235 results on '"Lorenzo Bruzzone"'
Search Results
2. A Novel Method for Hidden Natural Caves Characterization and Accessibility Assessment From Spaceborne VHR SAR Images
- Author
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Leonardo Carrer, Davide Castelletti, Riccardo Pozzobon, Francesco Sauro, and Lorenzo Bruzzone
- Subjects
Rocks ,Vegetation mapping ,Radar ,very high-resolution (VHR) synthetic aperture radar (SAR) ,Spaceborne radar ,Earth ,Synthetic aperture radar ,Radar imaging ,Capella space ,caves ,lava tubes ,planetary surfaces ,General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2023
3. Spatial-Spectral Dual Back-Projection Network for Pansharpening
- Author
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Kai Zhang, Anfei Wang, Feng Zhang, Wenbo Wan, Jiande Sun, and Lorenzo Bruzzone
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2023
4. Local and Long-Range Collaborative Learning for Remote Sensing Scene Classification
- Author
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Maofan Zhao, Qingyan Meng, Linlin Zhang, Xinli Hu, and Lorenzo Bruzzone
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2023
5. WIANet: A Wavelet-Inspired Attention-Based Convolution Neural Network for Land Cover Classification
- Author
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Lorenzo Bruzzone and Abhishek Singh
- Subjects
Electrical and Electronic Engineering ,Geotechnical Engineering and Engineering Geology - Published
- 2023
6. Lightweight Attention Network for Very High-Resolution Image Semantic Segmentation
- Author
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Renchu Guan, Mingming Wang, Lorenzo Bruzzone, Haishi Zhao, and Chen Yang
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2023
7. Analysis of Lava Tubes’ Roughness and Radar Near-Nadir Regime Backscattering Properties
- Author
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Leonardo Carrer and Lorenzo Bruzzone
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2023
8. Forest Change Detection in Lidar Data Based on Polar Change Vector Analysis
- Author
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Daniele Marinelli, Nicholas C. Coops, Lorenzo Bruzzone, and Douglas K. Bolton
- Subjects
Forest dynamics ,Computer science ,Sustainable forest management ,0211 other engineering and technologies ,Magnitude (mathematics) ,02 engineering and technology ,Land cover ,15. Life on land ,Geotechnical Engineering and Engineering Geology ,Set (abstract data type) ,Lidar ,Electrical and Electronic Engineering ,Polar coordinate system ,Change detection ,021101 geological & geomatics engineering ,Remote sensing - Abstract
Monitoring forest dynamics is of critical importance for both sustainable forest management and conservation purposes. Light detection and ranging (lidar) data provide a detailed representation of the 3-D structure of forest stands that can be used to analyze a number of trees and stand characteristics. Recently, multiple lidar acquisitions over the same area are becoming more common allowing changes in stand attributes to be assessed over time. In order to effectively utilize such multitemporal data sets for forest dynamics monitoring, we propose a method for unsupervised change detection (CD) of lidar data based on polar change vector analysis (CVA). The proposed method involves extracting relevant lidar point cloud metrics for a given area over time. Pixel-wise difference vectors of the metrics are then converted from Cartesian to polar coordinates to represent the magnitude and direction of change. Finally, the change vectors are analyzed in the polar domain to automatically discriminate between the different classes of change. The method is applied to a multitemporal lidar data set of coniferous forest on Vancouver Island, British Columbia, Canada, impacted by various types of land cover change. The experimental results demonstrate that the proposed method is capable of automatically discriminating between different classes of lidar change.
- Published
- 2022
9. RSCNet: A Residual Self-Calibrated Network for Hyperspectral Image Change Detection
- Author
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Liguo Wang, Lifeng Wang, Qunming Wang, and Lorenzo Bruzzone
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
10. A Range-Doppler Method for Focusing Radar Sounder Data Generated by Coherent Electromagnetic Simulators
- Author
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Elisa Sbalchiero, Sanchari Thakur, and Lorenzo Bruzzone
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
11. A Nonconvex Framework for Sparse Unmixing Incorporating the Group Structure of the Spectral Library
- Author
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Francesca Bovolo, Zheng Ma, Lorenzo Bruzzone, and Longfei Ren
- Subjects
Statistics::Machine Learning ,Spectral signature ,Pixel ,Computer science ,Norm (mathematics) ,Convergence (routing) ,General Earth and Planetary Sciences ,Hyperspectral imaging ,Penalty method ,Minification ,Electrical and Electronic Engineering ,Algorithm ,Image (mathematics) - Abstract
Sparse unmixing (SU) has been widely investigated for hyperspectral analysis with the aim to find the optimal subset of spectral signatures in a spectral library (known in advance) that can optimally model each pixel of the given hyperspectral image. Usually, the available spectral library organizes spectral signatures in groups. However, most existing strategies do not take full advantage of the inherent properties in the library. In this article, we design a convex framework for SU that incorporates the group structure of the spectral library. The convex framework includes two kinds of algorithms derived from either the primal or the dual form of the alternating direction method of multipliers (ADMM). Then, the convergence properties of the convex framework are established. Based on the convex framework, a novel nonconvex framework is developed for unmixing, which provides a new manner to enhance the sparsity of solution. The core of the nonconvex framework is to design a nonconvex penalty function for efficient minimization utilizing the generalized shrinkage mapping. The penalty function can be regarded as a closer approximation of the l₀ norm. Experiments conducted on simulated and real hyperspectral data demonstrate the superiority and effectiveness of the proposed nonconvex framework in improving the unmixing performance and enhancing the sparsity of solution with respect to state-of-the-art techniques.
