Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint

Chen, Min; Habib, Ayman; He, Hangen; Zhu, Qing; Zhang, Wei

doi:10.3390/rs9090882

Cited by 30 publications

(10 citation statements)

References 32 publications

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“…On the other hand, learning-based approaches attempt to predict whether a given pixel is a road or not, according to the context around the target pixel [9,10,[36][37][38][39][40]. The extraction is similar to the task of salient objects extraction or segmentation [41][42][43][44][45][46][47]. Liu et al, 2017 [46] exploited multiscale and multilevel information to extract edges and boundaries, which is also adopted by us as the multilevel discriminator.…”

Section: Related Workmentioning

confidence: 99%

Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks

et al. 2019

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Road network extraction from remote sensing images has played an important role in various areas. However, due to complex imaging conditions and terrain factors, such as occlusion and shades, it is very challenging to extract road networks with complete topology structures. In this paper, we propose a learning-based road network extraction framework via a Multi-supervised Generative Adversarial Network (MsGAN), which is jointly trained by the spectral and topology features of the road network. Such a design makes the network capable of learning how to “guess” the aberrant road cases, which is caused by occlusion and shadow, based on the relationship between the road region and centerline; thus, it is able to provide a road network with integrated topology. Additionally, we also present a sample quality measurement to efficiently generate a large number of training samples with a little human interaction. Through the experiments on images from various satellites and the comprehensive comparisons to state-of-the-art approaches on the public datasets, it is demonstrated that the proposed method is able to provide high-quality results, especially for the completeness of the road network.

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Section: Related Workmentioning

confidence: 99%

Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks

et al. 2019

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“…For example, a novel structural descriptor, the PCSD (phase congruency structural descriptor), is constructed to accurately describe the attributes of extracted points [45]. For this purpose, descriptor similarity and geometrical relationship are combined to constrain the matching process in order to significantly increase the number of correct matches [46]. The coupled optical and SAR patches for different sources are then automatically extracted by the learning features in the pretrained network Pseudo-Siamese CNN [47] and generative matching network (GMN) [48], respectively.…”

Section: Training Algorithms In B_cnn and Trans_cnnmentioning

confidence: 99%

Transfer and Association: A Novel Detection Method for Targets without Prior Homogeneous Samples

Zhou

Zhang

2019

Remote Sensing

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A primary problem faced during previous research was the gap in limited and unbalanced quantity of prior samples between computer classification tasks and targeted remote sensing applications. This paper presents the fusion method to overcome this limitation. It offers a novel method based on knowledge transfer and feature association, a strong combination of transfer learning and data fusion. The former reuses layers trained on complete data sets to compute a mid-level representation of the specific target. The latter brings additional information from heterogeneous sources to enrich the features in the target domain. Firstly, a basic convolutional neural network (B_CNN) is pretrained on to the CIFAR-10 dataset to produce a stable model responsible for general feature extraction from multiple inputs. Secondly, a transfer CNN (Trans_CNN) with fine-tuned and transferred parameters is constraint-trained to fit and switch between differing tasks. Meanwhile, the feature association (FA) frames a new feature space to achieve integration between training and testing samples from different sensors. Finally, on-line detection can be completed based on Trans_CNN to explore a state-of-the-art method to overcome the inadequate sample problems in real remote sensing applications rather than produce an unrolled version of training methods or structural improvement in CNN. Experimental results show that target detection rates without homogeneous prior samples can reach 85%. Under these conditions, the traditional CNN model is invalid.

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“…Different configurations of pixels may lead to different edges, which can be detected by computing the gradient of each pixel [46,47]. One of the commonly used tools to determine the gradient of a pixel is the Sobel operator [48], which consists of two 3 × 3 kernels ( Figure 5) used to convolve an image (denote the convolved images as G x and G y , respectively).…”

Section: Entropy Based On the Sobel Gradient Of A Pixelmentioning

confidence: 99%

Thermodynamics-Based Evaluation of Various Improved Shannon Entropies for Configurational Information of Gray-Level Images

Gao

Zhang

2018

Entropy

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Abstract:The quality of an image affects its utility and image quality assessment has been a hot research topic for many years. One widely used measure for image quality assessment is Shannon entropy, which has a well-established information-theoretic basis. The value of this entropy can be interpreted as the amount of information. However, Shannon entropy is badly adapted to information measurement in images, because it captures only the compositional information of an image and ignores the configurational aspect. To fix this problem, improved Shannon entropies have been actively proposed in the last few decades, but a thorough evaluation of their performance is still lacking. This study presents such an evaluation, involving twenty-three improved Shannon entropies based on various tools such as gray-level co-occurrence matrices and local binary patterns. For the evaluation, we proposed: (a) a strategy to generate testing (gray-level) images by simulating the mixing of ideal gases in thermodynamics; (b) three criteria consisting of validity, reliability, and ability to capture configurational disorder; and (c) three measures to assess the fulfillment of each criterion. The evaluation results show only the improved entropies based on local binary patterns are invalid for use in quantifying the configurational information of images, and the best variant of Shannon entropy in terms of reliability and ability is the one based on the average distance between same/different-value pixels. These conclusions are theoretically important in setting a direction for the future research on improving entropy and are practically useful in selecting an effective entropy for various image processing applications.

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Robust Feature Matching Method for SAR and Optical Images by Using Gaussian-Gamma-Shaped Bi-Windows-Based Descriptor and Geometric Constraint

Cited by 30 publications

References 32 publications

Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks

Topology-Aware Road Network Extraction via Multi-Supervised Generative Adversarial Networks

Transfer and Association: A Novel Detection Method for Targets without Prior Homogeneous Samples

Thermodynamics-Based Evaluation of Various Improved Shannon Entropies for Configurational Information of Gray-Level Images

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