Unsupervised classification of changes in multispectral satellite imagery

The statistical techniques of multivariate alteration detection, maximum autocorrelation factor transformation, expectation maximization, fuzzy maximum likelihood estimation and probabilistic label relaxation are combined in a unified scheme to classify changes in multispectral satellite data. An example involving bitemporal LANDSAT TM imagery is given

Visualization of and Software for Omnibus Test Based Change Detected in a Time Series of Polarimetric SAR Data

Based on an omnibus likelihood ratio test statistic for the equality of several variance-covariance matrices following the complex Wishart distribution and a factorization of this test statistic with associated p-values, change analysis in a time series of multilook polarimetric synthetic aperture radar data in the covariance matrix representation is carried out. The omnibus test statistic and its factorization detect if and when change occurs. Using airborne EMISAR and spaceborne RADARSAT-2 data, this article focuses on change detection based on the p-values, on visualization of change at pixel as well as segment level, and on computer software

Automatic Radiometric Normalization of Multitemporal Satellite Imagery

The linear scale invariance of the multivariate alteration detection (MAD) transformation is used to obtain invariant pixels for automatic relative radiometric normalization of time series of multispectral data. Normalization by means of ordinary least squares regression method is compared with normalization using orthogonal regression. The procedure is applied to Landsat TM images over Nevada, Landsat ETM+ images over Morocco, and SPOT HRV images over Kenya. Results from this new automatic, combined MAD/orthogonal regression method, based on statistical analysis of test pixels not used in the actual normalization, compare favorably with results from normalization from manually obtained time-invariant features. (C) 2004 Elsevier Inc. All rights reserved

Short-Term Change Detection in Wetlands Using Sentinel-1 Time Series

Automated monitoring systems that can capture wetlands’ high spatial and temporal variability are essential for their management. SAR-based change detection approaches offer a great opportunity to enhance our understanding of complex and dynamic ecosystems. We test a recently-developed time series change detection approach (S1-omnibus) using Sentinel-1 imagery of two wetlands with different ecological characteristics; a seasonal isolated wetland in southern Spain and a coastal wetland in the south of France. We test the S1-omnibus method against a commonly-used pairwise comparison of consecutive images to demonstrate its advantages. Additionally, we compare it with a pairwise change detection method using a subset of consecutive Landsat images for the same period of time. The results show how S1-omnibus is capable of capturing in space and time changes produced by water surface dynamics, as well as by agricultural practices, whether they are sudden changes, as well as gradual. S1-omnibus is capable of detecting a wider array of short-term changes than when using consecutive pairs of Sentinel-1 images. When compared to the Landsat-based change detection method, both show an overall good agreement, although certain landscape changes are detected only by either the Landsat-based or the S1-omnibus method. The S1-omnibus method shows a great potential for an automated monitoring of short time changes and accurate delineation of areas of high variability and of slow and gradual changes

Emulating simula in turbo pascal

Although the computer language SIMULA /1,2/ is now over 20 years old it remainsan excellent general purpose programming tool as well as a popular and powerfufsimulation language, the purpose for which it was originally developed. SIMULA isan extension of ALGOL 60, which it contains as a true subset, and its advancedconcepts have served as a model for modern object-oriented languages such asSMALLTALK.The properties which make SIMULA especially suitable for simulation tasks are1. a hierarchic cfass concept with inheritance,2. sophisticated list handfing facilities and3. concurrent programming capability.Of these three, the most essential property to allow for programming of discrete timesimulation tasks is the third one, concurrent programming. By this is meant theability to sustain parallel autonomous entities (called processes or co-routines) inmemory. Allowing an arbitrary number of such processes to interact with each otheralong a time axis forms the basis of SIMULA's model for discrete time simulation.Although virtually all major programming languages have been implemented in oneform or another on personal computers, SIMULA is a notable exception. Perhaps themain reason for this is that, while enjoying great popularity in Europe, SIMULA isnot as well known on the North American continent. Pascal belongs to the sameAlgol famify as SIMULA, and the dialect Turbo Pascal of the firm Borland Internationalhas become one bf the most wide spread high-levef languages for MS-DOSpersonal computers. Unfortunately, neither the ANSI-Pascal specification norTurbo-Pascal in particular allow for concurrent programming.A recent articfe in BYTE by Krishnamoorthy and Agnarsson /3/ presented an extensionto Turbo Pascal 3.0 which enables the creation of parallel processes. In the present report, their extension is modified for the latest version (4.0) ofTurbo Pascal/4/ and integrated into a Turbo Pascal unit (pre-compiled module) which emulatesthe elementary simulation constructs of the SIMULA language. A simple applicationillustrating the use of the unit is provided