This paper describes a simulated annealing based multiobjective optimization algorithm that incorporates the concept of archive in order to provide a set of tradeoff solutions for the problem under consideration. To determine the acceptance probability of a new solution vis-a-vis the current solution, an elaborate procedure is followed that takes into account the domination status of the new solution with the current solution, as well as those in the archive. A measure of the amount of domination between two solutions is also used for this purpose. A complexity analysis of the proposed algorithm is provided. An extensive comparative study of the proposed algorithm with two other existing and well-known multiobjective evolutionary algorithms (MOEAs) demonstrate the effectiveness of the former with respect to five existing performance measures, and several test problems of varying degrees of difficulty. In particular, the proposed algorithm is found to be significantly superior for many objective test problems (e.g., 4, 5, 10, and 15 objective problems), while recent studies have indicated that the Pareto ranking-based MOEAs perform poorly for such problems. In a part of the investigation, comparison of the real-coded version of the proposed algorithm is conducted with a very recent multiobjective simulated annealing algorithm, where the performance of the former is found to be generally superior to that of the latter.

http://www.isical.ac.in/~sriparna_r/ieeetec.pdf

A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA

Dual-season mapping of wetland inundation and vegetation for the central Amazon basin

Characterizations of land-cover dynamics are among the most important applications of Earth observation data, providing insights into management, policy and science. Recent progress in remote sensing and associated digital image processing offers unprecedented opportunities to detect changes in land cover more accurately over increasingly large areas, with diminishing costs and processing time. The advent of high-spatial-resolution remote-sensing imagery further provides opportunities to apply change detection with object-based image analysis (OBIA), that is, object-based change detection (OBCD). When compared with the traditional pixel-based change paradigm, OBCD has the ability to improve the identification of changes for the geographic entities found over a given landscape. In this article, we present an overview of the main issues in change detection, followed by the motivations for using OBCD as compared to pixel-based approaches. We also discuss the challenges caused by the use of objects in change ...

https://www.cfs.nrcan.gc.ca/pubwarehouse/pdfs/33144.pdf

Object-based change detection

An algorithm has been developed for mapping wet snow in mountainous terrain using repeat pass synthetic aperture radar (SAR) images. As a basis for algorithm development, backscattering properties of snow-free and snow-covered alpine surfaces were investigated using ERS SAR data and field measurements at test sites in the Austrian Alps. The incidence angle dependence of backscattering derived from SAR data is compared with simulations for snow-free surfaces and for surfaces covered by dry snow and wet snow. Significant seasonal changes of backscattering are observed, which are mainly caused by variations of the snow liquid water content and of the surface roughness. The importance of surface roughness for backscattering of wet snow is demonstrated by a surface roughness experiment. The algorithm for mapping wet snow applies change detection using ratios of wet snow versus snow-free or dry snow surfaces. The main steps include coregistration, speckle reduction, thresholding of ratio images, geocoding, and, optionally, combination of crossing passes to reduce the loss of information due to layover. A threshold of -3 dB was found to be appropriate for both Radarsat and ERS SAR to separate wet snow from other surfaces. Postprocessing steps, based on historic snow maps or topographic information, are used to correct for dry snow areas at high elevations. Effects of imaging geometry are investigated by comparing ERS SAR images with a look angle of 19/spl deg/ and Radarsat SAR Beam Mode S7 images with a look angle of 40/spl deg/. The comparison of snow maps from SAR and Landsat-5 Thematic Mapper images shows good agreement in areas of closed snow cover, whereas near the snow line/SAR tends to slightly underestimate the snow extent.

Retrieval of wet snow by means of multitemporal SAR data

The present article is an introductory paper in this special issue on synthetic aperture radar (SAR). A short review is presented on the recent trend and development of SAR and related techniques with selected topics, including the fields of applications, specifications of airborne and spaceborne SARs, and information contents in and interpretations of amplitude data, interferometric SAR (InSAR) data, and polarimetric SAR (PolSAR) data. The review is by no means extensive, and as such only brief summaries of of each selected topics and key references are provided. For further details, the readers are recommended to read the literature given in the references theirin.

