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Different methods are available for retrieving chlorophyll-a (Chl-a) in inland waters from optical imagery, but there is still a need for an inter-comparison among the products. Such analysis can provide insights into the method selection, integration of products, and algorithm development. This work aims at inter-comparison and consistency analyses among the Chl-a products derived from publicly available methods consisting of Case-2 Regional/Coast Colour (C2RCC), Water Color Simulator (WASI), and OC3 (3-band Ocean Color algorithm). C2RCC and WASI are physics-based processors enabling the retrieval of not only Chl-a but also total suspended matter (TSM) and colored dissolved organic matter (CDOM), whereas OC3 is a broadly used semi-empirical approach for Chl-a estimation. To pursue the inter-comparison analysis, we demonstrate the application of Sentinel-2 imagery in the context of multitemporal retrieval of constituents in some Italian lakes. The analysis is performed for different bio-optical conditions including subalpine lakes in Northern Italy (Garda, Idro, and Ledro) and a turbid lake in Central Italy (Lake Trasimeno). The Chl-a retrievals are assessed versus in situ matchups that indicate the better performance of WASI. Moreover, relative consistency analyses are performed among the products (Chl-a, TSM, and CDOM) derived from different methods. In the subalpine lakes, the results indicate a high consistency between C2RCC and WASI when
Milad Niroumand-Jadidi; Francesca Bovolo; Lorenzo Bruzzone; Peter Gege. Inter-Comparison of Methods for Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis in Italian Lakes. Remote Sensing 2021, 13, 2381 .
AMA StyleMilad Niroumand-Jadidi, Francesca Bovolo, Lorenzo Bruzzone, Peter Gege. Inter-Comparison of Methods for Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis in Italian Lakes. Remote Sensing. 2021; 13 (12):2381.
Chicago/Turabian StyleMilad Niroumand-Jadidi; Francesca Bovolo; Lorenzo Bruzzone; Peter Gege. 2021. "Inter-Comparison of Methods for Chlorophyll-a Retrieval: Sentinel-2 Time-Series Analysis in Italian Lakes." Remote Sensing 13, no. 12: 2381.
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 lâ‚€ 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.
Longfei Ren; Zheng Ma; Francesca Bovolo; Lorenzo Bruzzone. A Nonconvex Framework for Sparse Unmixing Incorporating the Group Structure of the Spectral Library. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -19.
AMA StyleLongfei Ren, Zheng Ma, Francesca Bovolo, Lorenzo Bruzzone. A Nonconvex Framework for Sparse Unmixing Incorporating the Group Structure of the Spectral Library. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-19.
Chicago/Turabian StyleLongfei Ren; Zheng Ma; Francesca Bovolo; Lorenzo Bruzzone. 2021. "A Nonconvex Framework for Sparse Unmixing Incorporating the Group Structure of the Spectral Library." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-19.
Dawei Wen; Xin Huang; Francesca Bovolo; Jiayi Li; Xinli Ke; Anlu Zhang; Jon Atli Benediktsson. Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine 2021, PP, 2 -35.
AMA StyleDawei Wen, Xin Huang, Francesca Bovolo, Jiayi Li, Xinli Ke, Anlu Zhang, Jon Atli Benediktsson. Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions. IEEE Geoscience and Remote Sensing Magazine. 2021; PP (99):2-35.
Chicago/Turabian StyleDawei Wen; Xin Huang; Francesca Bovolo; Jiayi Li; Xinli Ke; Anlu Zhang; Jon Atli Benediktsson. 2021. "Change Detection From Very-High-Spatial-Resolution Optical Remote Sensing Images: Methods, applications, and future directions." IEEE Geoscience and Remote Sensing Magazine PP, no. 99: 2-35.
