收峰特征的高光谱曲线匹配方法

为了解决在应用传统高光谱地物识别方法时,由于吸收峰个数不同,造成的光谱匹配误差较大的问题,采用了一种基于高光谱吸收峰特征的选择方法,根据选择后的吸收峰特征进行光谱曲线匹配。该算法首先对高光谱曲线进行包络线消除并提取光谱特征参量矩阵,然后根据标准特征参量矩阵与待测特征参量矩阵的每个向量的余弦距离-欧氏距离来逐一寻找吸收峰的匹配向量,之后根据选择的吸收峰特征参量矩阵进行了理论分析和实验验证。结果表明,该算法可以搜寻到最佳的特征参量向量,从而实现吸收峰的选择,用选择后的吸收峰的特征参量矩阵进行高光谱匹配。这一结果对降低匹配的误差是有帮助的。     

基于Landsat-8陆地卫星数据的火点检测方法研究

传统的火点检测算法通常利用高温地物在中红外波段或热红外波段的高发射率特性来提取火点, 然而受制于影像空间分辨率的限制如MODIS、AVHRR等, 使得很多小规模火情现象被漏检.研究发现短波红外数据也同样能被用于高温地物的识别和检测, 并且相较于热红外波段数据对低温和高温地物的区分度更大, 在精确识别和定位高温目标方面更加准确.文章利用空间分辨率为30米的Landsat-8 OLI传感器数据, 根据高温火点在近红外及短波红外波段的波谱特性, 利用改进的归一化燃烧指数(NBRS)结果自适应地确定阈值来提取疑似火点, 然后再利用高温火点在短波红外的峰值关系进行误检点剔除, 从而得到最终的火点产品.提出的算法能检测到所占像元面积10%左右的火点, 并能够有效地排除云层及建筑物的干扰, 在保证较低漏检率的同时还能达到90%左右的准确率, 相比于传统算法的火点提取精度有很大的提高.     

Effective short text classification via the fusion of hybrid features for IoT social data

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.