scholarly journals Region-by-Region Registration Combining Feature-Based and Optical Flow Methods for Remote Sensing Images

2021 ◽  
Vol 13 (8) ◽  
pp. 1475
Author(s):  
Ruitao Feng ◽  
Qingyun Du ◽  
Huanfeng Shen ◽  
Xinghua Li
Keyword(s):  
Remote Sensing ◽  
Optical Flow ◽  
Complex Terrain ◽  
Remote Sensing Images ◽  
Displacement Fields ◽  
Registration Accuracy ◽  
Flow Estimation ◽  
Feature Based ◽  
Weighted Taylor Expansion ◽  
Topographic Relief

While geometric registration has been studied in remote sensing community for many decades, successful cases are rare, which register images allowing for local inconsistency deformation caused by topographic relief. Toward this end, a region-by-region registration combining the feature-based and optical flow methods is proposed. The proposed framework establishes on the calculation of pixel-wise displacement and mosaic of displacement fields. Concretely, the initial displacement fields for a pair of images are calculated by the block-weighted projective model and Brox optical flow estimation, respectively in the flat- and complex-terrain regions. The abnormal displacements resulting from the sensitivity of optical flow in the land use or land cover changes, are adaptively detected and corrected by the weighted Taylor expansion. Subsequently, the displacement fields are mosaicked seamlessly for subsequent steps. Experimental results show that the proposed method outperforms comparative algorithms, achieving the highest registration accuracy qualitatively and quantitatively.

scholarly journals MOUNTAINOUS REMOTE SENSING IMAGES REGISTRATION BASED ON IMPROVED OPTICAL FLOW ESTIMATION

2019 ◽  
Vol IV-2/W5 ◽  
pp. 479-484 ◽  
Author(s):  
R. Feng ◽  
X. Li ◽  
H. Shen
Keyword(s):  
Remote Sensing ◽  
Optical Flow ◽  
Remote Sensing Image ◽  
Transformation Model ◽  
Geometric Distortion ◽  
Estimation Methods ◽  
Remote Sensing Images ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Feature Based

<p><strong>Abstract.</strong> Mountainous remote sensing images registration is more complicated than in other areas as geometric distortion caused by topographic relief, which could not be precisely achieved via constructing local mapping functions in the feature-based framework. Optical flow algorithm estimating motion of consecutive frames in computer vision pixel by pixel is introduced for mountainous remote sensing images registration. However, it is sensitive to land cover changes that are inevitable for remote sensing image, resulting in incorrect displacement. To address this problem, we proposed an improved optical flow estimation concentrated on post-processing, namely displacement modification. First of all, the Laplacian of Gaussian (LoG) algorithm is employed to detect the abnormal value in color map of displacement. Then, the abnormal displacement is recalculated in the interpolation surface constructed by the rest accurate displacements. Following the successful coordinate transformation and resampling, the registration outcome is generated. Experiments demonstrated that the proposed method is insensitive in changeable region of mountainous remote sensing image, generating precise registration, outperforming the other local transformation model estimation methods in both visual judgment and quantitative evaluation.</p>

scholarly journals Performance Evaluation of Single-Label and Multi-Label Remote Sensing Image Retrieval Using a Dense Labeling Dataset

2018 ◽  
Vol 10 (6) ◽  
pp. 964 ◽  
Author(s):  
Zhenfeng Shao ◽  
Ke Yang ◽  
Weixun Zhou
Keyword(s):  
Remote Sensing ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Image Retrieval ◽  
Semantic Segmentation ◽  
Semantic Content ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Benchmark Datasets ◽  
Feature Based

Benchmark datasets are essential for developing and evaluating remote sensing image retrieval (RSIR) approaches. However, most of the existing datasets are single-labeled, with each image in these datasets being annotated by a single label representing the most significant semantic content of the image. This is sufficient for simple problems, such as distinguishing between a building and a beach, but multiple labels and sometimes even dense (pixel) labels are required for more complex problems, such as RSIR and semantic segmentation.We therefore extended the existing multi-labeled dataset collected for multi-label RSIR and presented a dense labeling remote sensing dataset termed "DLRSD". DLRSD contained a total of 17 classes, and the pixels of each image were assigned with 17 pre-defined labels. We used DLRSD to evaluate the performance of RSIR methods ranging from traditional handcrafted feature-based methods to deep learning-based ones. More specifically, we evaluated the performances of RSIR methods from both single-label and multi-label perspectives. These results demonstrated the advantages of multiple labels over single labels for interpreting complex remote sensing images. DLRSD provided the literature a benchmark for RSIR and other pixel-based problems such as semantic segmentation.

scholarly journals An Image Registration Method Using Deep Residual Network Features for Multisource High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3425
Author(s):  
Xin Zhao ◽  
Hui Li ◽  
Ping Wang ◽  
Linhai Jing
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Registration ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Residual Network ◽  
Scale Invariant ◽  
Registration Method ◽  
Sensing Applications ◽  
Feature Based