Band selection refers to the process of choosing the most relevant bands in a hyperspectral image. By selecting a limited number of optimal bands, we aim at speeding up model training, improving accuracy, or both. It reduces redundancy among spectral bands while trying to preserve the original information of the image. By now, many efforts have been made to develop unsupervised band selection approaches, of which the majorities are heuristic algorithms devised by trial and error. In this article, we are interested in training an intelligent agent that, given a hyperspectral image, is capable of automatically learning policy to select an optimal band subset without any hand-engineered reasoning. To this end, we frame the problem of unsupervised band selection as a Markov decision process, propose an effective method to parameterize it, and finally solve the problem by deep reinforcement learning. Once the agent is trained, it learns a band-selection policy that guides the agent to sequentially select bands by fully exploiting the hyperspectral image and previously picked bands. Furthermore, we propose two different reward schemes for the environment simulation of deep reinforcement learning and compare them in experiments. This, to the best of our knowledge, is the first study that explores a deep reinforcement learning model for hyperspectral image analysis, thus opening a new door for future research and showcasing the great potential of deep reinforcement learning in remote sensing applications. Extensive experiments are carried out on four hyperspectral data sets, and experimental results demonstrate the effectiveness of the proposed method. The code is publicly available.
Lichao Mou; Sudipan Saha; Yuansheng Hua; Francesca Bovolo; Lorenzo Bruzzone; Xiao Xiang Zhu. Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.
AMA StyleLichao Mou, Sudipan Saha, Yuansheng Hua, Francesca Bovolo, Lorenzo Bruzzone, Xiao Xiang Zhu. Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.
Chicago/Turabian StyleLichao Mou; Sudipan Saha; Yuansheng Hua; Francesca Bovolo; Lorenzo Bruzzone; Xiao Xiang Zhu. 2021. "Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.
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).
Elena Donini; Leonardo Carrer; Christopher Gerekos; Lorenzo Bruzzone; Francesca Bovolo. An Unsupervised Fuzzy System for the Automatic Detection of Candidate Lava Tubes in Radar Sounder Data. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -19.
AMA StyleElena Donini, Leonardo Carrer, Christopher Gerekos, Lorenzo Bruzzone, Francesca Bovolo. An Unsupervised Fuzzy System for the Automatic Detection of Candidate Lava Tubes in Radar Sounder Data. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-19.
Chicago/Turabian StyleElena Donini; Leonardo Carrer; Christopher Gerekos; Lorenzo Bruzzone; Francesca Bovolo. 2021. "An Unsupervised Fuzzy System for the Automatic Detection of Candidate Lava Tubes in Radar Sounder Data." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-19.
While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change detection, we explore methods that exploit the temporal consistency of Sentinel-2 times series to obtain a usable self-supervised learning signal. For this, we build and make publicly available (https://zenodo.org/record/4280482) the Sentinel-2 Multitemporal Cities Pairs (S2MTCP) dataset, containing multitemporal image pairs from 1520 urban areas worldwide. We test the results of multiple self-supervised learning methods for pre-training models for change detection and apply it on a public change detection dataset made of Sentinel-2 image pairs (OSCD).
Marrit Leenstra; Diego Marcos; Francesca Bovolo; Devis Tuia. Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery. Transactions on Petri Nets and Other Models of Concurrency XV 2021, 578 -590.
AMA StyleMarrit Leenstra, Diego Marcos, Francesca Bovolo, Devis Tuia. Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery. Transactions on Petri Nets and Other Models of Concurrency XV. 2021; ():578-590.
Chicago/Turabian StyleMarrit Leenstra; Diego Marcos; Francesca Bovolo; Devis Tuia. 2021. "Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 578-590.
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.
Sudipan Saha; Francesca Bovolo; Lorenzo Bruzzone. Change Detection in Image Time-Series Using Unsupervised LSTM. IEEE Geoscience and Remote Sensing Letters 2020, PP, 1 -5.
AMA StyleSudipan Saha, Francesca Bovolo, Lorenzo Bruzzone. Change Detection in Image Time-Series Using Unsupervised LSTM. IEEE Geoscience and Remote Sensing Letters. 2020; PP (99):1-5.