- Published
- 2022
12. Analysis of Earth’s Ionosphere Effects on Englacial Layering Detectability in Spaceborne Radar Sounders Data
- Author
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Sanchari Thakur, Leonardo Carrer, and Lorenzo Bruzzone
- Subjects
Electrical and Electronic Engineering ,Geotechnical Engineering and Engineering Geology - Published
- 2022
13. Detecting Changes by Learning No Changes: Data-Enclosing-Ball Minimizing Autoencoders for One-Class Change Detection in Multispectral Imagery
- Author
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Lichao Mou, Yuansheng Hua, Sudipan Saha, Francesca Bovolo, Lorenzo Bruzzone, and Xiao Xiang Zhu
- Subjects
remote sensing ,Autoencoder network, change detection, one-class classification, remote sensing ,Autoencoder network ,General Earth and Planetary Sciences ,Electrical and Electronic Engineering ,change detection ,one-class classification - Published
- 2022
14. Looking Outside the Window: Wide-Context Transformer for the Semantic Segmentation of High-Resolution Remote Sensing Images
- Author
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Lei Ding, Dong Lin, Shaofu Lin, Jing Zhang, Xiaojie Cui, Yuebin Wang, Hao Tang, and Lorenzo Bruzzone
- Subjects
FOS: Computer and information sciences ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Abstract
Long-range contextual information is crucial for the semantic segmentation of High-Resolution (HR) Remote Sensing Images (RSIs). However, image cropping operations, commonly used for training neural networks, limit the perception of long-range contexts in large RSIs. To overcome this limitation, we propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs. Apart from extracting local features with a conventional CNN, the WiCoNet has an extra context branch to aggregate information from a larger image area. Moreover, we introduce a Context Transformer to embed contextual information from the context branch and selectively project it onto the local features. The Context Transformer extends the Vision Transformer, an emerging kind of neural network, to model the dual-branch semantic correlations. It overcomes the locality limitation of CNNs and enables the WiCoNet to see the bigger picture before segmenting the land-cover/land-use (LCLU) classes. Ablation studies and comparative experiments conducted on several benchmark datasets demonstrate the effectiveness of the proposed method. In addition, we present a new Beijing Land-Use (BLU) dataset. This is a large-scale HR satellite dataset with high-quality and fine-grained reference labels, which can facilitate future studies in this field.
- Published
- 2022
15. A Bayesian Approach to Active Self-Paced Deep Learning for SAR Automatic Target Recognition
- Author
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Haohao Ren, Yukun Zhang, Xuegang Wang, Xuelian Yu, Lorenzo Bruzzone, and Lin Zou
- Subjects
Synthetic aperture radar ,Training set ,Computer science ,business.industry ,Active learning (machine learning) ,Deep learning ,Bayesian probability ,0211 other engineering and technologies ,Pattern recognition ,Sample (statistics) ,02 engineering and technology ,Geotechnical Engineering and Engineering Geology ,Bayesian inference ,Target acquisition ,ComputingMethodologies_PATTERNRECOGNITION ,Automatic target recognition ,Active learning ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,021101 geological & geomatics engineering - Abstract
Deep learning has attracted intensive attention in synthetic aperture radar (SAR) automatic target recognition (ATR). Usually, a considerable number of labeled samples are necessary to learn a deep model for obtaining good generalization capability. However, the process of sample labeling is time-consuming and costly. This letter proposes an active self-paced deep learning (ASPDL) approach to SAR ATR. In a nutshell, we first introduce the Bayesian inference into the process of deep model parameter optimization, aiming at learning a robust classification model in the case of a limited number of labeled samples. Next, a cost-effective sample selection strategy is presented to iteratively and actively select the informative samples from a pool of unlabeled samples for labeling. Concretely, high-confidence samples are actively selected through self-paced learning (SPL) way and automatically pseudo-labeled with the current classification model, whereas low-confidence samples are chosen through an active learning strategy and manually labeled. Finally, we update the parameters of the model by minimizing a dual-loss function using a new training set that is constructed by incorporating new labeled samples with original ones. Experiments on the moving and stationary target acquisition and recognition (MSTAR) benchmark data demonstrate that the proposed method can achieve better classification accuracy with relatively few labeled samples compared with some state-of-the-art methods.
- Published
- 2022
16. A Deep Learning Architecture for Semantic Segmentation of Radar Sounder Data
- Author
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Francesca Bovolo, Lorenzo Bruzzone, and Elena Donini
- Subjects
Computer science ,business.industry ,law ,Deep learning ,General Earth and Planetary Sciences ,Segmentation ,Computer vision ,Artificial intelligence ,Electrical and Electronic Engineering ,Architecture ,Radar ,business ,law.invention - Published
- 2022
17. SPCNet: A Subpixel Convolution-Based Change Detection Network for Hyperspectral Images With Different Spatial Resolutions
- Author
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Lifeng Wang, Liguo Wang, Heng Wang, Xiaoyi Wang, and Lorenzo Bruzzone
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
18. Novel Cross-Resolution Feature-Level Fusion for Joint Classification of Multispectral and Panchromatic Remote Sensing Images
- Author
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Lorenzo Bruzzone, Sicong Liu, Alim Samat, Qian Du, Xiaohua Tong, and Hui Zhao
- Subjects
Computer science ,Multispectral image ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Autoencoder ,Panchromatic film ,Feature (computer vision) ,Robustness (computer science) ,Computer Science::Computer Vision and Pattern Recognition ,General Earth and Planetary Sciences ,Electrical and Electronic Engineering ,Scale (map) ,Joint (audio engineering) ,Representation (mathematics) ,Remote sensing - Abstract
With the increasing availability and resolution of satellite sensor data, multispectral (MS) and panchromatic (PAN) images are the most popular data that are used in remote sensing among applications. This paper proposes a novel cross-resolution hidden layer features fusion (CRHFF) approach for joint classification of multi-resolution MS and PAN images. In particular, shallow spectral and spatial features at a global scale are firstly extracted from a MS image. Then deep cross-resolution hidden layer features extracted from MS and PAN are fused from patches at a local scale according to an Autoencoder (AE) like deep network. Finally, the selected multi-resolution hidden layer features are classified in a supervised manner. By taking advantage of integrated shallow-to-deep and global-to-local features from the high-resolution MS and PAN images, the cross-resolution latent information can be extracted and fused in order to better model imaged objects from the multi-model representation, and finally increase the classification accuracy. Experimental results obtained on three real multiresolution data sets covering complex urban scenarios confirm the effectiveness of the proposed approach in terms of higher accuracy and robustness with respect to literature methods.
