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- 摘要
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Novel Fine Registration Technique for Very High Resolution Remote Sensing Images
摘要： This paper presents a novel registration noise (RN) estimation technique for fine registration of very high resolution (VHR) images. This is accomplished by using a two-step strategy to estimate and mitigate residual local misalignments in standardly registered VHR images. The first step takes advantages of the superpixel segmentation and frequency filtering to generate sparse superpixels as the basic objects for RN estimation. Then local rectification is employed for fine registration of the input image under the aid of RN information. More factors are taken into consideration in order to enhance the RN estimation performance. The proposed approach is designed in a fine registration strategy, which can effectively improve the pre-registration result. The experimental results obtained with real datasets confirm the effectiveness of the proposed method.
关键词： local rectification,superpixel segmentation,Fine registration,VHR image,sparse representation
更新于2025-09-23 15:22:29
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Rectangular-Normalized Superpixel Entropy Index for Image Quality Assessment
摘要： Image quality assessment (IQA) is a fundamental problem in image processing that aims to measure the objective quality of a distorted image. Traditional full-reference (FR) IQA methods use fixed-size sliding windows to obtain structure information but ignore the variable spatial configuration information. In order to better measure the multi-scale objects, we propose a novel IQA method, named RSEI, based on the perspective of the variable receptive field and information entropy. First, we find that consistence relationship exists between the information fidelity and human visual of individuals. Thus, we reproduce the human visual system (HVS) to semantically divide the image into multiple patches via rectangular-normalized superpixel segmentation. Then the weights of each image patches are adaptively calculated via their information volume. We verify the effectiveness of RSEI by applying it to data from the TID2008 database and denoise algorithms. Experiments show that RSEI outperforms some state-of-the-art IQA algorithms, including visual information fidelity (VIF) and weighted average deep image quality measure (WaDIQaM).
关键词： image quality assessment,superpixel segmentation,mutual information
更新于2025-09-23 15:22:29
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KNN-Based Representation of Superpixels for Hyperspectral Image Classification
摘要： Superpixel segmentation has been demonstrated to be a powerful tool in hyperspectral image (HSI) classification. Each superpixel region can be regarded as a homogeneous region, which is composed of a series of spatial neighboring pixels. However, a superpixel region may contain the pixels from different classes. To further explore the optimal representations of superpixels, a new framework based on two k selection rules is proposed to find the most representative training and test samples. The proposed method consists of the following four steps: first, a superpixel segmentation algorithm is performed on the HSI to cluster the pixels with similar spectral features into the same superpixel. Then, a domain transform recursive filtering is used to extract the spectral–spatial features of the HSI. Next, the k nearest neighbor (KNN) method is utilized to select k1 representative training samples and k2 test pixels for each superpixel, which can effectively overcome the within-class variations and between-class interference, respectively. Finally, the class label of superpixels can be determined by measuring the averaged distances among the selected training and test samples. Experiments conducted on four real hyperspectral datasets show that the proposed method provides competitive classification performances with respect to several recently proposed spectral–spatial classification methods.
关键词： superpixel segmentation,hyperspectral image classification,k nearest neighbor (KNN),Domain transform recursive filtering (RF)
更新于2025-09-23 15:21:01
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Classification Method for Polsar Images using SLIC Superpixel Segmentation and Deep Convolution Neural Network
摘要： Deep convolution neural networks (DCNN) have been successfully introduced in the field of Polarimetric SAR image classification. However, the commonly used DCNN will classify each pixel in the image and neglect the fact that neighboring pixels may have similar intensity. Besides, the fixed size input in DCNN cannot be well adopted in remote sensing image which includes a great deal of different-scale information. Thus, superpixel segmentation (SS) and the input pyramid are introduced in this paper to improve the performance of DCNN. The former will guide the DCNN to classify superpixel instead of single pixel and the latter will include different-scale information around the pixel. Experiments carried out on two scenes of ALOS-2 PALSAR-2 POLSAR images demonstrate that the introduced technic can help DCNN achieve good accuracy and smooth boundary adherence with highly efficiency.
关键词： superpixel segmentation,convolution neural network,Polarimetric synthetic aperture radar
更新于2025-09-10 09:29:36
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Hyperspectral image denoising via minimizing the partial sum of singular values and superpixel segmentation
摘要： Hyperspectral images (HSIs) are often corrupted by noise during the acquisition process, thus degrading the HSI’s discriminative capability significantly. Therefore, HSI denoising becomes an essential preprocess step before application. This paper proposes a new HSI denoising approach connecting Partial Sum of Singular Values (PSSV) and superpixels segmentation named as SS-PSSV, which can remove the noise effectively. Based on the fact that there is a high correlation between different bands of the same signal, it is easy to know the property of low rank between distinct bands. To this end, PSSV is utilized, and in order to better tap the low-rank attribute of pixels, we introduce the superpixels segmentation method, which allows pixels in HSI with high similarity to be grouped in the same sub-block as much as possible. Extensive experiments display that the proposed algorithm outperforms the state-of-the-art.
