HOMPC: A Local Feature Descriptor Based on the Combination of Magnitude and Phase Congruency Information for Multi-Sensor Remote Sensing Images

Fu, Zhiyao; Qin, Qianqing; Luo, Bin; Wu, Chun

doi:10.3390/rs10081234

Cited by 21 publications

(5 citation statements)

References 43 publications

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“…The system accuracy is computed at equal error rate (EER) value which reflects the maximum system performance when the false acceptance rate (FAR) equals to the false rejection rate (FRR) as explained in 15. The error distance between x ̅ and y ̅ with m+1 length can be described in (16)(17)(18) [30][31][32][33][34].…”

Section: Classificationmentioning

confidence: 99%

Towards better performance: phase congruency based face recognition

Hamd

Rasool

2020

TELKOMNIKA

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Phase congruency is an edge detector and measurement of the significant feature in the image. It is a robust method against contrast and illumination variation. In this paper, two novel techniques are introduced for developing a low-cost human identification system based on face recognition. Firstly, the valuable phase congruency features, the gradient-edges and their associated angles are utilized separately for classifying 130 subjects taken from three face databases with the motivation of eliminating the feature extraction phase. By doing this, the complexity can be significantly reduced. Secondly, the training process is modified when a new technique, called averaging-vectors is developed to accelerate the training process and minimizes the matching time to the lowest value. However, for more comparison and accurate evaluation, three competitive classifiers: Euclidean distance (ED), cosine distance (CD), and Manhattan distance (MD) are considered in this work. The system performance is very competitive and acceptable, where the experimental results show promising recognition rates with a reasonable matching time.

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Section: Classificationmentioning

confidence: 99%

Towards better performance: phase congruency based face recognition

Hamd

Rasool

2020

TELKOMNIKA

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“…Later on, many researchers employ phase congruency (PC) to construct feature descriptor for image registration task because it is invariant to the intensity variation of images and consistent with the human visual system [26], [27]. For example, Fu et al [28] developed a local feature descriptor by combining the oriented PC information and oriented magnitude binary map to capture the feature properties of local regions. Xiang et al [29] proposed an SAR-PC model with ratio-based edge detectors to extract spatial properties of optical and SAR images.…”

Section: Introductionmentioning

confidence: 99%

Robust Registration of Multimodal Remote Sensing Images With Spectrum Congruency

Huang

Yang

Chai

2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Among the existing registration methods, most feature descriptors are designed with image intensity, gradient information and phase congruency (PC). However, both intensity and gradient are sensitive to image illumination changes, complex intensity differences, noise, etc. Despite the fact that PC is invariant to image illumination and contrast, it does not perform well when images are corrupted with noise and nonlinear radiation distortions. In this article, we propose a novel feature called spectrum congruency (SC), which is robust to noise and variations of image illumination and intensity. SC focuses on exploiting the correlation of the multiscale patches based on their local energy and measures the congruency of the energy distribution in a data-driven transform domain. To demonstrate the superiority of SC, we apply it to multimodal image registration. We construct a histogram-based feature descriptor based on SC, termed as HOSC. Then the HOSC descriptor is integrated with two similarity metrics for multimodal remote sensing image registration. Extensive experimental results on both real and noisy image pairs show that the proposed method presents superior registration accuracy and excellent performance in resisting the nonlinear distortion and noise.

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“…Fan et al [27] presented the phase congruency structural descriptor (PCSD) by grouping PC maps to match SAR and optical images. Fu et al [28] developed a dense descriptor named histograms of oriented magnitude and phase congruency (HOMPC) based on oriented PC maps for multi-sensor image matching. Li et al [29] proposed a multimodal image matching method named radiation-invariant feature transform (RIFT).…”

Section: Introductionmentioning

confidence: 99%

Self-Similarity Features for Multimodal Remote Sensing Image Matching

Xiong¹,

Gao²,

Xu³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Multimodal remote sensing image matching is a challenging task because of the existence of significant radiometric differences. To address the problem, we develop a novel multimodal remote sensing image matching method based on selfsimilarity features. The offset mean filtering method is proposed first to calculate the self-similarity features fast based on the symmetry of the self-similarity. The self-similarity features are presented through a multi-channel self-similarity map (SSM) and a corresponding multi-channel symmetric SSM. On this basis, we develop the image matching method, including a feature detector named improved maximal self-dissimilarities (IMSD) and a feature descriptor named oriented self-similarity (OSS). The IMSD detector is designed by introducing the two multi-channel SSMs into the maximal self-dissimilarities (MSD) detector for feature point detection. The OSS descriptor is proposed based on the orientations of the self-similarities extracted from the multichannel SSMs. We conduct experiments with a variety of optical, synthetic aperture radar, and light detection and ranging data. Our results demonstrate the advantages of our proposed IMSD detector and OSS descriptor in comparison with state-of-the-art detectors and descriptors, respectively. The image registration results further confirm the effectiveness of the proposed method.

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HOMPC: A Local Feature Descriptor Based on the Combination of Magnitude and Phase Congruency Information for Multi-Sensor Remote Sensing Images

Cited by 21 publications

References 43 publications

Towards better performance: phase congruency based face recognition

Towards better performance: phase congruency based face recognition

Robust Registration of Multimodal Remote Sensing Images With Spectrum Congruency

Self-Similarity Features for Multimodal Remote Sensing Image Matching

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