Search results for "change detection"
showing 10 items of 68 documents
Semiautomatic Behavioral Change-Point Detection: A Case Study Analyzing Children Interactions With a Social Agent
2021
The study of human behaviors in cognitive sciences provides clues to understand and describe people’s personal and interpersonal functioning. In particular, the temporal analysis of behavioral dynamics can be a powerful tool to reveal events, correlations and causalities but also to discover abnormal behaviors. However, the annotation of these dynamics can be expensive in terms of temporal and human resources. To tackle this challenge, this paper proposes a methodology to semi-automatically annotate behavioral data. Behavioral dynamics can be expressed as sequences of simple dynamical processes: transitions between such processes are generally known as change-points. This paper describes th…
Improved locally adaptive least-squares detection of differences in images
2007
We introduce a method for change detection under nonuniform changes of intensity using an improved least-squares method. A locally adaptive normalizing window is correlated with the two images, and a morphological postprocessing is then applied to isolate objects that have been added or removed from the scene. We use a modification of the least-squares solution to get rid of clutter caused by intensity changes that do not satisfy the model assumed for the least-squares solution.
Unsupervised change detection with kernels
2012
In this paper an unsupervised approach to change detection relying on kernels is introduced. Kernel based clustering is used to partition a selected subset of pixels representing both changed and unchanged areas. Once the optimal clustering is obtained the estimated representatives (centroids) of each group are used to assign the class membership to all others pixels composing the multitemporal scenes. Different approaches of considering the multitemporal information are considered with accent on the computation of the difference image directly in the feature spaces. For this purpose a difference kernel approach is successfully adopted. Finally an effective way to cope with the estimation o…
Interannual vineyard crop variability in the Castilla–La Mancha region during the period 1991–1996 with Landsat Thematic Mapper images
2004
The vineyard crop is considered an indicator of vegetation cover processes in the Castilla–La Mancha region, as the crop has undergone far-reaching changes in the last ten years: abandonment, removal of vineyards and replacement with other crops such as cereal. The so-called ‘Change detection’ is a process that allows identification of differences in the state of the vineyard by observing it at different times. Essentially, it involves the ability to quantify temporal effects using multi-temporal datasets. The aim of this study is to analyse the vineyard variability during the period 1991–1996 using different Landsat-5 Thematic Mapper (TM) images belonging to an identified period that highl…
Drowned Landscapes: The Rediscovered Archaeological Heritage of the Mosul Dam Reservoir
2023
Like natural catastrophes or armed conflicts, resource extraction projects herald the alteration or destruction of natural and cultural landscapes alike. Dam construction is a major threat to cultural heritage in Western Asian archaeology. One event may result in obliterating hundreds of sites, most of which never reappear or do so only sporadically following cyclical water fluctuation. Destruction of sites remains ongoing, necessitating constant assessment of damage and the establishment of strategies of documentation and maintenance. This paper proposes a new paradigm for future safeguarding and, more widely, a new tool for managing contiguous terrestrial and lacustrine cultural zones. It…
An alternative simple approach to estimate atmospheric correction in multitemporal studies
1989
Abstract Studies that use multitemporal images require the conversion of original digital data into the corresponding physical magnitudes. Atmospheric correction is one of the most important steps in this process, which is usually undertaken using atmospheric radiative transfer models. The main difficulty in these models is the need of atmospheric input data which are not usually available. An alternative approach to atmospheric correction is proposed in this Letter. It is based on the idea that the atmospheric effects over two or more dates can be determined in a relative way, by using the apparent reflectance values of surfaces whose ground reflectance can be considered unchangeable with …
Cloud masking and removal in remote sensing image time series
2017
Automatic cloud masking of Earth observation images is one of the first required steps in optical remote sensing data processing since the operational use and product generation from satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions and the scarcity of labeled data force us to cast cloud screening as an unsupervised change detection problem in the temporal domain. We introduce a cloud screening method based on detecting abrupt changes along the time dimension. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes will be mainly due to the presence of clo…
Nonlinear Cook distance for Anomalous Change Detection
2020
In this work we propose a method to find anomalous changes in remote sensing images based on the chronochrome approach. A regressor between images is used to discover the most {\em influential points} in the observed data. Typically, the pixels with largest residuals are decided to be anomalous changes. In order to find the anomalous pixels we consider the Cook distance and propose its nonlinear extension using random Fourier features as an efficient nonlinear measure of impact. Good empirical performance is shown over different multispectral images both visually and quantitatively evaluated with ROC curves.
Unsupervised Anomaly and Change Detection With Multivariate Gaussianization
2022
Anomaly detection (AD) is a field of intense research in remote sensing (RS) image processing. Identifying low probability events in RS images is a challenging problem given the high dimensionality of the data, especially when no (or little) information about the anomaly is available a priori. While a plenty of methods are available, the vast majority of them do not scale well to large datasets and require the choice of some (very often critical) hyperparameters. Therefore, unsupervised and computationally efficient detection methods become strictly necessary, especially now with the data deluge problem. In this article, we propose an unsupervised method for detecting anomalies and changes …
Kernel Anomalous Change Detection for Remote Sensing Imagery
2020
Anomalous change detection (ACD) is an important problem in remote sensing image processing. Detecting not only pervasive but also anomalous or extreme changes has many applications for which methodologies are available. This paper introduces a nonlinear extension of a full family of anomalous change detectors. In particular, we focus on algorithms that utilize Gaussian and elliptically contoured (EC) distribution and extend them to their nonlinear counterparts based on the theory of reproducing kernels' Hilbert space. We illustrate the performance of the kernel methods introduced in both pervasive and ACD problems with real and simulated changes in multispectral and hyperspectral imagery w…