- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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A change detection framework by fusing threshold and clustering methods for optical medium resolution remote sensing images
摘要： In change detection (CD) of medium-resolution remote sensing images, the threshold and clustering methods are two kinds of the most popular ones. It is found that the threshold method of the expectation maximization (EM) algorithm usually generates a CD map including many false alarms but almost detecting all changes, and the fuzzy local information c-means algorithm (FLICM) obtains a homogenous CD map but with some missed detections. Therefore, a framework is designed to improve CD results by fusing the advantages of the threshold and clustering methods. The CD map generated by the clustering method of FLICM is used to remove false alarms in the CD map obtained by EM threshold method by an overlap fusion. Then, the local Markov random field model is implemented to verify the potentially missed detections. Finally, a fused CD map with less false alarms and missed detections is achieved. Two experiments were carried out on two Landsat ETM+ data sets. The proposed method obtained the least errors (1.11% and 3.51%) and the highest kappa coefficient (0.9366 and 0.8834), respectively, when compared with five popular CD methods.
关键词： Change detection,advantage fusion,remote sensing,clustering,threshold
更新于2025-09-23 15:23:52
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Detection of land use/cover change in Egyptian Nile Delta using remote sensing
摘要： The present study aims to assess the changes of different land use/land cover classes for Nile Delta of Egypt during the period from 1987 to 2015, to evaluate the impact of land cover change and urban sprawl, before, during and after the 25th of January 2011 using remote sensing and GIS techniques, as a result to unplanned urban sprawl which was done by people during the lack of general security of Egyptian revolution. The results indicated that there was a regular trend characterized in most classes and that the change in different land use/land cover classes ranged between increase and decrease areas. A continuous increase in agricultural, urban, ?sh farms and natural vegetation areas and a continuous decrease in water bodies and sand areas were detected in the studied area. The agricultural area recorded the highest increase during the period from 1987 to 2000 (305296.1 ha.) while it increased by 170578.1 ha., during the period from 2000 to 2015. However, in urban area, the highest increase was recorded during the period from 2000 to 2015 followed by the period 1987–2000 with mean values of 97940.8 and 53112.6 ha., respectively. The analysis of the results showed that most of Egyptian Delta governorates have been signi?cantly affected by the different classes of land use/land cover change due to agriculture activities, urban growth as a result of human activities dynamic impact.
关键词： Change detection,Remote sensing,Nile Delta governorates,Land use/land cover
更新于2025-09-23 15:23:52
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[IEEE 2018 12th International Conference on Communications (COMM) - Bucharest (2018.6.14-2018.6.16)] 2018 International Conference on Communications (COMM) - Supervized Change Detection for SAR Imagery Based on Processing of a Low Size Training Data Set by an Ensemble of Self-Organizing Maps
摘要： This paper presents a new method to improve accuracy of supervised change detection in Synthetic Aperture Radar (SAR) imagery. The model is based on the idea to apply a low size labeled dataset to the input of an Ensemble of Self-Organizing Maps (ESOM) for training data generation (TDG). The resulted synthetic data set produced by ESOM substitutes the initial authentically labeled sample set and it is used to train a supervised change detection classifier. The proposed method is evaluated using a TerraSAR-X image of 400x400 pixels acquired in the Fukushima region, Japan, before and after tsunami. As change detection classifiers we have comparatively considered Support Vector Machine (SVM), Nearest Neighbor (NN), the three-Nearest Neighbors (3-NN), and Likelihood Bayes classifier. The experimental results have confirmed the effectiveness of the proposed approach using only 100 authentic labeled pixels.
关键词： SAR images,ensemble of self-organizing maps (ESOM),virtual training data generation(VTDG),change detection
更新于2025-09-23 15:23:52
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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 in Semantic Level for SAR Images
摘要： Considering that the traditional change detection algorithms only focus on extracting the change area but ignore the change content identification, a novel change detection framework for synthetic aperture radar (SAR) images is proposed. The framework integrates the merits of unsupervised and supervised learning to detect changes in semantic level. First, the residual convolutional auto-encoder (RCAE) is designed to convert SAR image slices to the histogram representation. Then, we calculate the difference vectors and extract the change area by their norms. Finally, we classify the difference vectors of change region and identify the content of change. Experimental results indicate that the proposed method significantly achieves performance improvement over existing algorithms.
关键词： semantic,bag of visual words,synthetic aperture radar,auto-encoder,change detection
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 - Normalized Compression Distance for SAR Image Change Detection
摘要： With a continuous increase in multi-temporal synthetic aperture radar (SAR) images, leading to enable mapping applications for Earth environmental observation, the number of algorithms for detection of different types of terrain changes has greatly expanded. In this paper, a SAR image change detection method based on normalized compression distance (NCD) is proposed. The procedure mainly consists in dividing two time series images in patches, computing a collection of similarities corresponding to each pair of patches and generating the change map with a histogram-based threshold. The experimental results were computed using 2 Sentinel 1A images over the city of Bucharest, Romania and 2 TerraSAR-X images over the Elbe River and its surrounding area, Germany.
