- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Spectral–spatial classification of hyperspectral images by algebraic multigrid based multiscale information fusion
摘要： In this work, we present a novel spectral-spatial classification framework of hyperspectral images (HSIs) by integrating the techniques of algebraic multigrid (AMG), hierarchical segmentation (HSEG) and Markov random field (MRF). The proposed framework manifests two main contributions. First, an effective HSI segmentation method is developed by combining the AMG-based marker selection approach and the conventional HSEG algorithm to construct a set of unsupervised segmentation maps in multiple scales. To improve the computational efficiency, the fast Fish Markov selector (FMS) algorithm is exploited for feature selection before image segmentation. Second, an improved MRF energy function is proposed for multiscale information fusion (MIF) by considering both spatial and inter-scale contextual information. Experiments were performed using two airborne HSIs to evaluate the performance of the proposed framework in comparison with several popular classification methods. The experimental results demonstrated that the proposed framework can provide superior performance in terms of both qualitative and quantitative analysis.
关键词： hierarchical segmentation,algebraic multigrid,hyperspectral images,spectral-spatial classification,multiscale information fusion,Markov random field
更新于2025-09-23 15:22:29
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Semantic segmentation of high spatial resolution images with deep neural networks
摘要： Availability of reliable delineation of urban lands is fundamental to applications such as infrastructure management and urban planning. An accurate semantic segmentation approach can assign each pixel of remotely sensed imagery a reliable ground object class. In this paper, we propose an end-to-end deep learning architecture to perform the pixel-level understanding of high spatial resolution remote sensing images. Both local and global contextual information are considered. The local contexts are learned by the deep residual net, and the multi-scale global contexts are extracted by a pyramid pooling module. These contextual features are concatenated to predict labels for each pixel. In addition, multiple additional losses are proposed to enhance our deep learning network to optimize multi-level features from different resolution images simultaneously. Two public datasets, including Vaihingen and Potsdam datasets, are used to assess the performance of the proposed deep neural network. Comparison with the results from the published state-of-the-art algorithms demonstrates the effectiveness of our approach.
关键词： pyramid pooling,deep learning,global context information,high-resolution image segmentation,residual network
更新于2025-09-23 15:22:29
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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 - An Approach for Road Material Identification By Dual-Stage Convolutional Networks
摘要： The automatic extraction of road network information from satellite images is a meaningful and challenging task. Particularly, the analysis of road surface materials is very important during transport construction and maintenance. This paper proposes a method to extract road area and identify its corresponding materials. The approach is based on two different convolutional neural network structures. Firstly, we use encoder-decoder symmetric network structure to extract the candidate road area. Then the former outputs is processed by atrous convolutional network with very deep layers, in order to classify the covered substances through their representative spectral features. We also utilize the physical characteristics of road network to design morphology approach to enhance the completeness and formation of the road network. Experiential results on various satellite images show that the method can yields better accuracy and adaptability than other convolutional network based methods.
关键词： road region extraction,convolutional networks,image segmentation,Remote sensing,road material classification
更新于2025-09-23 15:22:29
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Multi-scale sifting for mammographic mass detection and segmentation
摘要： Breast mass detection and segmentation are challenging tasks due to the fact that breast masses vary in size and appearance. In this work, we present a simultaneous detection and segmentation scheme for mammographic lesions that is constructed in a sifting architecture. It utilizes a novel region candidate selection approach and cascaded learning techniques to achieve state-of-the-art results while handling a high class imbalance. The region candidates are generated by a novel multi-scale morphological sifting (MMS) approach, where oriented linear structuring elements are used to sieve out the mass-like objects in mammograms including stellate patterns. This method can accurately segment masses of various shapes and sizes from the background tissue. To tackle the class imbalance problem, two different ensemble learning methods are utilized: a novel self-grown cascaded random forests (CasRFs) and the random under-sampling boost (RUSBoost). The CasRFs is designed to handle class imbalance adaptively using a probability-ranking based under-sampling approach, while RUSBoost uses a random under-sampling technique. This work is evaluated on two publicly available datasets: INbreast and DDSM BCRP. On INbreast, the proposed method achieves an average sensitivity of 0.90 with 0.9 false positives per image (FPI) using CasRFs and with 1.2 FPI using RUSBoost. On DDSM BCRP, the method yields a sensitivity of 0.81 with 3.1 FPI using CasRFs and with 2.9 FPI using RUSboost. The performance of the proposed method compares favorably to the state-of-the-art methods on both datasets, especially on highly spiculated lesions.
关键词： Morphological sifting,Mammography,Breast mass detection and segmentation,Cascaded random forest,Ensemble learning
更新于2025-09-23 15:22:29
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[IEEE 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - Aristi Village, Zagorochoria, Greece (2018.6.10-2018.6.12)] 2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP) - MindCamera: Interactive Image Retrieval and Synthesis
摘要： Composing a realistic picture according to the mind is tough work for most people. It is not only a complex operation but also a creation process from nonexistence to existence. Therefore, the core of this problem is to provide rich existing materials for stitching. We present an interactive sketch-based image retrieval and synthesis system, MindCamera. Compared with existing methods, it can use images of daily scenes as the dataset and proposes a sketch-based scene image retrieval model. Furthermore, MindCamera can blend the target object in the gradient domain to avoid the visible seam, and it introduces alpha matting to realize real-time foreground object extraction and composition. Experiments verify that our retrieval model has higher precision and provides more reasonable and richer materials for users. The practical usage demonstrates that MindCamera allows the interactive creation of complex images, and its final compositing results are natural and realistic.
