1,720,991 research outputs found
One-Class Classification Of Vegetation Related Changes Via Mutual Ordering Of Normalized Differences
No full textClimate change finds one of its main causes in the happening transition between forest and arid lands of different types as a consequence of wildfires and/or massive deforestation practices. Remote sensing should provide effective and scalable solutions to monitor this dangerous trend. In this paper, a recently developed novel model for one-class classification based on abstract features constructed from normalized difference indices is presented and challenged on detecting deforestation patterns on multispectral images. Results are promising as the performance is nearly optimal, showing that the model comes with good generalization capabilities to deal with vegetation related changes in general
A Multivariate Change Vector Analysis System for Unsupervised Detection of Clear-Cuts in Sentinel-2 Time Series of the Indonesian Forest
No full textWe propose a system for detecting clear-cuts in Sentinel-2 (S-2) images of the Indonesian forest by means of an adaptive and unsupervised multivariate Change Vector Analysis (CVA) method. By leveraging on the unique spatial and spectral characteristics of the S-2 mission, the proposed method characterizes a relevant portion of the target change as lying in a Gaussian neighborhood of the spectral stacked bi-temporal domain of the change. The processing system analyzes all the available bi-temporal pairs in the time series, enabling us to: (1) partially recovering lost information due to cloud coverage, and (2) providing a representation of the change evolving in time. The system is fully automated and potentially operational ready, so it can be used to provide accurate information about clear-cuts at the country scale in Indonesia
Rayleigh-Rice Mixture Parameter Estimation via EM Algorithm for Change Detection in Multispectral Images
Full text linkThe problem of estimating the parameters of a Rayleigh-Rice mixture density is often encountered in image analysis (e.g., remote sensing and medical image processing). In this paper, we address this general problem in the framework of change detection (CD) in multitemporal and multispectral images. One widely used approach to CD in multispectral images is based on the change vector analysis. Here, the distribution of the magnitude of the difference image can be theoretically modeled by a Rayleigh-Rice mixture density. However, given the complexity of this model, in applications, a Gaussian-mixture approximation is often considered, which may affect the CD results. In this paper, we present a novel technique for parameter estimation of the Rayleigh-Rice density that is based on a specific definition of the expectation-maximization algorithm. The proposed technique, which is characterized by good theoretical properties, iteratively updates the parameters and does not depend on specific optimization routines. Several numerical experiments on synthetic data demonstrate the effectiveness of the method, which is general and can be applied to any image processing problem involving the Rayleigh-Rice mixture density. In the CD context, the Rayleigh-Rice model (which is theoretically derived) outperforms other empirical models. Experiments on real multitemporal and multispectral remote sensing images confirm the validity of the model by returning significantly higher CD accuracies than those obtained by using the state-of-the-art approaches
Time Series Change Vector Analysis for Semisupervised Abrupt Land Cover Change Detection
No full textChange detection (CD) in satellite image time series (SITS) is more complex than in bitemporal images due to the higher dimensionality of the data. Utilizing the full dimensionality of the time series remains challenging, particularly with dense SITS. An approach that can minimize dimensions without compromising informational depth is essential. In this article, we present an innovative framework for change vector analysis (CVA) in time series analysis and initial demonstrations of its effectiveness in capturing the spectral–temporal characteristics of changes. Unlike current methods, the proposed approach incorporates a wide range of spectral–temporal information and constructs separate reference matrices for each change type, facilitating an in-depth analysis of change components for CD. Based on the time series change vector (TSCV), the proposed framework extends CVA into the time series perspective, offering novel interpretations for magnitude and direction across temporal and spectral dimensions. The framework’s effectiveness is validated using Sentinel-2 data, demonstrating significant improvements in tackling multiple CD challenges in dense SITS scenarios
Time Series Directional Change Vector Analysis
No full textDetecting various types of changes in dense Satellite Image Time Series (SITS) presents a complex challenge. While Change Vector Analysis (CVA) is widely used for Change Detection (CD), it presents limitations due to a lack of prior information on changes, such as optimal spectral channels and change timing. To overcome these obstacles, the study focuses on the direction analysis of the Time Series Change Vectors (TSCV) [1], built upon CVA principles. Conducting unsupervised CD using time series magnitude information, the approach leverages multiple change dimensions in the direction analysis. A novel scheme formulates a representative change matrix within SITS temporal and spectral domains, guiding change representations and allowing segregation based on significance in both spectral and temporal dimensions. The proposed method efficacy is evaluated using Sentinel-2 time series data, with results affirming its robustness in effectively addressing multi-CD challenges within dense SITS