Chicago/Turabian StyleSudipan Saha; Francesca Bovolo; Lorenzo Bruzzone. 2020. "Change Detection in Image Time-Series Using Unsupervised LSTM." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
A new era of spaceborne hyperspectral imaging has just begun with the recent availability of data from PRISMA (PRecursore IperSpettrale della Missione Applicativa) launched by the Italian space agency (ASI). There has been pre-launch optimism that the wealth of spectral information offered by PRISMA can contribute to a variety of aquatic science and management applications. Here, we examine the potential of PRISMA level 2D images in retrieving standard water quality parameters, including total suspended matter (TSM), chlorophyll-a (Chl-a), and colored dissolved organic matter (CDOM) in a turbid lake (Lake Trasimeno, Italy). We perform consistency analyses among the aquatic products (remote sensing reflectance (Rrs) and constituents) derived from PRISMA and those from Sentinel-2. The consistency analyses are expanded to synthesized Sentinel-2 data as well. By spectral downsampling of the PRISMA images, we better isolate the impact of spectral resolution in retrieving the constituents. The retrieval of constituents from both PRISMA and Sentinel-2 images is built upon inverting the radiative transfer model implemented in the Water Color Simulator (WASI) processor. The inversion involves a parameter (gdd) to compensate for atmospheric and sun-glint artifacts. A strong agreement is indicated for the cross-sensor comparison of Rrs products at different wavelengths (average R ≈ 0.87). However, the Rrs of PRISMA at shorter wavelengths (gdd through the inversion that suggests an underestimated atmospheric path radiance of PRISMA level 2D products compared to the atmospherically corrected Sentinel-2 data. The results indicate the high potential of PRISMA level 2D imagery in mapping water quality parameters in Lake Trasimeno. The PRISMA-based retrievals agree well with those of Sentinel-2, particularly for TSM.
Milad Niroumand-Jadidi; Francesca Bovolo; Lorenzo Bruzzone. Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2. Remote Sensing 2020, 12, 3984 .
AMA StyleMilad Niroumand-Jadidi, Francesca Bovolo, Lorenzo Bruzzone. Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2. Remote Sensing. 2020; 12 (23):3984.
Chicago/Turabian StyleMilad Niroumand-Jadidi; Francesca Bovolo; Lorenzo Bruzzone. 2020. "Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2." Remote Sensing 12, no. 23: 3984.
In the presence of abrupt change events, multitemporal synthetic aperture radar (SAR) data represent a precious supporting tool for quantifying changes, in particular in urban areas. A large amount of SAR data also exists at very high resolution (VHR). Over urban areas, the introduction of the VHR imagery moves the analysis down to the single building scale. However, VHR imagery is also characterized by a large heterogeneity and a more complex representation of the building. In this work, we propose a geometrical model for describing partially destroyed buildings and derive the corresponding multitemporal backscattering signature by applying the ray-tracing method. The model is integrated into an unsupervised automatic approach for the detection of both fully and partially destroyed buildings. The strategy considers a hierarchical structure of the changes. Experimental results conducted on two multitemporal VHR SAR datasets show a large robustness of the approach and good accuracy in the detection of the classes for damaged buildings with different severity levels.
Davide Pirrone; Francesca Bovolo; Lorenzo Bruzzone. An Approach to Unsupervised Detection of Fully and Partially Destroyed Buildings in Multitemporal VHR SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 5938 -5953.
AMA StyleDavide Pirrone, Francesca Bovolo, Lorenzo Bruzzone. An Approach to Unsupervised Detection of Fully and Partially Destroyed Buildings in Multitemporal VHR SAR Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 ():5938-5953.
