A novel change detection (CD) method for very high-resolution images is proposed by integrating multi-scale features. First, a novel edge density matching index was designed, and the structural similarity of textures, including grey level co-occurrence matrix, Gaussian Markov random field, and Gabor features between bitemporal images, were extracted to measure changes. Then, an adaptive approach was proposed to select optimal textures based on the majority consistency between spectrum and textures. Afterward, all features were decomposed into multi-scale features and fused into initial CD maps using Dempster–Shafer evidence theory. Finally, advantage fusion was implemented to generate the final CD map by fusing initial CD maps to remove noise and preserve details. Experiments conducted on real SPOT 5 and simulated QuickBird datasets, which achieved the total error ratios of 8.74% and 2.50%, respectively, indicate the effectiveness of the proposed approach.
Abstract Although deep neural network technology brings high recognition accuracy to the field of synthetic aperture radar image‐based automatic target recognition, it also produces the catastrophic forgetting problem. Here, a new incremental learning method that can extract more information about old data is proposed. Based on the rehearsal method, the authors’ method adds extra linear layers after the feature extractor of the network before training on new incremental data and uses the network to generate distilled labels for incremental training. Through experiments on the moving and stationary target acquisition and recognition data set, we conclude that, when the old model has good performance, our method has better performance than other typical incremental learning methods on small data sets.
Abstract Image datasets in the field of industrial manufacturing usually have the problem of uneven distribution of samples. To solve this problem, this paper utilizes transfer learning to recognize surface blemishes of Aluminum material image. In order to get rid of the disadvantage of VGGNet too many parameters, this paper combines VGGNet with Network-in-Network to generate a new model. The results of experiments is shown that it has achieved significant improvement by using transfer learning. Additionally, the new model also achieves a better performance and the number of parameters of the new model is much smaller than that of the original VGGNet.
Deriving characteristic parameters is very important to the accurate interpretation of synthetic aperture radar (SAR) image and the application of land cover classification. In this paper, we apply the uniform polarimetric matrix rotation theory to the polarimetric interferometric SAR (PolInSAR) data and deduce the parameter set of the polarimetric interferometric coherency matrix in rotation domain. The relationship between the characteristics of the parameter set and the terrain is also analyzed. Finally, we propose a land cover classification scheme using parameters in rotation domain and apply it to measured PolInSAR data. The classification result is better than using the parameters in rotation domain of polarimetric SAR (PolSAR) data and confirm that the polarimetric interferometric coherency matrix parameters in rotation domain can be used for land cover classification.
ABSTRACTSeasonal changes usually exist and cause false alarms in the bi-temporal change detection from high-resolution remote sensing images. It is difficult to remove these false alarms only using bi-temporal images for traditional change detection methods. A change detection method is proposed to remove seasonal false alarms in bi-temporal change detection by introducing time series information of medium-resolution remote sensing images. First, the mid-resolution time series results are mapped to the ground objects obtained by multiscale segmentation of high-resolution remote sensing images. Second, set the thresholds for the proportion of each category of pixels in the object to obtain high-resolution time series results. Finally, the high-resolution change detection results are optimized by the improved high-resolution time series results. Experimental results show that this method can optimize the results of high-resolution change detection, and the accuracy of this method was improved by at least 0.23 than that of traditional change detection by reducing seasonal errors. The proposed method was an effective change detection approach for high-resolution images to reduce detection errors due to seasonal differences.KEYWORDS: Change detectionhigh-resolution imagestime seriesseasonal differences AcknowledgementsThis work was supported by the Fundamental Research Funds for the Central Universities under Grant 2019ZDPY09 and the National Natural Science Foundation of China under Grant 42271368 and U22A20569.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe work was supported by the National Natural Science Foundation of China [42271368,U22A20569]; Fundamental Research Funds for the Central Universities [2019ZDPY09].
Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to increase CD accuracy. First, two temporal images are stacked and segmented into multiscale segmentation maps. Then, a pixel-based change map with memberships belonging to changed and unchanged parts is obtained by fuzzy c-means clustering. Finally, based on the Dempster-Shafer evidence theory, the proposed scale-driven CD method incorporating uncertainty analysis is performed on the multiscale segmentation maps and the pixel-based change map. Two experiments were carried out on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and SPOT 5 data sets. The ratio of total errors can be reduced to 4.0% and 7.5% for the ETM+ and SPOT 5 data sets in this study, respectively. Moreover, the proposed approach outperforms some state-of-the-art CD methods and provides an effective solution for CD.
This letter presents a multiscale convolutional neural network with color vegetation indices (MCCNN) for semantic labeling of point cloud directly in a 3-D model. First, color vegetation indices are calculated for each point with RGB information. Second, based on classic Point convolutional neural network (CNN), a new multiscale network is designed to incorporate multiscale information through the spatial contexts of different sizes around each point by setting different convolution of kernels $K$ , and then multiscale features produced by different convolutional layers are aggregated and unsampled. Finally, via Fully Connection layer and Softmax classifier, each point is labeled. Two different datasets, Semantic3D and Vaihingen3D, are used to evaluate the performance of the proposed method, and the results are compared with those produced by other existing approaches. Experimental results indicate that the proposed method achieves 84.5% in terms of overall accuracy on Semantic3D, and 85.2% on Vaihingen3D, which is the highest among the considered methods.
In this letter, we propose a change detection method based on Gabor wavelet features for very high resolution (VHR) remote sensing images. First, Gabor wavelet features are extracted from two temporal VHR images to obtain spatial and contextual information. Then, the Gabor-wavelet-based difference measure (GWDM) is designed to generate the difference image. In GWDM, a new local similarity measure is defined, in which the Markov random field neighborhood system is incorporated to obtain a local relationship, and the coefficient of variation method is applied to discriminate contributions from different features. Finally, the fuzzy c-means cluster algorithm is employed to obtain the final change map. Experiments employing QuickBird and SPOT5 images demonstrate the effectiveness of the proposed approach.
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