关键词： Change detection,SAR,NCD,satellite image time series (SITS)
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 - Sea Ice Change Detection in SAR Images Based on Collaborative Representation
摘要： Sea ice change detection from synthetic aperture radar (SAR) images is important for navigation safety and natural resource extraction. This paper proposed a sea ice change detection method from SAR images based on collaborative representation. First, neighborhood-based ratio is used to generate a difference image (DI). Then, some reliable samples are selected from the DI by hierarchical fuzzy C-means (FCM) clustering. Finally, based upon these samples, collaborative representation method is utilized to classify pixels from the original SAR images into unchanged and changed class. From there, the final change map can be obtained. Experimental results on two real sea ice datasets demonstrate the superiority of the proposed method over two closely related methods.
关键词： sea ice change detection,synthetic aperture radar,clustering method,collaborative representation
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 - The Earth Obsevation Data Ecosystem Monitoring (Eodesm) System
摘要： Through the EU FP7 Horizon 2020 ECOPOTENTIAL project, a novel and innovative approach to classification has been developed, which is termed the Earth Observation Data for Ecosystem Monitoring (EODESM), and has been built on concepts behind an implementation of the Earth Observation Data for Habitat Monitoring (EODHaM) system generated as part of the EU FP7 BIOSOS project, applied to Very High Resolution (VHR) Worldview data. The EODESM system facilitates routine classification of land covers according to the Food and Agricultural Organisations Land Cover Classification System (FAO-LCCS), translates these to other taxonomies (including General Habitat Classifications; GHCs) and facilitates routine detection of change and the generation of maps indicating the causes and consequences of such change.
关键词： change detection,earth observation,environmental variables,Land cover classification
更新于2025-09-23 15:23:52
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A Generative Discriminatory Classified Network for Change Detection in Multispectral Imagery
摘要： Multispectral image change detection based on deep learning generally needs a large amount of training data. However, it is difficult and expensive to mark a large amount of labeled data. To deal with this problem, we propose a generative discriminatory classified network (GDCN) for multispectral image change detection, in which labeled data, unlabeled data, and new fake data generated by generative adversarial networks are used. The GDCN consists of a discriminatory classified network (DCN) and a generator. The DCN divides the input data into changed class, unchanged class, and extra class, i.e., fake class. The generator recovers the real data from input noises to provide additional training samples so as to boost the performance of the DCN. Finally, the bitemporal multispectral images are input to the DCN to get the final change map. Experimental results on the real multispectral imagery datasets demonstrate that the proposed GDCN trained by unlabeled data and a small amount of labeled data can achieve competitive performance compared with existing methods.
关键词： Change detection,deep learning,multispectral imagery,generative adversarial networks (GANs)
更新于2025-09-23 15:23:52
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Triplet-Based Semantic Relation Learning for Aerial Remote Sensing Image Change Detection
摘要： This letter presents a novel supervised change detection method based on a deep siamese semantic network framework, which is trained by using improved triplet loss function for optical aerial images. The proposed framework can not only extract features directly from image pairs which include multiscale information and are more abstract as well as robust, but also enhance the interclass separability and the intraclass inseparability by learning semantic relation. The feature vectors of the pixels pair with the same label are closer, and at the same time, the feature vectors of the pixels with different labels are farther from each other. Moreover, we use the distance of the feature map to detect the changes on the difference map between the image pair. Binarized change map can be obtained by a simple threshold. Experiments on optical aerial image data set validate that the proposed approach produces comparable, even better results, favorably to the state-of-the-art methods in terms of F-measure.
关键词： triplet loss function,Change detection,semantic relation,optical aerial images,siamese semantic network
更新于2025-09-23 15:23:52
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Imbalanced Learning-Based Automatic SAR Images Change Detection by Morphologically Supervised PCA-Net
摘要： Change detection is a quite challenging task due to the imbalance between unchanged and changed class. In addition, the traditional difference map generated by log-ratio is subject to the speckle, which will reduce the accuracy. In this letter, an imbalanced learning-based change detection is proposed based on PCA network (PCA-Net), where a supervised PCA-Net is designed to obtain the robust features directly from given multitemporal synthetic aperture radar (SAR) images instead of a difference map. Furthermore, to tackle with the imbalance between changed and unchanged classes, we propose a morphologically supervised learning method, where the knowledge in the pixels near the boundary between two classes is exploited to guide network training. Finally, our proposed PCA-Net can be trained by the data sets with available reference maps and applied to a new data set, which is quite practical in change detection projects. Our proposed method is veri?ed on ?ve sets of multiple temporal SAR images. It is demonstrated from the experiment results that with the knowledge in training samples from the boundary, the learned features bene?t change detection and make the proposed method outperform than supervised methods trained by randomly drawing samples.
关键词： Change detection,imbalance learning,synthetic aperture radar (SAR) images,PCA network (PCA-Net)
更新于2025-09-23 15:23:52