- Published
- 2022
19. A Novel Technique for Robust Training of Deep Networks With Multisource Weak Labeled Remote Sensing Data
- Author
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Lorenzo Bruzzone and Gianmarco Perantoni
- Subjects
Complex data type ,Digital mapping ,Generalization ,Computer science ,business.industry ,Reliability (computer networking) ,Deep learning ,Process (computing) ,A-weighting ,Robustness (computer science) ,General Earth and Planetary Sciences ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Remote sensing - Abstract
Deep learning has gained broad interest in remote sensing image scene classification thanks to the effectiveness of deep neural networks in extracting the semantics from complex data. However, deep networks require large amounts of training samples to obtain good generalization capabilities and are sensitive to errors in the training labels. This is a problem in remote sensing since highly reliable labels can be obtained at high costs and in limited amount. However, many sources of less reliable labeled data are available, e.g., obsolete digital maps. In order to train deep networks with larger datasets, we propose both the combination of single or multiple weak sources of labeled data with a small but reliable dataset to generate multisource labeled datasets and a novel training strategy where the reliability of each source is taken into consideration. This is done by exploiting the transition matrices describing the statistics of the errors of each source. The transition matrices are embedded into the labels and used during the training process to weigh each label according to the related source. The proposed method acts as a weighting scheme at gradient level, where each instance contributes with different weights to the optimization of different classes. The effectiveness of the proposed method is validated by experiments on different datasets. The results proved the robustness and capability of leveraging on unreliable source of labels of the proposed method.
- Published
- 2022
20. Superpixel-Level Global and Local Similarity Graph-Based Clustering for Large Hyperspectral Images
- Author
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Haishi Zhao, Fengfeng Zhou, Lorenzo Bruzzone, Renchu Guan, and Chen Yang
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
21. Spatial and Spectral Extraction Network With Adaptive Feature Fusion for Pansharpening
- Author
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Kai Zhang, Anfei Wang, Feng Zhang, Wenxiu Diao, Jiande Sun, and Lorenzo Bruzzone
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
22. SIGAN: Spectral Index Generative Adversarial Network for Data Augmentation in Multispectral Remote Sensing Images
- Author
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Lorenzo Bruzzone and Abhishek Singh
- Subjects
Test data generation ,Computer science ,business.industry ,Deep learning ,Multispectral image ,Pattern recognition ,Geotechnical Engineering and Engineering Geology ,Image (mathematics) ,Domain (software engineering) ,Multispectral pattern recognition ,Unsupervised learning ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Generative grammar - Abstract
Generative models are typically employed to approximate the distribution of deep features. Recently, these state-of-the-art methods have been applied to estimate image transformations by an unsupervised learning approach. In this letter, a novel spectral index generative adversarial network (SIGAN) is proposed for the generation of multispectral (MS) remote sensing images. This network is defined to effectively perform data augmentation starting from a limited number of training samples in the MS remote sensing domain for training deep learning models. The SIGAN model is able to capture class-specific properties in data augmentation, by incorporating the task-specific normalized spectral indices to model class-by-class properties of MS images. Experimental results obtained on a Sentinel 2 dataset show that the proposed model provides better performance than other generative adversarial networks (GANs) in MS data generation.
- Published
- 2022
23. Better Memorization, Better Recall: A Lifelong Learning Framework for Remote Sensing Image Scene Classification
- Author
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Dingqi Ye, Jian Peng, Haifeng Li, and Lorenzo Bruzzone
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
24. An Interactive Strategy for the Training Set Definition Based on Active Self-Paced Learning Implemented on a Cloud-Computing Platform
- Author
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Luca Orlandi, Lorenzo Bruzzone, and Claudia Paris
- Subjects
Training set ,business.industry ,Computer science ,Remote sensing (archaeology) ,Convergence (routing) ,Real-time computing ,Training (meteorology) ,Cloud computing ,Electrical and Electronic Engineering ,Geotechnical Engineering and Engineering Geology ,business ,Self paced - Published
- 2022
25. A Self-Supervised Approach to Pixel-Level Change Detection in Bi-Temporal RS Images
- Author
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Lorenzo Bruzzone and Chen Yuxing
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
26. Mono- and Dual-Regulated Contractive-Expansive-Contractive Deep Convolutional Networks for Classification of Multispectral Remote Sensing Images
- Author
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Lorenzo Bruzzone and Abhishek Singh
- Subjects
Electrical and Electronic Engineering ,Geotechnical Engineering and Engineering Geology - Published
- 2022
27. Change Detection in Image Time-Series Using Unsupervised LSTM
- Author
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Francesca Bovolo, Sudipan Saha, and Lorenzo Bruzzone
- Subjects
Sequence ,Pixel ,business.industry ,Computer science ,Feature vector ,Deep learning ,0211 other engineering and technologies ,Pattern recognition ,02 engineering and technology ,Geotechnical Engineering and Engineering Geology ,Identification (information) ,Anomaly detection ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Change detection ,021101 geological & geomatics engineering ,Event (probability theory) - Abstract
Deep learning-based unsupervised change detection (CD) methods compare a prechange and a postchange image in deep feature space and require precise knowledge of the event date for selecting proper pre-/post-change images. However, in many applications changes may occur gradually over a span of time making pre-/post-dates difficult to establish or prior knowledge of event date is unknown. On the other hand, deep learning-based time-series analysis methods are generally supervised. Considering such scenarios, we propose a novel unsupervised deep learning-based method to detect changes in an image time-series. The method does not make any assumption on the date of the occurrence of the change event. It treats CD as an anomaly detection problem by exploiting multilayer long short term memory (LSTM) network to learn a representation of the time series. The proposed method ingests a shuffled time series and uses an encoder-decoder LSTM model to rearrange the input sequence in correct order. While the model fails to rearrange the changed pixels, unchanged data can be rearranged in the correct order. This enables the identification of the changed pixels. To show the effectiveness of the proposed method, we tested it on two multitemporal Sentinel-1 data sets over Brumadinho, Brazil, and Bhavanisagar, India.
