- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Spatial–Spectral Feature Fusion Coupled with Multi-Scale Segmentation Voting Decision for Detecting Land Cover Change with VHR Remote Sensing Images
摘要： In this article, a novel approach for land cover change detection (LCCD) using very high resolution (VHR) remote sensing images based on spatial–spectral feature fusion and multi-scale segmentation voting decision is proposed. Unlike other traditional methods that have used a single feature without post-processing on a raw detection map, the proposed approach uses spatial–spectral features and post-processing strategies to improve detecting accuracies and performance. Our proposed approach involved two stages. First, we explored the spatial features of the VHR remote sensing image to complement the insu?ciency of the spectral feature, and then fused the spatial–spectral features with di?erent strategies. Next, the Manhattan distance between the corresponding spatial–spectral feature vectors of the bi-temporal images was employed to measure the change magnitude between the bi-temporal images and generate a change magnitude image (CMI). Second, the use of the Otsu binary threshold algorithm was proposed to divide the CMI into a binary change detection map (BCDM) and a multi-scale segmentation voting decision algorithm to fuse the initial BCDMs as the ?nal change detection map was proposed. Experiments were carried out on three pairs of bi-temporal remote sensing images with VHR remote sensing images. The results were compared with those of the state-of-the-art methods including four popular contextual-based LCCD methods and three post-processing LCCD methods. Experimental comparisons demonstrated that the proposed approach had an advantage over other state-of-the-art techniques in terms of detection accuracies and performance.
关键词： very high resolution,spatial–spectral features,bi-temporal remote sensing images,land cover change detection,multi-scale segmentation
更新于2025-09-11 14:15:04
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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 - Semi-Supervised Scene Classification for Remote Sensing Images Based on CNN and Ensemble Learning
摘要： The special characteristic of remote sensing (RS) images being large scale while only low number of labeled samples available in practical applications has been obstacle to the development of RS image classification. In this paper, a novel semi-supervised framework is proposed. The high-capacity convolutional neural networks (CNN) are adopted to extract preliminary image features. The strategy of ensemble learning is then utilized to establish discriminative image representations by exploring intrinsic information of available data. Plain supervised learning is finally performed to obtain classification results. To verify the efficacy of our work, we compare it with mainstream feature representation and semi-supervised approaches. Experimental results show that by utilizing CNN features and ensemble learning, our framework can obtain more effective image representations and achieve superior results compared with other paradigms of semi-supervised classification.
关键词： convolutional neural network,ensemble learning,remote sensing images,Semi-supervised classification,scene classification
更新于2025-09-11 14:15:04
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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 - Joint Feature Network for Bridge Segmentation in Remote Sensing Images
摘要： This paper proposes a novel convolutional neural network architecture for semantic segmentation of bridges with various scales in optical remote sensing images. In the context of RSI analysis on objects with irregular shapes, it is necessary to get dense, pixelwise classification maps. To address the issue, a new network architecture for producing refined shapes is required instead of image categorization labels. In our end-to-end framework, a ResNet is used as a backbone model to extract semantic features, then a cascaded top-down path is added to fuse these features as different scales. Joint features are obtained by stacking different layers of feature maps. Experiments show our proposed architecture has the ability to combine rich multi-scale contextual information to produce semantic segmentation maps with high accuracy.
关键词： remote sensing images (RSIs),semantic segmentation,convolutional neural networks (CNNs),pixelwise classification
更新于2025-09-10 09:29:36
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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 - Robust Super-Resolution Image Reconstruction Method for Geometrically Deformed Remote Sensing Images
摘要： Due to the limitations of imaging sensors, remote sensing images often have limited resolution. To address this issue, various super-resolution (SR) image reconstruction techniques have been developed to reconstruct a high-resolution image from a sequence of low-resolution, noisy and blurry observations. In this paper, we propose an efficient super-resolution image reconstruction method for geometrically deformed remote sensing images, based on the nonlocal total variation (NLTV) regularization. The proposed minimization problem is solved by a fast primal-dual algorithm. Numerical experiments demonstrate the performance of the proposed method.
关键词： super-resolution image reconstruction,Remote sensing images,primal-dual algorithm
更新于2025-09-10 09:29:36
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[IEEE 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Beijing (2018.8.19-2018.8.20)] 2018 10th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) - Multi-Branch Regression Network For Building Classification Using Remote Sensing Images
摘要： Convolutional neural networks (CNN) are widely used for processing high-resolution remote sensing images like segmentation or classification, and have been demonstrated excellent performance in recent years. In this paper, a novel classification framework based on segmentation method, called Multi-branch regression network (named as MBR-Net) is proposed. The proposed method can generate multiple losses rely on training images in different size of information. In addition, a complete training strategy for classifying remote sensing images, which can reduce the influence of uneven samples is also developed. Experimental results with Inrial aerial dataset demonstrate that the proposed framework can provide much better results compared to state-of-the-art U-Net and generate fine-grained prediction maps.
