- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Automatic Image Segmentation with Superpixels and Image-level Labels
摘要： Automatically and ideally segmenting the semantic region of each object in an image will greatly improve the precision and efficiency of subsequent image processing. We propose an automatic image segmentation algorithm based on superpixels and image-level labels. The proposed algorithm consists of three stages. At the stage of superpixel segmentation, we adaptively generate the initial number of superpixels using minimum spatial distance and the total number of pixels in the image. At the stage of superpixel merging, we define small superpixels and directly merge the most similar superpixel pairs without considering the adjacency, until the number of superpixels equals the number of groupings contained in image-level labels. Furthermore, we add a stage of reclassification of disconnected regions after superpixel merging to enhance the connectivity of segmented regions. On the widely-used Microsoft Research Cambridge data set and Berkeley segmentation data set, we demonstrate that our algorithm can produce high-precision image segmentation results compared to the state-of-the-art algorithms.
关键词： superpixels,image-level labels,Image segmentation,disconnected regions
更新于2025-09-23 15:23:52
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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 - Hyperspectral Image Super-Resolution via Local Low-Rank and Sparse Representations
摘要： Remotely sensed hyperspectral images (HSIs) usually have high spectral resolution but low spatial resolution. A way to increase the spatial resolution of HSIs is to solve a fusion inverse problem, which fuses a low spatial resolution HSI (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) of the same scene. In this paper, we propose a novel HSI super-resolution approach (called LRSR), which formulates the fusion problem as the estimation of a spectral dictionary from the LR-HSI and the respective regression coefficients from both images. The regression coefficients are estimated by formulating a variational regularization problem which promotes local (in the spatial sense) low-rank and sparse regression coefficients. The local regions, where the spectral vectors are low-rank, are estimated by segmenting the HR-MSI. The formulated convex optimization is solved with SALSA. Experiments provide evidence that LRSR is competitive with respect to the state-of-the-art methods.
关键词： Hyperspectral image super-resolution,low rank,superpixels
更新于2025-09-23 15:22:29
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A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing
摘要： Sparse hyperspectral unmixing from large spectral libraries has been considered to circumvent the limitations of endmember extraction algorithms in many applications. This strategy often leads to ill-posed inverse problems, which can greatly benefit from spatial regularization strategies. However, existing spatial regularization strategies lead to large-scale non-smooth optimization problems. Thus, efficiently introducing spatial context in the unmixing problem remains a challenge and a necessity for many real world applications. In this letter, a novel multiscale spatial regularization approach for sparse unmixing is proposed. The method uses a signal-adaptive spatial multiscale decomposition based on segmentation and oversegmentation algorithms to decompose the unmixing problem into two simpler problems: one in an approximation image domain and another in the original domain. Simulation results using both synthetic and real data indicate that the proposed method outperforms the state-of-the-art total variation-based algorithms with a computation time comparable to that of their unregularized counterparts.
关键词： spatial regularization,superpixels,Hyperspectral data,sparse unmixing,multiscale
更新于2025-09-23 15:21:01
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Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks
摘要： Remote sensing is important to precision agriculture and the spatial resolution provided by Unmanned Aerial Vehicles (UAVs) is revolutionizing precision agriculture workflows for measurement crop condition and yields over the growing season, for identifying and monitoring weeds and other applications. Monitoring of individual trees for growth, fruit production and pest and disease occurrence remains a high research priority and the delineation of each tree using automated means as an alternative to manual delineation would be useful for long-term farm management. In this paper, we detected citrus and other crop trees from UAV images using a simple convolutional neural network (CNN) algorithm, followed by a classification refinement using superpixels derived from a Simple Linear Iterative Clustering (SLIC) algorithm. The workflow performed well in a relatively complex agricultural environment (multiple targets, multiple size trees and ages, etc.) achieving high accuracy (overall accuracy = 96.24%, Precision (positive predictive value) = 94.59%, Recall (sensitivity) = 97.94%). To our knowledge, this is the first time a CNN has been used with UAV multi-spectral imagery to focus on citrus trees. More of these individual cases are needed to develop standard automated workflows to help agricultural managers better incorporate large volumes of high resolution UAV imagery into agricultural management operations.
关键词： UAS,tree identification,citrus,precision agriculture,CNN,feature extraction,deep learning,superpixels
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 - Block-Based and Segmentation-Based Approaches for Component Substitution based Hyperspectral Pansharpening
摘要： Pansharpening is the fusion of panchromatic (PAN) image and multispectral (MS) or hyperspectral (HS) images and provides high spatial and high spectral resolution MS or HS images. Pansharpening mainly extracs the high frequency details from the PAN image, and then injects these details to the MS or HS image. This detail injection procedure can be performed in a variety of ways: using global, block-based or clustering-based techniques. In this paper, block-based and clustering-based approaches are utilized for standart component substitution pansharpening approaches, namely Intensity Hue Saturation (IHS), Brovey Transform (BT), Gram Schmidt (GS) orthagonalization procedure and Principal Component Analysis (PCA) techniques. Both non-overlapping and overlapping blocks are considered, along with various segmentation approaches such as k-means, Iterative Self Organizing Data Analysis Techniques Algorithm (ISODATA) and Simple Linear Iterative Clustering (SLIC). Two datasets with different characteristics are used in order to evaluate the approaches, and the block-based and segmentation-based approaches are shown to provide enhanced performance.
关键词： hyperspectral,superpixels,pansharpening,segmentation,Block-based
更新于2025-09-10 09:29:36
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[ACM Press the 2nd International Conference - Las Vegas, NV, USA (2018.08.27-2018.08.29)] Proceedings of the 2nd International Conference on Vision, Image and Signal Processing - ICVISP 2018 - Automatic 3D Prostate Image Segmentation via Patch-based Density Constraints Clustering
摘要： Currently methods on prostate segmentation barely solve the problems about the low prostate CT contrast, high edge ambiguity, surrounding adhesion tissues and especially the tumor motion. To effectively manage those problems in prostate treatment using CT guided radiotherapy, automated segmentation needs to be performed. In this paper, an automatic 3D prostate image segmentation via Patch-based density constraints clustering (PDCC) is developed. The main contributions of this method lie in the following three strategies: 1) compared with only using pixel intensity information, Superpixel-based 3D patch includes more structure contexts to deal with low contrast problem in prostate CT images. 2) Compacting and extracting discriminative information in the each patch with 3D gray-gradient co-occurrence matrix are used to distinguish tiny texture difference between prostate and non-prostate. 3) Density constraints clustering algorithm focus on a higher density than their neighbors’ points with relatively small distance to cope with two nearby organs touch together. Further, clusters are recognized regardless of their shape and of the dimensionality of the space in which they are embedded. The proposed method has been evaluated on 10 patients’ prostate CT image database where each patient includes 50 treatment images, and several state-of-the-art prostate CT segmentation algorithms with various evaluation metrics have been as comparisons. Experimental results demonstrate that the proposed method achieves higher segmentation accuracy and lower average surface distance.
关键词： Prostate,Segmentation,CT Image,Superpixels
更新于2025-09-10 09:29:36