- Published
- 2022
28. A Shallow-to-Deep Feature Fusion Network for VHR Remote Sensing Image Classification
- Author
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Sicong Liu, Yongjie Zheng, Qian Du, Lorenzo Bruzzone, Alim Samat, Xiaohua Tong, Yanmin Jin, and Chao Wang
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
29. MP-ResNet: Multipath Residual Network for the Semantic Segmentation of High-Resolution PolSAR Images
- Author
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Bing Liu, Lei Ding, Dong Lin, Yuxing Chen, Lorenzo Bruzzone, Jiansheng Li, and Kai Zheng
- Subjects
FOS: Computer and information sciences ,business.industry ,Intersection (set theory) ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Speckle noise ,Pattern recognition ,Geotechnical Engineering and Engineering Geology ,Residual ,Convolutional neural network ,Discriminative model ,Code (cryptography) ,Embedding ,Segmentation ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Multipath propagation - Abstract
There are limited studies on the semantic segmentation of high-resolution polarimetric synthetic aperture radar (PolSAR) images due to the scarcity of training data and the complexity of managing speckle noise. The Gaofen contest has provided open access a high-quality PolSAR semantic segmentation dataset. Taking this opportunity, we propose a multipath residual network (MP-ResNet) architecture for the semantic segmentation of high-resolution PolSAR images. Compared to conventional U-shape encoder-decoder convolutional neural network (CNN) architectures, the MP-ResNet learns semantic context with its parallel multiscale branches, which greatly enlarges its valid receptive fields and improves the embedding of local discriminative features. In addition, MP-ResNet adopts a multilevel feature fusion design in its decoder to effectively exploit the features learned from its different branches. Comparisons with the baseline method of fully connected network (FCN with ResNet34) show that the MP-ResNet has achieved significant accuracy improvements. It also surpasses several state-of-the-art methods in terms of overall accuracy (OA), mF₁ and frequency weighted intersection over union (fwIoU), with only a limited increase of computational costs. This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images. The code is available at: https://github.com/ggsDing/SARSeg.
- Published
- 2022
30. A Triangulation-Based Technique for Tree-Top Detection in Heterogeneous Forest Structures Using High Density LiDAR Data
- Author
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Lorenzo Bruzzone, Claudia Paris, and Daniele Marinelli
- Subjects
Tree (data structure) ,Computer science ,business.industry ,Triangulation (social science) ,High density ,Lidar data ,Computer vision ,Artificial intelligence ,Electrical and Electronic Engineering ,Geotechnical Engineering and Engineering Geology ,business - Published
- 2022
31. Full-Level Domain Adaptation for Building Extraction in Very-High-Resolution Optical Remote-Sensing Images
- Author
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Yufu Zang, Daifeng Peng, Lorenzo Bruzzone, and Haiyan Guan
- Subjects
Generalization ,business.industry ,Computer science ,Pattern recognition ,Filter (signal processing) ,Convolutional neural network ,Regularization (mathematics) ,Domain (software engineering) ,Image (mathematics) ,Consistency (database systems) ,Feature (computer vision) ,General Earth and Planetary Sciences ,Artificial intelligence ,Electrical and Electronic Engineering ,business - Abstract
Convolutional neural networks (CNNs) have achieved tremendous success in computer vision tasks, such as building extraction. However, due to domain shift, the performance of the CNNs drops sharply on unseen data from another domain, leading to poor generalization. As it is costly and time-consuming to acquire dense annotations for remote-sensing (RS) images, developing algorithms that can transfer knowledge from a labeled source domain to an unlabeled target domain is of great significance. To this end, we propose a novel full-level domain adaptation network (FDANet) for building extraction by combining image-, feature-, and output-level information effectively. At the input level, a simple Wallis filter method is employed to transfer source images into target-like ones whereby alleviating radiometric discrepancy and achieving image-level alignment. To further reduce domain shift, adversarial learning is used to enforce feature distribution consistency constraints between the source and target images. In this way, feature-level alignment can be embedded effectively. At the output level, a mean-teacher model is introduced to enforce transformation-consistent constraint for the target output so that the regularization effect is enhanced and the uncertain predictions can be suppressed as much as possible. To further improve the performance, a novel self-training strategy is also employed by using pseudo labels. The effectiveness of the proposed FDANet is verified on three diverse high-resolution aerial datasets with different resolutions and scenarios. Extensive experimental results and ablation studies demonstrated the superiority of the proposed method.
- Published
- 2022
32. An Unsupervised Fuzzy System for the Automatic Detection of Candidate Lava Tubes in Radar Sounder Data
- Author
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Leonardo Carrer, Francesca Bovolo, Christopher Gerekos, Lorenzo Bruzzone, and Elena Donini
- Subjects
geography ,geography.geographical_feature_category ,Lava ,Crust ,Geophysics ,Fuzzy control system ,Gravity anomaly ,law.invention ,Lava tube ,Volcano ,law ,Simulated data ,General Earth and Planetary Sciences ,Electrical and Electronic Engineering ,Radar ,Geology - Abstract
Lava tubes are buried channels that transport thermally insulated lava. Nowadays, lava tubes on the Moon are believed to be empty and thus indicated as potential habitats for humankind. In recent years, several studies investigated possible lava tube locations, considering the gravity anomaly distribution and surficial volcanic features. This article proposes a novel and unsupervised method to map candidate buried empty lava tubes in radar sounder data (radargrams) and extract their physical properties. The approach relies on a model that describes the geometrical and electromagnetic (EM) properties of lava tubes in radargrams. According to this model, reflections in radargrams are automatically detected and analyzed with a fuzzy system to identify those associated with lava tube boundaries and reject the others. The fuzzy rules consider the EM and geometrical properties of lava tubes, and thus, their appearance in radargrams. The proposed method can address the complex task of identifying candidate lava tubes on a large number of radargrams in an automatic, fast, and objective way. The final decision on candidate lava tubes should be taken in postprocessing by expert planetologists. The proposed method is tested on both a real and a simulated data set of radargrams acquired on the Moon by the Lunar Radar Sounder (LRS). Identified candidate lava tubes are processed to extract geometrical parameters, such as the depth and the thickness of the crust (roof).