Chicago/Turabian StyleDavide Pirrone; Francesca Bovolo; Lorenzo Bruzzone. 2020. "An Approach to Unsupervised Detection of Fully and Partially Destroyed Buildings in Multitemporal VHR SAR Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. : 5938-5953.
Spectrally-based retrieval of bathymetry is challenging in inland/coastal waters due to variations in factors other than water depth across a water body, including bottom type, optically significant constituents, water surface roughness, and others. Optimal band ratio analysis (OBRA) is the most widely used method to deal with these confounding effects to retrieve water depth. OBRA identifies the pair of bands that provides the highest accuracy among all possible pairs when using a log-transformed band ratio model. However, this approach fits a single ratio model to all training samples without fully accounting for the heterogeneity of the aforementioned complicating factors. To deal with this problem, we introduce a novel method called Sample-specific Multiple bAnd Ratio Technique for Satellite-Derived Bathymetry (SMART-SDB). The proposed SMART-SDB technique partitions the feature space of the spectral data and creates for every subspace a different band ratio model that performs better than the other models within that subspace. Bathymetry is then predicted based on the closest model/s in the feature space by performing a sample-specific k-nearest neighbor (K−NN) analysis. The depth estimates provided by the neighboring models are averaged with weights proportional to their inverse distance in the feature space. The proposed method can be used in both inland and coastal waters. Here, we thoroughly examine its effectiveness in the challenging and heterogenous environments of fluvial systems using a wide range of spectral data including radiative transfer simulations, an airborne hyperspectral image of the Snake River (USA), and a WorldView-3 satellite image of the Sarca River (Italy). The results of this study indicate a significant improvement in depth retrieval for estimations based on the SMART-SDB method relative to standard OBRA, either when a single model or multiple models are employed.
Milad Niroumand-Jadidi; Francesca Bovolo; Lorenzo Bruzzone. SMART-SDB: Sample-specific multiple band ratio technique for satellite-derived bathymetry. Remote Sensing of Environment 2020, 251, 112091 .
AMA StyleMilad Niroumand-Jadidi, Francesca Bovolo, Lorenzo Bruzzone. SMART-SDB: Sample-specific multiple band ratio technique for satellite-derived bathymetry. Remote Sensing of Environment. 2020; 251 ():112091.
Chicago/Turabian StyleMilad Niroumand-Jadidi; Francesca Bovolo; Lorenzo Bruzzone. 2020. "SMART-SDB: Sample-specific multiple band ratio technique for satellite-derived bathymetry." Remote Sensing of Environment 251, no. : 112091.
Crown features derived from high-density airborne laser scanning (ALS) data have proven to be effective for forest species classification at the individual tree level. Most of the general state-of-the-art (SoA) techniques rely on coarse-level crown features extracted from ALS data and under-utilize both the spatial and the spectral information available in the point clouds, Moreover, they are designed on the expected properties of the specific analyzed forest. We present a novel species classification approach, based on quantization of the entire 3-D tree crown into smaller elementary crown volumes (ECVs) that effectively captures the spatial distribution of filled (i.e., stem, branch, and foliage) and empty volumes of crowns. In the first step, a data-driven process dynamically tests and compares three quantization strategies to tailor the definition of the ECV to the forest type (e.g., conifer and deciduous forest). In the second step, for each ECV, a histogram vector is made up of features representing the light detection and ranging (LiDAR) point distribution and intensity to model the internal and the external local crown characteristics. Then, tree histogram feature vectors are obtained by stacking all the ECV histogram feature vectors. Finally, classification is performed by a support vector machine (SVM) classifier using the histogram intersection kernel. All experiments were performed on three high-density (50-200 points/m²) ALS data sets of deciduous, conifer, and mixed (i.e., both deciduous and conifer) trees. The higher classification accuracy of the proposed method over the SoA one proves its ability to better capture the crown characteristics of individual trees, including species-specific traits.