关键词： Superpixel segmentation,Hyperspectral images,Denoising,PSSV
更新于2025-09-04 15:30:14
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Online weld pool contour extraction and seam width prediction based on mixing spectral vision
摘要： In this paper, based on gas–metal–arc welding (GMAW), we used a passive vision sensing system and proposed a double-path imaging method to capture weld pool images, and empirically and theoretically demonstrated the optimal bands. According to the mixed spectra of self-emitted radiation of the weld pool and the arc spectra, we selected 660-nm narrowband and 850-nm long-pass as the system’s working bands. Two cameras with 660-nm narrowband filter and 850-nm long-pass filter were used to capture weld pool images at the background level through a synchronous acquisition equation and weld pool images with high signal-to-noise ratio were obtained. After image registration, we used Gradient and Gray-based Neighbor Superpixel Merging (GNSM) method to extract the contour of weld pool image. Comparing with other algorithms, the proposed algorithm can obtain an effective and accurate contour of the weld pool image. Then we proposed an online seam width prediction method before seam formation which is based on the contour of the weld pool image. We used Gaussian distribution to fit the pixel width of the contour and the corresponding seam width measured by three-dimensional reconstruction. By comparatively analyzing the fitting deviation and the actual measurement results, we concluded that the deviation of weld seam width prediction was within 0.20 mm.
关键词： Three-dimensional reconstruction,Superpixel segmentation,Seam width prediction,Vision sensing system,Double-band imaging,Contour of weld pool
更新于2025-09-04 15:30:14
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[IEEE 2018 25th IEEE International Conference on Image Processing (ICIP) - Athens, Greece (2018.10.7-2018.10.10)] 2018 25th IEEE International Conference on Image Processing (ICIP) - Improving Time-of-Flight Sensor for Specular Surfaces with Shape from Polarization
摘要： Time-of-Flight (ToF) sensors can obtain depth values for diffuse objects. However, the essential problem is that these sensors cannot receive active light from specular surfaces due to specular reflections. In this paper, we propose a new depth reconstruction framework for specular objects that combines ToF cues and Shape from Polarization (SfP). To overcome the ill-posedness of SfP with a single view, we integrate superpixel segmentation with planarity constraints for every superpixel. Experimental results demonstrate the effectiveness of the depth reconstruction algorithm for both controlled environment data and real vehicle data in a parking area.
关键词： Shape from Polarization,specular surface,Time-of-Flight sensor,single view reconstruction,superpixel segmentation
更新于2025-09-04 15:30:14
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[Lecture Notes in Computer Science] Algorithms and Architectures for Parallel Processing Volume 11335 (18th International Conference, ICA3PP 2018, Guangzhou, China, November 15-17, 2018, Proceedings, Part II) || SMIM: Superpixel Mutual Information Measurement for Image Quality Assessment
摘要： The image quality assessment (IQA) is a fundamental problem in signal processing that aims to measure the objective quality of an image by designing a mathematical model. Most full-reference (FR) IQA methods use ?xed sliding windows to obtain structure information but ignore the variable spatial con?guration information. In this paper, we propose a novel full-reference IQA method, named “superpixel normalized mutual information (SMIM)” based on the perspective of variable receptive ?eld and information entropy. First, we ?nd that consistence relationship exists between the information ?delity and human visual of individuals. Thus, we reproduce the human visual system (HVS) to semantically divide the image into multiple patches via superpixel segmentation. Then the weights of each image patches are adaptively calculated via its information volume. We veri?ed the e?ectiveness of SMIM by applying it to data from the TID2008 database and data generated using some real application scenarios. Experiments show that SMIM outperforms some state-of-the-art FR IQA algorithms, including visual information ?delity (VIF).
关键词： Superpixel segmentation,Mutual information,Image quality assessment
更新于2025-09-04 15:30:14
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Haze Removal of Single Remote Sensing Image by Combining Dark Channel Prior with Superpixel
摘要： Dehazing is important in remote sensing image restorations to enhance the acquired low quality image for interpretation. However, traditional methods have some limitations for dehazing of remote sensing images due to its color distortion and noise. In this paper, we propose an improved method combining superpixel segmentation with luminance information of a haze image to estimate the atmospheric light instead of dark channel prior. Using this method with the haze imaging model, we can directly estimate the thickness of the haze and restore a high quality haze-free image. Experimental results on a variety of remote sensing haze images demonstrate our approach can achieve better image quality when compared with well-known He's [1] method for remote sensing images.
关键词： atmospheric scattering model,Haze removal,superpixel segmentation
更新于2025-09-04 15:30:14