- Published
- 2022
33. GCFnet: Global Collaborative Fusion Network for Multispectral and Panchromatic Image Classification
- Author
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Hui Zhao, Sicong Liu, Qian Du, Lorenzo Bruzzone, Yongjie Zheng, Kecheng Du, Xiaohua Tong, Huan Xie, and Xiaolong Ma
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
34. Clutter Reduction by Estimation of Echoes Direction of Arrival in Distributed Radar Sounders in Formation Flying
- Author
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Leonardo Carrer, Sanchari Thakur, Luca Sericati, and Lorenzo Bruzzone
- Subjects
General Earth and Planetary Sciences ,Electrical and Electronic Engineering - Published
- 2022
35. A System for Burned Area Detection on Multispectral Imagery
- Author
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Mario Costantini, Francesca Bovolo, Sudipan Saha, Massimo Zanetti, Maria Lucia Magliozzi, Lorenzo Bruzzone, Daniele Marinelli, and Massimo Zavagli
- Subjects
Computer science ,business.industry ,Algebraic operation ,Multispectral image ,General Earth and Planetary Sciences ,Cloud computing ,Electrical and Electronic Engineering ,business ,Thresholding ,Field (computer science) ,Remote sensing - Abstract
The current remote sensing (RS) open data policy for multispectral (MS) missions such as Sentinel-2 and Landsat-8, together with the availability of free cloud distributed processing platforms such as Google Earth Engine, makes it possible the quick generation of burned area (BA) products even for nonexperts in the field. Indeed, fires and BAs can be detected using burn severity indices, which are usually obtained by simple band algebra operations. However, simple approaches can aid BA estimation only if typical error patterns are known and accounted for, especially when working at large (e.g., continental) scales. This article proposes an automatic BA detection system based on burn severity index thresholding, which integrates dedicated false and missed alarm mitigation strategies to improve the detection accuracy. The system is tested on Sentinel-2 and Landsat-8 data over ten different locations in Europe and spanning year 2018. Three known burn severity indices plus a custom one defined to improve the performance in the considered study area are under study. Results show that burned index thresholding is possible within accuracy bounds slightly larger than the state of the art, which is acceptable by considering the proposed simplified processing framework.
- Published
- 2022
36. A Novel Approach to the Detection and Imaging of Candidate Martian Subglacial Water Bodies by Radar Sounder Data
- Author
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Lorenzo Bruzzone and Leonardo Carrer
- Subjects
Martian ,MARSIS ,Mars Exploration Program ,law.invention ,Depth sounding ,law ,Lake Vostok ,General Earth and Planetary Sciences ,Electrical and Electronic Engineering ,Radar ,Ionosphere ,Digital elevation model ,Geology ,Remote sensing - Abstract
Research based on Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) data detected unusual radar bright basal reflections located at about 1.5 km depth in a Mars region denoted as Ultimi Scopuli. These reflections were interpreted as a signature of subglacial liquid water even though this interpretation is still being debated in the literature. In this article, we propose a novel approach to the detection and imaging of candidate subglacial liquid water from radar sounding data. The approach combines the radar echo power traces with a suitable digital elevation model to provide a bidimensional representation of the surface. Even if the imaging method reconstructs a representation of the surface, we prove that it can be used to identify subsurface bright reflections in icy regions. Imaging is feasible even if the basal interface is not directly included in the processed data for image generation. To support this experimental evidence, we show that a relationship exists between the value of the reflected echo power originating from the englacial layers and the basal-to-surface-echo-power ratio. The observed relationship holds on both Ultimi Scopuli radar sounding data acquired on Mars and Lake Vostok data acquired on Earth. Our results show that the 2-D imaging provides an alternative way for locating candidate subglacial liquid water bodies on Mars over large areas also where the basal interface is not directly measured. The proposed approach complements previous research for further evaluation of the actual presence of liquid water on Mars.
- Published
- 2022
37. Multilayer Feature Fusion Network With Spatial Attention and Gated Mechanism for Remote Sensing Scene Classification
- Author
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Qingyan Meng, Maofan Zhao, Linlin Zhang, Wenxu Shi, Chen Su, and Lorenzo Bruzzone
- Subjects
Electrical and Electronic Engineering ,Geotechnical Engineering and Engineering Geology - Published
- 2022
38. Conditioning Jovian Burst Signals for Passive Sounding Applications
- Author
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Leonardo Carrer, T. Maximillian Roberts, Lorenzo Bruzzone, Andrew Romero-Wolf, S. T. Peters, and Dustin M. Schroeder
- Subjects
Depth sounding ,Acoustics ,General Earth and Planetary Sciences ,Conditioning ,Electrical and Electronic Engineering ,Jovian ,Geology - Published
- 2022
39. An Approach to the Assessment of Detectability of Subsurface Targets in Polar Ice From Satellite Radar Sounders
- Author
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Lorenzo Bruzzone, Francesca Bovolo, Sanchari Thakur, and Elena Donini
- Subjects
Satellite radar ,Interface (computing) ,Climate change ,Physics::Geophysics ,law.invention ,law ,General Earth and Planetary Sciences ,Polar ,Cryosphere ,Satellite ,Electrical and Electronic Engineering ,Antenna gain ,Radar ,Physics::Atmospheric and Oceanic Physics ,Geology ,Remote sensing - Abstract
A satellite mission onboard a radar sounder for the observation of the earth's polar regions can greatly support the monitoring of the cryosphere and climate change analyses. Several studies are in progress proposing the design and demonstrating the performance of such an earth-orbiting radar sounder (EORS). However, one critical aspect of the cryospheric targets that are often ignored and simplified in these studies is the complex geoelectrical nature of the polar ice. In this article, we present a performance assessment of the polar ice target detectability by focusing on their realistic representation. This is obtained by simulating the orbital radargrams corresponding to different regions of the polar cryosphere by leveraging the data available from airborne campaigns in Antarctica and Greenland. We propose novel performance metrics to analyze the detectability of the internal reflecting horizons (IRHs), the basal interface, and to analyze the nature of the basal interface. This performance assessment strategy can be applied to guide the design of the signal-to-noise ratio (SNR) budget at the surface, which can further support the selection of the main orbital instrument parameters, such as the transmitted power, the two-way antenna gain, and the processing gains.