Aravind Harikumar; Claudia Paris; Francesca Bovolo; Lorenzo Bruzzone. A Crown Quantization-Based Approach to Tree-Species Classification Using High-Density Airborne Laser Scanning Data. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 4444 -4453.
AMA StyleAravind Harikumar, Claudia Paris, Francesca Bovolo, Lorenzo Bruzzone. A Crown Quantization-Based Approach to Tree-Species Classification Using High-Density Airborne Laser Scanning Data. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (5):4444-4453.
Chicago/Turabian StyleAravind Harikumar; Claudia Paris; Francesca Bovolo; Lorenzo Bruzzone. 2020. "A Crown Quantization-Based Approach to Tree-Species Classification Using High-Density Airborne Laser Scanning Data." IEEE Transactions on Geoscience and Remote Sensing 59, no. 5: 4444-4453.
The recent PlanetScope constellation (130+ satellites currently in orbit) has shifted the high spatial resolution imaging into a new era by capturing the Earth’s landmass including inland waters on a daily basis. However, studies on the aquatic-oriented applications of PlanetScope imagery are very sparse, and extensive research is still required to unlock the potentials of this new source of data. As a first fully physics-based investigation, we aim to assess the feasibility of retrieving bathymetric and water quality information from the PlanetScope imagery. The analyses are performed based on Water Color Simulator (WASI) processor in the context of a multitemporal analysis. The WASI-based radiative transfer inversion is adapted to process the PlanetScope imagery dealing with the low spectral resolution and atmospheric artifacts. The bathymetry and total suspended matter (TSM) are mapped in the relatively complex environment of Venice lagoon during two benchmark events: The coronavirus disease 2019 (COVID-19) lockdown and an extreme flood occurred in November 2019. The retrievals of TSM imply a remarkable reduction of the turbidity during the lockdown, due to the COVID-19 pandemic and capture the high values of TSM during the flood condition. The results suggest that sizable atmospheric and sun-glint artifacts should be mitigated through the physics-based inversion using the surface reflectance products of PlanetScope imagery. The physics-based inversion demonstrated high potentials in retrieving both bathymetry and TSM using the PlanetScope imagery.
Milad Niroumand-Jadidi; Francesca Bovolo; Lorenzo Bruzzone; Peter Gege. Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon. Remote Sensing 2020, 12, 2381 .
AMA StyleMilad Niroumand-Jadidi, Francesca Bovolo, Lorenzo Bruzzone, Peter Gege. Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon. Remote Sensing. 2020; 12 (15):2381.
Chicago/Turabian StyleMilad Niroumand-Jadidi; Francesca Bovolo; Lorenzo Bruzzone; Peter Gege. 2020. "Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon." Remote Sensing 12, no. 15: 2381.
Recent works highlighted the significant potential of Lung Ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID- 19 diagnostic. In this paper, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the Hidden Markov Model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the Support Vector Machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition.
Leonardo Carrer; Elena Donini; Daniele Marinelli; Massimo Zanetti; Federico Mento; Elena Torri; Andrea Smargiassi; Riccardo Inchingolo; Gino Soldati; Libertario Demi; Francesca Bovolo; Lorenzo Bruzzone. Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 2020, 67, 2207 -2217.
AMA StyleLeonardo Carrer, Elena Donini, Daniele Marinelli, Massimo Zanetti, Federico Mento, Elena Torri, Andrea Smargiassi, Riccardo Inchingolo, Gino Soldati, Libertario Demi, Francesca Bovolo, Lorenzo Bruzzone. Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2020; 67 (11):2207-2217.
Chicago/Turabian StyleLeonardo Carrer; Elena Donini; Daniele Marinelli; Massimo Zanetti; Federico Mento; Elena Torri; Andrea Smargiassi; Riccardo Inchingolo; Gino Soldati; Libertario Demi; Francesca Bovolo; Lorenzo Bruzzone. 2020. "Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data." IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control 67, no. 11: 2207-2217.
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).
