Matching Large Baseline Oblique Stereo Images Using an End-to-End Convolutional Neural Network

Abstract

The available stereo matching algorithms produce large number of false positive matches or only produce a few true-positives across oblique stereo images with large baseline. This undesired result happens due to the complex perspective deformation and radiometric distortion across the images. To address this problem, we propose a novel affine invariant feature matching algorithm with subpixel accuracy based on an end-to-end convolutional neural network (CNN). In our method, we adopt and modify a Hessian affine network, which we refer to as IHesAffNet, to obtain affine invariant Hessian regions using deep learning framework. To improve the correlation between corresponding features, we introduce an empirical weighted loss function (EWLF) based on the negative samples using K nearest neighbors, and then generate deep learning-based descriptors with high discrimination that is realized with our multiple hard network structure (MTHardNets). Following this step, the conjugate features are produced by using the Euclidean distance ratio as the matching metric, and the accuracy of matches are optimized through the deep learning transform based least square matching (DLT-LSM). Finally, experiments on Large baseline oblique stereo images acquired by ground close-range and unmanned aerial vehicle (UAV) verify the effectiveness of the proposed approach, and comprehensive comparisons demonstrate that our matching algorithm outperforms the state-of-art methods in terms of accuracy, distribution and correct ratio. The main contributions of this article are: (i) our proposed MTHardNets can generate high quality descriptors; and (ii) the IHesAffNet can produce substantial affine invariant corresponding features with reliable transform parameters.

Regional Crustal Vertical Deformation Driven by Terrestrial Water Load Depending on CORS Network and Environmental Loading Data: A Case Study of Southeast Zhejiang

“…However, the GRACE monitoring results show the same variation characteristics at different locations in the study area, indicating that the CORS network can reflect local characteristics when used to monitor the vertical deformation of the terrestrial water load [18]. GRACE is limited by its spatial resolution, as it struggles to identify details at such scales as the study area [46][47][48]. Table 4 shows that the correlation coefficient of crustal vertical deformation related to terrestrial water load, as monitored by CORS and GRACE, which reaches 0.64~0.74.…”

Generative Adversarial Approach to Urban Areas NDVI Estimation Using Information Exclusively from Structural and Textural Analysis of Panchromatic Orthoimagery: A Case Study of Łódź, Poland

“…as a result of the development of plant cover or movements [63]. The NDVIartifcial estimation method proposed by us can be interwoven into ML-based digital image correlation (DIC) procedures, to find the same objects e.g., in oblique photos, multi-view oblique images [64] or pairs of images even with large differences in viewpoints [65]. We expect that having information about the third dimension of objects can be used to develop expansions of known "two-dimensional" textural features into their "three-dimensional" versions [28], which will facilitate the recognition of objects, determining their shapes and estimating their NDVIartificial on a level similar to NDVItrue.…”

DBSCAN and TD Integrated Wi-Fi Positioning Algorithm

“…In this situation, the degree of differentiation between ED, MD, and CD was considered to be consistent. TD (ED, MD, and CD) were simultaneously applied to calculate the positioning results, as shown in Equation (20).…”

“…The former methods were primarily based on electromagnetic and acoustic signals, such as ultra-wideband (UWB) [2][3][4], Bluetooth [5,6], wireless fidelity (Wi-Fi) [7][8][9], radio frequency identification (RFID) [10][11][12], ultrasonic or acoustic [13,14], geo-magnetism [15,16], pseudolite [17,18], and so on. The latter ones were based on computer vision [19,20] and inertial navigation system (INS) or pedestrian dead reckoning (PDR) [21,22]. Multiple techniques strength of RPs and map position-domain and signal-domain distances into the same metrics, reserving intrinsic connection and avoiding zero value of signal-domain distance.…”