- Published
- 2022
40. Spectral-Spatial Genetic Algorithm-Based Unsupervised Band Selection for Hyperspectral Image Classification
- Author
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Chen Yang, Lorenzo Bruzzone, Haishi Zhao, Renchu Guan, and Fengfeng Zhou
- Subjects
Fitness function ,business.industry ,Computer science ,Hyperspectral imaging ,Pattern recognition ,Scoring algorithm ,Genetic algorithm ,Redundancy (engineering) ,General Earth and Planetary Sciences ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Spatial analysis ,Premature convergence ,Curse of dimensionality - Abstract
Band selection (BS) can mitigate the “curse of dimensionality” problem and improve the performance of hyperspectral image (HSI) classification. Genetic algorithms (GAs) have been applied to the task of hyperspectral BS showing significant advantages compared with other literature methods. However, the traditional GAs-based methods often select sets of bands having residual redundancy due to the large search space related to hyperspectral BS and the limitation of premature convergence in GAs. Moreover, existing GAs-based methods often are supervised, and that needs a large number of labeled samples to compute the fitness value for assessing the quality of selected bands. In this article, an unsupervised BS approach based on an improved GA is proposed. A fitness function based on the fisher score combined with superpixel is designed for evaluating the discriminability of band subsets considering both spectral and spatial information. Then, modified genetic operations are constructed to restrain the search space and reduce the redundancy of selected bands. The performance of the proposed spectral-spatial GA-based BS method is evaluated on three HSIs. The experimental results demonstrate that the proposed method is superior to the traditional GA-based method and seven state-of-the-art unsupervised methods.
- Published
- 2021
41. ClusterCNN: Clustering-Based Feature Learning for Hyperspectral Image Classification
- Author
-
Lorenzo Bruzzone, Wei Yao, and Cheng Lian
- Subjects
Pixel ,business.industry ,Computer science ,Feature extraction ,Hyperspectral imaging ,Pattern recognition ,Geotechnical Engineering and Engineering Geology ,Convolutional neural network ,Field (computer science) ,Artificial intelligence ,Electrical and Electronic Engineering ,Focus (optics) ,business ,Cluster analysis ,Feature learning - Abstract
Convolutional neural networks (CNNs) are widely used in the field of remote sensing images. However, the applications of CNNs and related techniques often ignore the properties of remote sensing data. In our study, we focus on the hyperspectral image (HSI) classification problem, and address the issue of including the very rich spectral information present in HSIs in CNN-based models to produce highly accurate classification results. We propose a two-step classification technique, ClusterCNN. The first step divides HSI pixels into different clusters, to form a material map which can be considered as a compressed expression of the original spectral features. The second step trains a CNN that can extract spatial features from the material map, and then exploits these spatial features to classify HSI pixels. The proposed approach follows a strict hierarchy to exploit both the spectral and spatial features in HSIs. Experimental results show the effectiveness of ClusterCNN as compared to the much more complicated state-of-the-art approaches.
- Published
- 2021
42. Attention-Based Adaptive Spectral–Spatial Kernel ResNet for Hyperspectral Image Classification
- Author
-
Swalpa Kumar Roy, Suvojit Manna, Tiecheng Song, and Lorenzo Bruzzone
- Subjects
Computer science ,business.industry ,Feature extraction ,Hyperspectral imaging ,Pattern recognition ,Residual ,Convolutional neural network ,Residual neural network ,Kernel (linear algebra) ,Discriminative model ,Kernel (image processing) ,General Earth and Planetary Sciences ,Artificial intelligence ,Electrical and Electronic Engineering ,business - Abstract
Hyperspectral images (HSIs) provide rich spectral–spatial information with stacked hundreds of contiguous narrowbands. Due to the existence of noise and band correlation, the selection of informative spectral–spatial kernel features poses a challenge. This is often addressed by using convolutional neural networks (CNNs) with receptive field (RF) having fixed sizes. However, these solutions cannot enable neurons to effectively adjust RF sizes and cross-channel dependencies when forward and backward propagations are used to optimize the network. In this article, we present an attention-based adaptive spectral–spatial kernel improved residual network ( A2S2K-ResNet ) with spectral attention to capture discriminative spectral–spatial features for HSI classification in an end-to-end training fashion. In particular, the proposed network learns selective 3-D convolutional kernels to jointly extract spectral–spatial features using improved 3-D ResBlocks and adopts an efficient feature recalibration (EFR) mechanism to boost the classification performance. Extensive experiments are performed on three well-known hyperspectral data sets, i.e., IP, KSC, and UP, and the proposed A2S2K-ResNet can provide better classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa compared with the existing methods investigated. The source code will be made available at https://github.com/suvojit- $0\times 55$ aa/A2S2K-ResNet.
- Published
- 2021
43. ARU-Net: Reduction of Atmospheric Phase Screen in SAR Interferometry Using Attention-Based Deep Residual U-Net
- Author
-
Qishi Sun, Lorenzo Bruzzone, Yuxing Chen, and Liming Jiang
- Subjects
Synthetic aperture radar ,Reduction (complexity) ,Interferometry ,Interferometric synthetic aperture radar ,Atmospheric correction ,General Earth and Planetary Sciences ,Environmental science ,Atmospheric model ,Electrical and Electronic Engineering ,Residual ,Standard deviation ,Remote sensing - Abstract
Atmospheric phase screen (APS) is a very critical issue for the application of interferometric synthetic aperture radar (InSAR) techniques. The spatial–temporal variations of APS are the dominant error source in interferograms and may completely mask displacement signals. Many external meteorological data-based methods and phase-based methods have been developed in the past decades, but all have their inherent limitations. In this article, we propose a deep learning-based method, which is based on an attention-based deep residual U-shaped network (ARU-Net), to mitigate atmospheric artifacts. With this approach, APS patches and clean interferogram patches are sampled from InSAR interferograms to train the network. After training, the network can be used to mitigate the APS for individual interferograms. Compared with the generic atmospheric correction model (GACOS) and the advanced time-series InSAR method distributed scatterer interferometry (DSI), the key advantage of our method is that atmospheric delay can be effectively learned and removed from individual high-resolution interferometric phase itself without external data. Accuracy was validated by using individual and stacked interferograms from TerraSAR-X data over the Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP) sites. The results showed that our method consistently delivered greater standard deviation (SD) reduction after APS correction than the GACOS method. Moreover, the time-series results were in agreement with the DSI and leveling measurements. The effectiveness of the proposed ARU-Net to remove APS effects from interferograms shows great potential for the development of a new set of deep learning-based APS reduction methods.