关键词： image fusion,image retrieval,image segmentation
更新于2025-09-23 15:22:29
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[IEEE 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Yassmine Hammamet, Tunisia (2018.3.19-2018.3.22)] 2018 15th International Multi-Conference on Systems, Signals & Devices (SSD) - Developing Modified Fuzzy C-Means Clustering Algorithm for Image Segmentation
摘要： Effective algorithm for segmenting image is important for images analysis and computer vision. Fuzzy c-means (FCM) is the mostly used methodology in image clustering. However, the results of the standard and the modified version FCM are not always satisfactory. This paper introduces a modification on spatial FCM considering the weighted fuzzy effect of neighboring pixels on the center of the cluster. So, the objective function in FCM algorithm is modified to minimize the intensity inhomogeneities by implicating the spatial information and the modified membership weighting. The advantages of the new FCM algorithm are: (a) produces homogeneous regions, (b) handles noisy spots, and (c) relatively less sensitive to noise. Experimental results on real images show that the algorithm is effective, efficient, and is relatively independent of the type of noise. Especially, it can process non-noisy and noisy images without knowing the type of the noise.
关键词： image processing,images segmentation,fuzzy c-means,image clustering
更新于2025-09-23 15:22:29
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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 - Deep Semantic Segmentation of Aerial Imagery Based on Multi-Modal Data
摘要： In this paper, we focus on the use of multi-modal data to achieve a semantic segmentation of aerial imagery. Thereby, the multi-modal data is composed of a true orthophoto, the Digital Surface Model (DSM) and further representations derived from these. Taking data of different modalities separately and in combination as input to a Residual Shuffling Convolutional Neural Network (RSCNN), we analyze their value for the classification task given with a benchmark dataset. The derived results reveal an improvement if different types of geometric features extracted from the DSM are used in addition to the true orthophoto.
关键词： multi-modal data,aerial imagery,Shuffling-CNN,deep learning,Semantic segmentation
更新于2025-09-23 15:22:29
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[IEEE 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Stuttgart, Germany (2018.11.20-2018.11.22)] 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) - Plant Cell Segmentation with Adaptive Thresholding
摘要： There are many approaches to plant cell segmentation, but there is no established method to segment plant cell for a portable, USB-powered optical microscope. Existing methods leverage on sophisticated microscope such as confocal laser scanning microscope or electron microscope may not be applicable for a portable setup. Staining of plant cell specimens, in order to improve visibility of boundaries, might affect the plant cell and also requires additional preparation work prior to acquisition which could be infeasible for on-the-fly applications. Conventional plant cell segmentation using watershed transform often results in over-segmentation, hindering the effectiveness of the method. Hence, we propose a thresholding method based on Otsu's method, to retain majority of the image information to improve the success rate of the cell segmentation. The method is implemented on a leaf cellular image acquired from freshwater weed elodea. The region identified by the improved watershed transform can be further processed to locate the centroids of the cells. We experimented our method on images filled fully with plant cells and filled partially with plant cells. We also studied the impact of boundary definition of the image to our method.
关键词： cell segmentation,watershed transform,image processing
更新于2025-09-23 15:22:29
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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 - Road Segmentation of UAV RS Image Using Adversarial Network with Multi-Scale Context Aggregation
摘要： Semantic segmentation using adversarial networks has been approved to produce the better artificial results in image processing fields. Focused on current Deep Convolutional Neural Networks (DCNNs), since the convolutional kernel size has been fixed in every convolutional operation, the small objects would be ignored with large convolutional kernel size, and the segmentation result of large objects is not continuous with small convolutional kernel size. The paper developed a semantic segmentation model that combined the adversarial networks with multi-scale context aggregation. Further, the model was applied to road segmentation of UAV RS images. The experimental results of this semantic segmentation model with multi-scale context aggregation has a better performance for road segmentation and fit well with the reference standard results. It can improve the road segmentation accuracy obviously in the situation where there are other small regions whose shape or color is similar to road regions in UAV RS images.
关键词： Road Segmentation,Adversarial Network,UAV image,Image processing,multi-scale context aggregation
更新于2025-09-23 15:22:29
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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 - Efficient Registration for InSAR Large-Scale Image Using Quadtree Segmentation
摘要： In this paper, an efficient registration algorithm for InSAR large scale image via discrete Fourier transform (DFT) model of the maximum correlation and image quadtree segmentation is proposed. In the scheme, a DFT-based sub-pixel registration model of InSAR complex images is constructed. Then, efficient sub-pixel registration for InSAR large-scale image is achieved by joint quadtree segmentation and DFT-based interpolation registration. Simulation and experimental results are presented to confirm the effectiveness of the proposed algorithm. The results demonstrate that the algorithm not only can achieve sub-pixel registration of InSAR large-scale image, but also has higher computational efficiency compared with the traditional maximum correlation registration method.
关键词： quadtree segmentation,maximum correlation,InSAR,large-scale image,image registration
更新于2025-09-23 15:22:29