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The 7th Workshop on Pattern Recognition in Remote Sensing (PRRS 2012) was successfully held on November 11, 2012 in Tsukuba Science City, Japan, in conjunction with the 2012 International Conference on Pattern Recognition (ICPR 2012). It is organized by the Technical Committee 7 (Remote Sensing and Mapping) of the International Association for Pattern Recognition (IAPR), and co-sponsored by IAPR and...
This paper considers a GPU implementation of a two-dimensional infinite impulse-response filter. The presented flexible allocation approach makes it possible to efficiently implement the polytope model in a single GPU kernel. Some theoretical performance estimations of the proposed flexible allocation algorithm are given in the paper. The proposed IIR filtering technique is efficient when applied...
When dealing with change detection problems, information about the nature of the changes is often unavailable. In this paper we propose a solution to perform unsupervised change detection based on nonlinear support vector clustering. We build a series of nested hierarchical support vector clustering descriptions, select the appropriate one using a cluster validity measure and finally merge the clusters...
Band clustering is applied to dimensionality reduction of hyperspectral imagery. The proposed method is based on a hierarchical clustering structure, which aims to group bands using an information or similarity measure. Specifically, the distance based on orthogonal projection divergence (OPD) is used as a criterion for clustering. Moreover, different from unsupervised clustering using all the pixels...
Modern spaceborne SAR sensors like TerraSAR-X offer ground resolutions of about one meter in range and azimuth direction which allows for the discrimination between different facade elements. Those objects often feature a trihedral structure, which leads to a strong radar response due to triple-bounce reflection. The resulting radar signal is frequently observed to be temporally stable and usually...
In this paper, a new metric called despeckling structural loss(DSL) is proposed for performance assessment of despeckling algorithms with a focus on the preservation of structural information. By taking into account characteristics of the best and worst structure preservation in despeckling, the DSL metric examines the presence of image structures in ratio images by using local correlations between...
In this paper, we investigate the performance of a sparsity-preserving graph embedding based approach, called l1 graph, in hyperspectral image dimensionality reduction (DR), and propose noise-adjusted sparsity-preserving (NASP) based DR when training samples are unavailable. In conjunction with the state-of-the-art hyperspectral image classifier, support vector machine with composite kernels (SVM-CK),...
The objective of this study is to develop high accuracy land cover classification algorithm for Global scale by using multi-temporal MODIS land reflectance products. In this study, time-domain co-occurrence matrix was introduced as a classification feature which provides time-series signature of land covers. Further, the non-parametric minimum distance classifier was introduced for time-domain co-occurrence...
We apply Conditional Random Fields for the classification of scenes containing crossroads, using a simple appearance-based model in combination with a probabilistic model of the co-occurrence of class labels at neighbouring image sites. We use multiple overlap aerial images to derive a digital surface model and a true orthophoto without dynamic objects such as cars. An evaluation on an urban data...
Shoreline extraction algorithms from multispectral imagery depend on threshold selection over spectral values and segmentation in general. Although this method gives high performance values for water delineation, error is accumulated on pixels near shoreline and complicates detection of nearby ships, docks etc. Water-shadow spectral mixing and spectral difference in water regions are two of the reasons...
In this paper, we explore hyperspectral feature extraction using the contourlet transform (CT), a promising multireolution analysis technique emerging in recent years. Hyperspectral imagery is first processed in the spectral domain with some decorrelation techniques. Then the nonsubsampled CT (NSCT) is applied in the spatial domain. The resulting NSCT coefficients are used as features for hyperspectral...
Some volcanoes show a kind of periodicity through the time series of volcano temperature. Analyzing the satellite data observed in 2012 from 2006, there are found six volcanoes in which the high temperature occurs in a periodic interval from half year to two years. The activity pattern appears like some kind of pulse train. There is a possibility of the periodical event which occurs according to the...
The purpose of this study is to analyze the distribution and the classification of gravel bar along the shore of Balkhash Lake by using the high precision DSM newly produced from the stereo pair data of PRISM carried in ALOS, and to consider the age and environment of formation of every classified gravel bar, and the long-term environmental change based on water level fluctuation of the lake. The...
Gestalt-laws such as good continuation and similarity are coded as production systems. Applied to aerial images such systems can automatically perform grouping inferences following the archetype of human perception. Two variants are investigated on the same data: 1) Using only the geometrical gestalt laws on short contour line objects; 2) using also color close to the contours from the NIR images...
In this work we present the enrichment of the Prague texture segmentation data-generator and benchmark (PTSDB) also for the assessment of the remote sensing image segmenters. The PTSDB tool is a web based (http://mosaic.utia.cas.cz) service designed for real-time performance evaluation, mutual comparison, and ranking of various supervised or unsupervised static or dynamic image segmenters. PTSDB supports...
This article focuses on remote sensing image fusion in order to improve target recognition performance. Current fusion algorithms are mostly designed for specific purpose and have exponential complexity. We propose a fast and robust image fusion algorithm—the iterative learning fusion (ILF) algorithm, to improve the quality of images. This algorithm combines iterative learning in control theory with...
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