- Published
- 2021
44. SemiCDNet: A Semisupervised Convolutional Neural Network for Change Detection in High Resolution Remote-Sensing Images
- Author
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Xu Huang, Haiyong Ding, Lorenzo Bruzzone, Haiyan Guan, Yongjun Zhang, and Daifeng Peng
- Subjects
business.industry ,Computer science ,Deep learning ,Feature extraction ,0211 other engineering and technologies ,02 engineering and technology ,Image segmentation ,Convolutional neural network ,Data modeling ,General Earth and Planetary Sciences ,Entropy (information theory) ,Segmentation ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Change detection ,021101 geological & geomatics engineering ,Remote sensing - Abstract
Change detection (CD) is one of the main applications of remote sensing. With the increasing popularity of deep learning, most recent developments of CD methods have introduced the use of deep learning techniques to increase the accuracy and automation level over traditional methods. However, when using supervised CD methods, a large amount of labeled data is needed to train deep convolutional networks with millions of parameters. These labeled data are difficult to acquire for CD tasks. To address this limitation, a novel semisupervised convolutional network for CD (SemiCDNet) is proposed based on a generative adversarial network (GAN). First, both the labeled data and unlabeled data are input into the segmentation network to produce initial predictions and entropy maps. Then, to exploit the potential of unlabeled data, two discriminators are adopted to enforce the feature distribution consistency of segmentation maps and entropy maps between the labeled and unlabeled data. During the competitive training, the generator is continuously regularized by utilizing the unlabeled information, thus improving its generalization capability. The effectiveness and reliability of our proposed method are verified on two high-resolution remote sensing data sets. Extensive experimental results demonstrate the superiority of the proposed method against other state-of-the-art approaches.
- Published
- 2021
45. Unsupervised Deep Transfer Learning-Based Change Detection for HR Multispectral Images
- Author
-
Yady Tatiana Solano-Correa, Francesca Bovolo, Lorenzo Bruzzone, and Sudipan Saha
- Subjects
Pixel ,Contextual image classification ,Computer science ,business.industry ,Feature extraction ,Multispectral image ,0211 other engineering and technologies ,Pattern recognition ,02 engineering and technology ,Spectral bands ,Geotechnical Engineering and Engineering Geology ,Feature (computer vision) ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Image resolution ,Change detection ,021101 geological & geomatics engineering - Abstract
To overcome the limited capability of most state-of-the-art change detection (CD) methods in modeling spatial context of multispectral high spatial resolution (HR) images and exploiting all spectral bands jointly, this letter presents a novel unsupervised deep-learning-based CD method that can effectively model contextual information and handle the large number of bands in multispectral HR images. This is achieved by exploiting all spectral bands after grouping them into spectral-dedicated band groups. To eliminate the necessity of multitemporal training data, the proposed method exploits a data set targeted for image classification to train spectral-dedicated Auxiliary Classifier Generative Adversarial Networks (ACGANs). They are used to obtain pixelwise deep change hypervector from multitemporal images. Each feature in deep change hypervector is analyzed based on the magnitude to identify changed pixels. An ensemble decision fusion strategy is used to combine change information from different features. Experimental results on the urban, Alpine, and agricultural Sentinel-2 data sets confirm the effectiveness of the proposed method.
- Published
- 2021
46. Mars Surface Imaging by Exploiting Off-Nadir Radar Sounding Data
- Author
-
Lorenzo Bruzzone, Federico Zancanella, and Leonardo Carrer
- Subjects
0211 other engineering and technologies ,02 engineering and technology ,law.invention ,Depth sounding ,Lidar ,law ,Radar imaging ,Mars Orbiter Laser Altimeter ,Martian surface ,Surface roughness ,General Earth and Planetary Sciences ,Electrical and Electronic Engineering ,Radar ,Digital elevation model ,Geology ,021101 geological & geomatics engineering ,Remote sensing - Abstract
Radar sounder surface imaging is a rather unexplored approach to the analysis of planetary bodies. While a radar sounder is an instrument specifically designed for subsurface investigations, a particular set of power measurements (denoted as off-nadir surface echoes) can be exploited together with an external digital elevation model to produce images of the investigated surface at meters wavelength. The use of the off-nadir data may also reveal the presence of previously undetected subsurface features. In this article, we present a method for producing surface roughness images by high-frequency (HF) radar sounder data. The study of surface roughness in the HF band is particularly useful for both geologic studies and landing-zone reconnaissance as it is evaluated at meters to hundreds of meters horizontal scale. The proposed method combines off-nadir data of the Shallow Radar Sounder (SHARAD) with the Mars Orbiter Laser Altimeter (MOLA) digital elevation model. The produced roughness images at 20 MHz (15-m wavelength) of the Martian surface provide higher coverage and resolution of the surface roughness characterization at a 10–100-m horizontal scale than previous SHARAD work. By comparing the experimental roughness image with the one obtained by radar backscattering simulations, it is possible to identify subsurface features. In our experiments, we were able to produce a bidimensional image of a previously undetected large buried crater (10 km $\times12$ km) located in the Nili Fossae. This finding opens up new possibilities in exploiting radar sounding data for better detecting shallow subsurface features.