Accurate registration for multisource high-resolution remote sensing images is an essential step for various remote sensing applications. Due to the complexity of the feature and texture information of high-resolution remote sensing images, especially for images covering earthquake disasters, feature-based image registration methods need a more helpful feature descriptor to improve the accuracy. However, traditional image registration methods that only use local features at low levels have difficulty representing the features of the matching points. To improve the accuracy of matching features for multisource high-resolution remote sensing images, an image registration method based on a deep residual network (ResNet) and scale-invariant feature transform (SIFT) was proposed. It used the fusion of SIFT features and ResNet features on the basis of the traditional algorithm to achieve image registration. The proposed method consists of two parts: model construction and training and image registration using a combination of SIFT and ResNet34 features. First, a registration sample set constructed from high-resolution satellite remote sensing images was used to fine-tune the network to obtain the ResNet model. Then, for the image to be registered, the Shi_Tomas algorithm and the combination of SIFT and ResNet features were used for feature extraction to complete the image registration. Considering the difference in image sizes and scenes, five pairs of images were used to conduct experiments to verify the effectiveness of the method in different practical applications. The experimental results showed that the proposed method can achieve higher accuracies and more tie points than traditional feature-based methods.

scholarly journals UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features

2021 ◽  
Vol 13 (18) ◽  
pp. 3605
Author(s):  
Xin Luo ◽  
Guangling Lai ◽  
Xiao Wang ◽  
Yuwei Jin ◽  
Xixu He ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Rapid Development ◽  
Image Features ◽  
Automatic Registration ◽  
Remote Sensing Images ◽  
Registration Accuracy ◽  
Registration Method ◽  
Uav Images ◽  
High Level

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.

scholarly journals Feedback Unilateral Grid-Based Clustering Feature Matching for Remote Sensing Image Registration

2019 ◽  
Vol 11 (12) ◽  
pp. 1418
Author(s):  
Zhaohui Zheng ◽  
Hong Zheng ◽  
Yong Ma ◽  
Fan Fan ◽  
Jianping Ju ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Image Matching ◽  
Feature Matching ◽  
Remote Sensing Image ◽  
Registration Accuracy ◽  
Computation Efficiency ◽  
Feature Based ◽  
Local Grid ◽  
Grid Based

In feature-based image matching, implementing a fast and ultra-robust feature matching technique is a challenging task. To solve the problems that the traditional feature matching algorithm suffers from, such as long running time and low registration accuracy, an algorithm called feedback unilateral grid-based clustering (FUGC) is presented which is able to improve computation efficiency, accuracy and robustness of feature-based image matching while applying it to remote sensing image registration. First, the image is divided by using unilateral grids and then fast coarse screening of the initial matching feature points through local grid clustering is performed to eliminate a great deal of mismatches in milliseconds. To ensure that true matches are not erroneously screened, a local linear transformation is designed to take feedback verification further, thereby performing fine screening between true matching points deleted erroneously and undeleted false positives in and around this area. This strategy can not only extract high-accuracy matching from coarse baseline matching with low accuracy, but also preserves the true matching points to the greatest extent. The experimental results demonstrate the strong robustness of the FUGC algorithm on various real-world remote sensing images. The FUGC algorithm outperforms current state-of-the-art methods and meets the real-time requirement.

scholarly journals Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

2021 ◽  
Vol 13 (24) ◽  
pp. 5128
Author(s):  
Xinyue Zhang ◽  
Chengcai Leng ◽  
Yameng Hong ◽  
Zhao Pei ◽  
Irene Cheng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Registration ◽  
Remote Sensing Image ◽  
Comprehensive Analysis ◽  
Remote Sensing Images ◽  
Research Focus ◽  
Theoretical Frameworks ◽  
Nonlinear Radiation ◽  
Multi Temporal ◽  
Feature Based

With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.

scholarly journals FAST AND ROBUST REGISTRATION OF MULTIMODAL REMOTE SENSING IMAGES VIA DENSE ORIENTATED GRADIENT FEATURE

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1009-1015 ◽  
Author(s):  
Y. Ye
Keyword(s):  
Remote Sensing ◽  
Computational Efficiency ◽  
Template Matching ◽  
Remote Sensing Data ◽  
Experimental Results ◽  
Feature Descriptor ◽  
Remote Sensing Images ◽  
Registration Accuracy ◽  
Registration System ◽  
Similarity Metric

This paper presents a fast and robust method for the registration of multimodal remote sensing data (e.g., optical, LiDAR, SAR and map). The proposed method is based on the hypothesis that structural similarity between images is preserved across different modalities. In the definition of the proposed method, we first develop a pixel-wise feature descriptor named Dense Orientated Gradient Histogram (DOGH), which can be computed effectively at every pixel and is robust to non-linear intensity differences between images. Then a fast similarity metric based on DOGH is built in frequency domain using the Fast Fourier Transform (FFT) technique. Finally, a template matching scheme is applied to detect tie points between images. Experimental results on different types of multimodal remote sensing images show that the proposed similarity metric has the superior matching performance and computational efficiency than the state-of-the-art methods. Moreover, based on the proposed similarity metric, we also design a fast and robust automatic registration system for multimodal images. This system has been evaluated using a pair of very large SAR and optical images (more than 20000&amp;thinsp;×&amp;thinsp;20000 pixels). Experimental results show that our system outperforms the two popular commercial software systems (i.e. ENVI and ERDAS) in both registration accuracy and computational efficiency.

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