Sudipan Saha; Francesca Bovolo; Lorenzo Bruzzone. Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding. IEEE Transactions on Geoscience and Remote Sensing 2020, 59, 1917 -1929.
AMA StyleSudipan Saha, Francesca Bovolo, Lorenzo Bruzzone. Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding. IEEE Transactions on Geoscience and Remote Sensing. 2020; 59 (3):1917-1929.
Chicago/Turabian StyleSudipan Saha; Francesca Bovolo; Lorenzo Bruzzone. 2020. "Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding." IEEE Transactions on Geoscience and Remote Sensing 59, no. 3: 1917-1929.
High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth's surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento.
Sudipan Saha; Lichao Mou; Chunping Qiu; Xiao Xiang Zhu; Francesca Bovolo; Lorenzo Bruzzone. Unsupervised Deep Joint Segmentation of Multitemporal High-Resolution Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 8780 -8792.
AMA StyleSudipan Saha, Lichao Mou, Chunping Qiu, Xiao Xiang Zhu, Francesca Bovolo, Lorenzo Bruzzone. Unsupervised Deep Joint Segmentation of Multitemporal High-Resolution Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (12):8780-8792.
Chicago/Turabian StyleSudipan Saha; Lichao Mou; Chunping Qiu; Xiao Xiang Zhu; Francesca Bovolo; Lorenzo Bruzzone. 2020. "Unsupervised Deep Joint Segmentation of Multitemporal High-Resolution Images." IEEE Transactions on Geoscience and Remote Sensing 58, no. 12: 8780-8792.
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.
Sudipan Saha; Yady Tatiana Solano-Correa; Francesca Bovolo; Lorenzo Bruzzone. Unsupervised Deep Transfer Learning-Based Change Detection for HR Multispectral Images. IEEE Geoscience and Remote Sensing Letters 2020, 18, 856 -860.
AMA StyleSudipan Saha, Yady Tatiana Solano-Correa, Francesca Bovolo, Lorenzo Bruzzone. Unsupervised Deep Transfer Learning-Based Change Detection for HR Multispectral Images. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (5):856-860.
Chicago/Turabian StyleSudipan Saha; Yady Tatiana Solano-Correa; Francesca Bovolo; Lorenzo Bruzzone. 2020. "Unsupervised Deep Transfer Learning-Based Change Detection for HR Multispectral Images." IEEE Geoscience and Remote Sensing Letters 18, no. 5: 856-860.
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.
Sudipan Saha; Lichao Mou; Xiao Xiang Zhu; Francesca Bovolo; Lorenzo Bruzzone. Semisupervised Change Detection Using Graph Convolutional Network. IEEE Geoscience and Remote Sensing Letters 2020, 18, 607 -611.
AMA StyleSudipan Saha, Lichao Mou, Xiao Xiang Zhu, Francesca Bovolo, Lorenzo Bruzzone. Semisupervised Change Detection Using Graph Convolutional Network. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (4):607-611.
Chicago/Turabian StyleSudipan Saha; Lichao Mou; Xiao Xiang Zhu; Francesca Bovolo; Lorenzo Bruzzone. 2020. "Semisupervised Change Detection Using Graph Convolutional Network." IEEE Geoscience and Remote Sensing Letters 18, no. 4: 607-611.
With the remarkable development of spectral unmixing, the sparse-representation-based approaches have emerged as a promising alternative. The sparse-representation-based approaches aim at finding the optimal subset of a spectral library that can optimally model each pixel of a given hyperspectral image in a semisupervised fashion. The classic sparse unmixing models are solved by the prime alternating direction method of multipliers (pADMMs). However, the computation task of pADMM is heavy and time consuming. In this letter, we design a novel dual-alternating direction method of multipliers (dADMMs) for the classic sparse unmixing models. We also present the global convergence analysis of our algorithm in some special cases. As shown in our experiments, the proposed algorithm is more effective than the state-of-the-art algorithms.