- Published
- 2021
47. Semisupervised Change Detection Using Graph Convolutional Network
- Author
-
Sudipan Saha, Francesca Bovolo, Lorenzo Bruzzone, Xiao Xiang Zhu, and Lichao Mou
- Subjects
Training set ,Pixel ,business.industry ,Computer science ,Feature extraction ,Pattern recognition ,Image segmentation ,semi-supervised ,Geotechnical Engineering and Engineering Geology ,Graph ,Data modeling ,graph convolutional network ,Graph (abstract data type) ,Segmentation ,Artificial intelligence ,Electrical and Electronic Engineering ,change detection ,business ,Image resolution ,Change detection - Abstract
Most change detection (CD) methods are unsupervised as collecting substantial multitemporal training data is challenging. Unsupervised CD methods are driven by heuristics and lack the capability to learn from data. However, in many real-world applications, it is possible to collect a small amount of labeled data scattered across the analyzed scene. Such a few scattered labeled samples in the pool of unlabeled samples can be effectively handled by graph convolutional network (GCN) that has recently shown good performance in semisupervised single-date analysis, to improve change detection performance. Based on this, we propose a semisupervised CD method that encodes multitemporal images as a graph via multiscale parcel segmentation that effectively captures the spatial and spectral aspects of the multitemporal images. The graph is further processed through GCN to learn a multitemporal model. Information from the labeled parcels is propagated to the unlabeled ones over training iterations. By exploiting the homogeneity of the parcels, the model is used to infer the label at a pixel level. To show the effectiveness of the proposed method, we tested it on a multitemporal Very High spatial Resolution (VHR) data set acquired by Pleiades sensor over Trento, Italy.
- Published
- 2021
48. Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding
- Author
-
Sudipan Saha, Francesca Bovolo, and Lorenzo Bruzzone
- Subjects
Synthetic aperture radar ,Computer science ,business.industry ,Feature extraction ,Multispectral image ,0211 other engineering and technologies ,Pattern recognition ,02 engineering and technology ,Transcoding ,computer.software_genre ,Feature (computer vision) ,General Earth and Planetary Sciences ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,computer ,Change detection ,021101 geological & geomatics engineering - Abstract
Building change detection (CD), important for its application in urban monitoring, can be performed in near real time by comparing prechange and postchange very-high-spatial-resolution (VHR) synthetic-aperture-radar (SAR) images. However, multitemporal VHR SAR images are complex as they show high spatial correlation, prone to shadows, and show an inhomogeneous signature. Spatial context needs to be taken into account to effectively detect a change in such images. Recently, convolutional-neural-network (CNN)-based transfer learning techniques have shown strong performance for CD in VHR multispectral images. However, its direct use for SAR CD is impeded by the absence of labeled SAR data and, thus, pretrained networks. To overcome this, we exploit the availability of paired unlabeled SAR and optical images to train for the suboptimal task of transcoding SAR images into optical images using a cycle-consistent generative adversarial network (CycleGAN). The CycleGAN consists of two generator networks: one for transcoding SAR images into the optical image domain and the other for projecting optical images into the SAR image domain. After unsupervised training, the generator transcoding SAR images into optical ones is used as a bitemporal deep feature extractor to extract optical-like features from bitemporal SAR images. Thus, deep change vector analysis (DCVA) and fuzzy rules can be applied to identify changed buildings (new/destroyed). We validate our method on two data sets made up of pairs of bitemporal VHR SAR images on the city of L’Aquila (Italy) and Trento (Italy).
- Published
- 2021
49. LANet: Local Attention Embedding to Improve the Semantic Segmentation of Remote Sensing Images
- Author
-
Lorenzo Bruzzone, Hao Tang, and Lei Ding
- Subjects
Artificial neural network ,Computer science ,Convolutional neural network (CNN) ,Feature extraction ,0211 other engineering and technologies ,deep learning ,Context (language use) ,02 engineering and technology ,Image segmentation ,Convolutional neural network ,semantic segmentation ,remote sensing ,Feature (computer vision) ,General Earth and Planetary Sciences ,Embedding ,Electrical and Electronic Engineering ,Convolutional neural network (CNN), deep learning, remote sensing, semantic segmentation ,021101 geological & geomatics engineering ,Remote sensing - Abstract
The trade-off between feature representation power and spatial localization accuracy is crucial for the dense classification/semantic segmentation of remote sensing images (RSIs). High-level features extracted from the late layers of a neural network are rich in semantic information, yet have blurred spatial details; low-level features extracted from the early layers of a network contain more pixel-level information but are isolated and noisy. It is therefore difficult to bridge the gap between high- and low-level features due to their difference in terms of physical information content and spatial distribution. In this article, we contribute to solve this problem by enhancing the feature representation in two ways. On the one hand, a patch attention module (PAM) is proposed to enhance the embedding of context information based on a patchwise calculation of local attention. On the other hand, an attention embedding module (AEM) is proposed to enrich the semantic information of low-level features by embedding local focus from high-level features. Both proposed modules are lightweight and can be applied to process the extracted features of convolutional neural networks (CNNs). Experiments show that, by integrating the proposed modules into a baseline fully convolutional network (FCN), the resulting local attention network (LANet) greatly improves the performance over the baseline and outperforms other attention-based methods on two RSI data sets.
- Published
- 2021
50. An Unsupervised Approach to Change Detection in Built-Up Areas by Multitemporal PolSAR Images
- Author
-
Lorenzo Bruzzone, Francesca Bovolo, Davide Pirrone, Shaunak De, and Avik Bhattacharya
- Subjects
Scattering ,Computer science ,business.industry ,Feature extraction ,0211 other engineering and technologies ,Polarimetry ,Pattern recognition ,02 engineering and technology ,Standard methods ,Geotechnical Engineering and Engineering Geology ,Thresholding ,Radar imaging ,Entropy (information theory) ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Change detection ,021101 geological & geomatics engineering - Abstract
Information from polarimetric synthetic aperture radar (PolSAR) imagery has been used for detecting built-up targets in classification problems, whereas it has been poorly exploited for change detection in multitemporal images. In this letter, we proposed an unsupervised approach for the detection of built-up changed areas from multitemporal full-polSAR images. The approach is based on the automatic thresholding of a novel change index based on the joint use of polarimetric span and average-alpha multitemporal information. The index is proposed for highlighting both constructed and demolished built-up elements. The experimental results on multitemporal UAVSAR images demonstrate that the proposed approach provides high detection accuracy and effectively separates among different types of changes, which is not the case with standard methods.
- Published
- 2020
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