Instantaneous infrastructure monitoring by Earth observation: SAR-based railway obstacle detection
No full textIn the framework of the Horizon-Europe project “Instantaneous Infrastructure Monitoring by Earth Observation (IIMEO)” the objective is to design, implement and demonstrate key technological factors of a future satellite-based Earth Observation (EO) system capable of providing functions necessary for instantaneous monitoring of infrastructures in near real time. The system will implement a tiled acquisition of multitemporal SAR images over a railway infrastructure and perform near real-time change-obstacle detection at every new acquisition within one hour after the satellite passes over the area. The tile-based obstacle change-detection multitemporal system is explained in detail
Edge-crease detection and surface reconstruction from point clouds using a second-order variational model
No full textThe automatic detection of geometric features, such as edges and creases, from objects represented by 3D point clouds (e.g., LiDAR measurements, Tomographic SAR) is a very important issue in different application domains including urban monitoring and building reconstruction. A limitation of many methods in the literature is that they rely on rasterization or interpolation of the original grid, with consequent potential loss of detail. Recently, a second-order variational model for edge and crease detection and surface regularization has been presented in literature and succesfully applied to DSMs. In this paper we address the generalization of this model to unstructured grids. The model is based on the Blake-Zisserman energy and allows to obtain a regularization of the original data (noise reduction) which does not affect crucial regions containing jumps and creases. Specifically, we focus on the detection of these features by means of two auxiliary functions that are computable by solving specific differential equations. Results obtained on LiDAR data by solving the equations via Finite Element Method are presented
A theoretical framework for unsupervised land cover change detection in dense satellite image time series
No full textThis paper addresses the complex task of detecting and characterizing changes in dense Satellite Image Time Series (SITS). Although Change Vector Analysis (CVA) is widely used for Change Detection (CD), it has limitations due to missing prior information on changes, such as: optimal spectral channels and change timing. Time series data can help overcome these limitations, but working with them is challenging. To address these challenges, the paper introduces a novel framework called Time Series Change Vector Analysis (TSCVA), which builds upon the principles of CVA. In TSCVA, the paper redefines CVA in the time series feature space and introduces new definitions for change in time series magnitude and direction. This allows for a detailed analysis of change components in the time and spectrum domain within the SITS, enabling unsupervised CD. We utilize the expectation-maximization algorithm to estimate parameters of statistical distributions for change and no change classes. The effectiveness of the proposed TSCVA method is evaluated using Sentinel-2 time series data. The results, both quantitative and qualitative, confirm the robustness of this approach in effectively addressing the CD problem in dense SITS
A generalized statistical model for binary change detection in multispectral images
No full textRecently, a thresholding method based on the RayleighRice mixture has been proposed for solving binary change detection problems in multispectral image pairs. However, when images acquired by the last generation of multispectral scanners having high radiometric resolution are considered, the distribution fitting is still not satisfactory and computed thresholds remain quite distant from the optimal values. The main reason for this seems to be that in all previous approaches the unchange class is modeled as a single class. Instead, both practice and recent studies showed that this is not the case for new generation data. In this work, we propose a generalized statistical model for the difference image that allows the unchange class to be complex. The resulting model has more degrees of freedom, therefore it better fits real distributions and returns almost optimal
thresholds for binary decision also with high radiometric resolution images
Piecewise linear approximation of vector-valued images and curves via second-order variational model
No full textVariational models are known to work well for addressing image restoration/regularization problems. However, most of the methods proposed in literature are defined for scalar inputs and are used on multiband images (such as RGB or multispectral imagery) by the composition of a simple band-wise processing. This involves suboptimal results and may introduce artifacts. Only in a few cases variational models are extended to the case of vector-valued inputs. However, the known implementations are restricted to 1st-order models, while 2nd-order models are never considered. Thus, typical problems of 1st-order models such as the staircasing effect cannot be overtaken. This paper considers a 2nd-order functional model to function approximation with free discontinuities given by Blake-Zisserman (BZ) and proposes an efficient minimization algorithm in the case of vector-valued inputs. In the BZ model, the Hessian of the solution is penalized outside a set of finite length, therefore the solution is forced to be piecewise linear. Moreover, the model allows the formation of free discontinuities and free gradient discontinuities. The proposed algorithm is applied to difficult color image restoration/regularization problems and to piecewise linear approximation of curves in space
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