关键词： Deep learning,building classification,remote sensing images,multi-branch regression network
更新于2025-09-09 09:28:46
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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Change Detection Based on the Combination of Improved SegNet Neural Network and Morphology
摘要： Through the analysis of satellite remote sensing image data, the identification of newly added buildings in the same area can be realized to judge the use of land. The identification of newly added buildings based on remote sensing images, involving image object extraction, semantic segmentation and change detection. The difficulty is not only to identify the changes of remote sensing images in different periods, but also to identify the newly added buildings with the original buildings. Both of the recognition effect and the detection precision of the traditional method based on mathematical modeling need to be improved. SegNet neural network is a kind of deep convolution neural network. It shows good performance in dealing with the task of semantic segmentation of single image, but it is directly applied to building change detection with low accuracy. The simulation results show that the improved SegNet neural network method improves the accuracy of the quantitative evaluation index F1 score by 8.6% compared with the conventional SegNet network in the newly added building detection effect in the same area in 2015 and 2017. In addition, the situation that the change detection result will produce a large number of noise, a combination of improved SegNet network and morphological method is adopted to eliminate the noise and reduce the misjudgment. The simulation results show that the F1 index increased further by 1.4% on the basis of 8.6%.
关键词： convolutional neural network,deep learning,remote sensing images,building change detection,morphology
更新于2025-09-09 09:28:46
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Context-Aware and Depthwise-based Detection on Orbit for Remote Sensing Image
摘要： Automatic detection on orbit is an efficient way to filter useless data downloaded to the ground. However, detection on orbit is a challenging task due to limited computational resources on the satellite. In this paper, a context-aware and depthwise-based detection framework for remote sensing images is proposed which can be used on orbit. In the result of limited computational resources on the satellite, on-orbit object detection should detect with low memory cost and fast speed while ensuring the accuracy. To address the problem of small model in the process of feature extracting, a depthwise convolution is applied instead of typical convolution. In this light, a small deep neural network is built to run on orbit, using Single Shot Multibox Detector (SSD) as basic detection module. Motivated by its weak performance on remote sensing image owing to few pixel about target object, context information about target object is added to improve performance. To further investigate the context information influence, we add a balance factor to balance the context information and background noise it brings. Then an experiment on real remote sensing image dataset is conducted comparing our extended model with other current state-of-the-art detection models. Results show our extended model outperforms other models in accuracy and speed. Deploying the pretrained model on the Android Platform with only 60M memory cost confirms the feasibility to detect on orbit. This detection system is to be verified on the TZ-1 satellite which will be launched in the year of 2018.
关键词： context-aware,Automatic detection,SSD,remote sensing images,depthwise-based
更新于2025-09-09 09:28:46
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Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification
摘要： In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.
关键词： remote-sensing images,kernel space,collaborative representation,hybrid collaborative representation
更新于2025-09-04 15:30:14
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[IEEE 2018 26th International Conference on Geoinformatics - Kunming, China (2018.6.28-2018.6.30)] 2018 26th International Conference on Geoinformatics - SPARK Processing of Computing-Intensive Classification of Remote Sensing Images: The Case on K-Means Clustering Algorithm
摘要： High performance processing of remote sensing images is an important topic in remote sensing applications. One typical type of remote sensing processing is the iterative computing algorithms such as image classification algorithms, which are often computing-intensive and time-consuming. Recent advancement of cloud computing technologies such as APACHE SPARK has shown great promise for improving the computing performance. This paper presents a MapReduce based approach for parallelizing classification algorithms of remote sensing images on the cloud computing platform. The iterative processing is transformed into iterative Map and Reduce tasks that can be executed in parallel. The K-Means clustering algorithm is experimented with the SPARK cluster deployed on the OpenStack cloud computing platform to illustrate the applicability and effectiveness of the approach.
关键词： cloud computing,classification,distributed computing,remote sensing images
更新于2025-09-04 15:30:14
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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 - Determination of Degree of Damage on Building Roofs Due to Wind Disaster from Close Range Remote Sensing Images Using Texture Wavelet Analysis
摘要： In the current era of increasing natural disasters, especially wind disasters such as tropical cyclones, tornadoes, thunder storms etc., the need for a rapid damage assessment and mitigation action became inevitable. Detecting damages on a wider perspective using remote sensing images makes the damage investigation much faster. The current work introduces the technology of texture-wavelet analysis for detection of roof damages due to cyclones and tornadoes from close range remote sensing imageries. Degree of Damage (DoD) is quantified by calculating the percentage of damaged portion of the building roofs. A positive correlation factor ranging from 0.75 to 0.80 for remote imagery with respect to the visually measured data as well as field investigation data validates the accuracy of the method. Thus depending on severity measured from the percentage area of damage determined, emergency aid and medication can be prioritized thereby aiding disaster mitigation process.
关键词： Degree of Damage,Remote Sensing Images,Natural Disaster,Texture-Wavelet analysis,Correlation Factor
更新于2025-09-04 15:30:14