Recent Trend and Advance of Synthetic Aperture Radar with Selected Topics

Methods to evaluate the performance of segmentation algorithms for synthetic aperture radar (SAR) images are developed, based on known properties of coherent speckle and a scene model in which areas of constant backscatter coefficient are separated by abrupt edges. Local and global measures of segmentation homogeneity are derived and applied to the outputs of two segmentation algorithms developed for SAR data, one based on iterative edge detection and segment growing, the other based on global maximum a posteriori (MAP) estimation using simulated annealing. The quantitative statistically based measures appear consistent with visual impressions of the relative quality of the segmentations produced by the two algorithms. On simulated data meeting algorithm assumptions, both algorithms performed well but MAP methods appeared visually and measurably better. On real data, MAP estimation was markedly the better method and retained performance comparable to that on simulated data, while the performance of the other algorithm deteriorated sharply. Improvements in the performance measures will require a more realistic scene model and techniques to recognize oversegmentation.

Quantitative comparison of the performance of SAR segmentation algorithms

The feasibility of matching templates representing map features to features in synthetic aperture radar images is investigated. The performance of template matching using cross-correlation (which has no knowledge of speckle) is compared with a correlation measure, based on a Kolmogorov-Smirnov test of fit, which does take speckle into consideration. This is shown to perform better than cross-correlation in simulated images. However, neither method performs well on real data. This is due to the exponential speckle model being inappropriate for linear features in real data. Alternative methods for matching map features to SAR images are suggested. >

Matching map features to synthetic aperture radar (SAR) images using template matching

Extensions of three single-channel segmentation algorithms to multi-channel operation are described. Algorithm performance is illustrated using multi-temporal ERS-1 images of an agricultural scene. It is shown that full multi-channel segmentation performed better than an approach based on segmenting channels separately and then recombining information. A multi-channel segmentor (RWSEG) based on segment growing constrained by edge detection was shown to perform better than a multichannel segmentor based on segment merging (MUM). However, in terms of preserving small scale detail neither preformed as well as a multi-channel filter (GAMANN) based on simulated annealing. It is illustrated how multi-channel segmentation may be used for identifying structural change and measuring radiometric change. With RWSEG, the choice of an RMS measure for combining information from different channels into a single parameter is somewhat arbitrary, its primary attraction being simplicity. More optimal methods probably exist and should be investigated.

Multi-channel SAR segmentation: algorithms and applications

Agricultural areas in low resolution AirSAR polarimetric data exhibit K distributed statistics. The order parameter of the K distribution shows considerable variation with frequency and vegetation type. At C-band, all vegetation types give large order parameters, so that they are essentially gaussian, but order parameters decrease with frequency, markedly so for cereal crops. Coniferous woodland is gaussian at all frequencies. Root crops show intermediate behaviour, being better fitted by K distributions whose order parameters are not as low as those of cereals. These results indicate that five parameters are sufficient to describe distributed agricultural targets at higher frequencies, but an extra parameter is available at lower frequencies. >

Statistical models for polarimetric SAR data

The CEO HYDALP project aims to use Earth Observation data to improve the monitoring and forecasting of snowmelt runoff from alpine and high latitude basins, and to prepare a basis for the operational use of this information. A crucial parameter for snowmelt runoff modelling is the snow covered area. Methods have previously been developed for mapping snow cover in alpine basins using medium and high resolution optical data and for mapping wet snow using synthetic aperture radar (SAR) data. An objective of HYDALP is to determine the applicability of these methods to high latitude basins. This paper discusses the applicability of the SAR wet snow mapping method to high latitude basins and highlights differences with respect to alpine areas. The authors wish to determine: a) If wet snow causes a detectable change in backscatter as compared to no or dry snow cover. b) What the detection and false alarm rates are. c) What other dynamic changes occur within the image and whether these could be mistaken for wet snow. d) How the method is affected by topography and whether ascending and descending pass image combinations are needed to reduce these effects.

R.G. Caves

Papers

Quantitative comparison of the performance of SAR segmentation algorithms

Matching map features to synthetic aperture radar (SAR) images using template matching

Multi-channel SAR segmentation: algorithms and applications

Statistical models for polarimetric SAR data

The role of Earth Observation in snowmelt runoff monitoring from high latitude basins: SAR aspects