- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Evaluation of ForestPA for VHR RS image classification using spectral and superpixel-guided morphological profiles
摘要： In very high resolution (VHR) remote sensing (RS) classification tasks, conventional pixel-based contextual information extraction methods such as morphological profiles (MPs), extended MPs (EMPs) and MPs with partial reconstruction (MPPR) with limited numbers, sizes and shapes of structural elements (SEs) cannot perfectly match all sizes and shapes of the objects in an image. To overcome such limitation, we introduce novel spatial feature extractors, namely, the superpixel-guided morphological profiles (SPMPs), where the superpixels are used as SEs in opening by reconstruction and closing by reconstruction operations. Moreover, to avoid possible side effects from unusual maximum and minimum values within superpixels, the mean pixel value of superpixels is adopted (SPMPsM). Additionally, new decision forest based on penalizing the attributes in previous trees, the ForestPA is introduced and evaluated through a comparative investigation on three VHR multi-/hyperspectral RS image classification tasks. Support vector machine and benchmark ensemble classifiers, including bagging, AdaBoost, MultiBoost, ExtraTrees, Random Forest and Rotation Forest, are adopted. The experimental results confirm the effectiveness and superior performances of the proposed SPMPs and SPMPsM relative to those of the MPs and MPPR. Moreover, ForestPA outperforms only bagging and is not suitable for learning from large numbers of samples with high dimensionality from the computational efficiency and classification accuracy perspective.
关键词： ForestPA,superpixel,MPs,superpixel-guided morphological profiles,MPPR,image classification,VHR images
更新于2025-09-23 15:23:52
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Superpixel-Based Semisupervised Active Learning for Hyperspectral Image Classification
摘要： In this work, we propose a new semisupervised active learning approach for hyperspectral image classification. The proposed method aims at improving machine generalization by using pseudolabeled samples, both confident and informative, which are automatically and actively selected, via semisupervised learning. The learning is performed under two assumptions: a local one for the labeling via a superpixel-based constraint dedicated to the spatial homogeneity and adaptivity into the pseudolabels, and a global one modeling the data density by a multinomial logistic regressor with a Markov random field regularizer. Furthermore, we propose a density-peak-based augmentation strategy for pseudolabels, due to the fact that the samples without manual labels in their superpixel neighborhoods are out of reach for the automatic sampling. Three real hyperspectral datasets were used in our experiments to evaluate the effectiveness of the proposed superpixel-based semisupervised learning approach. The obtained results indicate that the proposed approach can greatly improve the potential for semisupervised learning in hyperspectral image classification.
关键词： semisupervised learning,hyperspectral image classification,superpixel,clustering,Active learning
更新于2025-09-23 15:23:52
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Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery
摘要： Dense semantic labeling is significant in high-resolution remote sensing imagery research and it has been widely used in land-use analysis and environment protection. With the recent success of fully convolutional networks (FCN), various types of network architectures have largely improved performance. Among them, atrous spatial pyramid pooling (ASPP) and encoder-decoder are two successful ones. The former structure is able to extract multi-scale contextual information and multiple effective field-of-view, while the latter structure can recover the spatial information to obtain sharper object boundaries. In this study, we propose a more efficient fully convolutional network by combining the advantages from both structures. Our model utilizes the deep residual network (ResNet) followed by ASPP as the encoder and combines two scales of high-level features with corresponding low-level features as the decoder at the upsampling stage. We further develop a multi-scale loss function to enhance the learning procedure. In the postprocessing, a novel superpixel-based dense conditional random field is employed to refine the predictions. We evaluate the proposed method on the Potsdam and Vaihingen datasets and the experimental results demonstrate that our method performs better than other machine learning or deep learning methods. Compared with the state-of-the-art DeepLab_v3+ our model gains 0.4% and 0.6% improvements in overall accuracy on these two datasets respectively.