Longfei Ren; Zheng Ma; Francesca Bovolo. A Novel Dual-Alternating Direction Method of Multipliers for Spectral Unmixing. IEEE Geoscience and Remote Sensing Letters 2020, 18, 528 -532.
AMA StyleLongfei Ren, Zheng Ma, Francesca Bovolo. A Novel Dual-Alternating Direction Method of Multipliers for Spectral Unmixing. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (3):528-532.
Chicago/Turabian StyleLongfei Ren; Zheng Ma; Francesca Bovolo. 2020. "A Novel Dual-Alternating Direction Method of Multipliers for Spectral Unmixing." IEEE Geoscience and Remote Sensing Letters 18, no. 3: 528-532.
Milad Niroumand-Jadidi; Massimo Santoni; Lorenzo Bruzzone; Francesca Bovolo. Snow Cover Estimation Underneath the Clouds Based on Multitemporal Correlation Analysis in Historical Time-Series Imagery. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 5703 -5714.
AMA StyleMilad Niroumand-Jadidi, Massimo Santoni, Lorenzo Bruzzone, Francesca Bovolo. Snow Cover Estimation Underneath the Clouds Based on Multitemporal Correlation Analysis in Historical Time-Series Imagery. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (8):5703-5714.
Chicago/Turabian StyleMilad Niroumand-Jadidi; Massimo Santoni; Lorenzo Bruzzone; Francesca Bovolo. 2020. "Snow Cover Estimation Underneath the Clouds Based on Multitemporal Correlation Analysis in Historical Time-Series Imagery." IEEE Transactions on Geoscience and Remote Sensing 58, no. 8: 5703-5714.
Change detection (CD) is a crucial topic in many remote sensing applications. In the recent years, satellite polarimetric synthetic aperture radar (PolSAR) systems (e.g., the Sentinel-1 constellation) became a suitable tool for multitemporal monitoring due to the regular acquisitions with a short revisit time in different polarimetric channels. Methods for CD in PolSAR data mainly focus on binary CD (i.e., they provide information about the presence/absence of change only), whereas the polarimetric enhanced information provides multiple features that can be exploited for performing multiclass CD. In this article, we introduce a novel framework for the characterization of multitemporal changes in dual-polarimetric data. The framework is based on the definition of polarimetric change vectors (PCVs) and their representation in a polar coordinate system. PCVs allow characterizing and, thus, to separate multiclass changes in terms of target properties of the single-time scenes and the scattering theory. The proposed model is used to: 1) derive the statistical behaviors of change and no change classes in PolSAR multitemporal images; 2) design an automatic and unsupervised strategy to estimate the optimal number of changes; and 3) distinguish no change from change classes and the kinds of change from each other. An experimental analysis has been conducted on three multitemporal PolSAR data sets having different complexities in terms of number and kinds of change classes. The results confirm the effectiveness of the proposed approach and the better performance with respect to both specific techniques for CD in dual-pol SAR data and a general multiclass CD method, not designed for PolSAR data.
Davide Pirrone; Francesca Bovolo; Lorenzo Bruzzone. A Novel Framework Based on Polarimetric Change Vectors for Unsupervised Multiclass Change Detection in Dual-Pol Intensity SAR Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 4780 -4795.
AMA StyleDavide Pirrone, Francesca Bovolo, Lorenzo Bruzzone. A Novel Framework Based on Polarimetric Change Vectors for Unsupervised Multiclass Change Detection in Dual-Pol Intensity SAR Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (7):4780-4795.
Chicago/Turabian StyleDavide Pirrone; Francesca Bovolo; Lorenzo Bruzzone. 2020. "A Novel Framework Based on Polarimetric Change Vectors for Unsupervised Multiclass Change Detection in Dual-Pol Intensity SAR Images." IEEE Transactions on Geoscience and Remote Sensing 58, no. 7: 4780-4795.