关键词： dense semantic labeling,encoder-decoder,superpixel-based DenseCRF,remote sensing imagery,fully convolutional networks,atrous spatial pyramid pooling
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 - 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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Water Body Extraction From Very High-Resolution Remote Sensing Imagery Using Deep U-Net and a Superpixel-Based Conditional Random Field Model
摘要： Water body extraction (WBE) has attracted considerable attention in the field of remote sensing image analysis. Herein, we present an enhanced deep convolutional encoder–decoder (DCED) network (or Deep U-Net) specifically tailored to WBE from remote sensing images by applying superpixel segmentation and conditional random fields (CRFs). First, we preclassify the entire remote sensing image into the water and nonwater areas via Deep U-Net, using the results of class membership probabilities as the unary potential in the CRF model. The pairwise potential of CRF is defined by a linear combination of Gaussian kernels, which forms a fully connected neighbor structure. Next, regional restriction is incorporated into the approach to enhance the consistency of the connected area. We use the simple linear iterative clustering algorithm to generate superpixels and correct the binary classification results by calculating their average posterior probabilities. Finally, a highly efficient approximate inference algorithm, mean-field inference, is generated for the final model. The results from the experimental application to GaoFen-2 images and WorldView-2 images demonstrate that the proposed approach exhibits competitive quantitative and qualitative performance, which effectively reduces salt-and-pepper noise and retains the edge structures of water bodies. Compared to existing state-of-the-art methods, our proposed method achieves superior final results.
关键词： Conditional random fields (CRFs),Deep U-Net,superpixel,regional restriction (RR),water body extraction (WBE)
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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3D Human Pose Estimation from Range Images with Depth Difference and Geodesic Distance
摘要： Depth difference, as a popularly used feature for characterizing pairwise pixels of range images, fails to precisely capture skeleton joints when human body possesses a wild and complicated articulation. As the geodesic distance of pairwise pixels is able to present a global connected property and adjacent pixels often belong to the same body component, we propose an effective and efficient framework for pose estimation. Firstly, all the pixels of a range image are grouped into superpixels using an improved Simple Linear Iterative Clustering algorithm. Secondly, those superpixels are labelled as the components of a human body using the hybrid feature. Thirdly, component-based cluster feature extraction is undertaken on skeleton joints of body components with K-means clustering algorithm. Finally, the feature points of each component are then stacked as a compact representation of human poses and are mapped to the skeleton joints of a human body. Experimental results demonstrate that the proposed framework outperforms several state-of-the-art pose estimation methods.
关键词： Superpixel,Random decision forest,Geodesic distance,Human pose estimation,Sparse representation
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 - Individual Tree Detection from Multi-View Satellite Images
摘要： Individual tree detection is critical in forest monitoring and inventory. In this paper, we propose a novel method to use multi-view satellite images to detect individual trees and delineate their crowns. As compared to previous methods that only use image information, we generate the DSM from the multi-view high-resolution satellite images and combine it with the spectral information to detect the trees. Firstly, the vegetation areas are extracted to remove the non-vegetation objects while terrain areas are extracted to help estimate the tree height. Then, we utilize top-hat morphological operation to efficiently find the local maximal points as tree tops and further refine them by checking their heights and doing non-maximum suppression. Finally, we use a revised superpixel segmentation algorithm to delineate the tree crowns which considered both 2D spectral and 3D structure similarities. To effectively assess the performance, we rigorously match and evaluate the detected and reference trees in a one-to-one relationship. A quantitative evaluation at three different sites shows that the proposed method is able to detect individual trees at different regions with high accuracy.
关键词： Remote Sensing,DSM,Individual Tree Detection,Superpixel,Multi-view Satellite Image
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, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Multi-Attribute Super-Tensor Model for Remote Sensing Image Classification with High Spatial Resolution
摘要： With the development of remote sensors, it is much easier to acquire large amount of remote sensing images (RSIs) with very high spatial resolution, which has made the spatial characteristics play an important role in classification task. Many work of spatial-spectral classification have been done and achieved good results, especially superpixel-based methods. However, these methods didn’t take each superpixel as an entirety, which had ignored the relationship between spatial and spectral signature. It is well known that RSI can be treated as a third-order data cube, thus it can also be represented by a third-order tensor. This paper proposed a Multi-Attribute Superpixel Tensor (MAST) model to address the aforementioned problem. Experiments conducted on two real RSIs and compared with several well-known methods demonstrate the effectiveness of the proposed model.
关键词： remote sensing images,superpixel,spatial-spectral classification,EMAP,tensor
更新于2025-09-23 15:21:21
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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