1,721,310 research outputs found
Combination of Neural and Statistical Algorithms for Supervised Classification of Remote-Sensing Images
No full textVarious experimental comparisons of algorithms for supervised classification of remote-sensing images have been reported in the literature. Among others, a comparison of neural and statistical classifiers has previously been made by the authors in (Serpico, S.B., Bruzzone, L., Roli, F., 1996. Pattern Recognition Letters 17, 1331–1341). Results of reported experiments have clearly shown that the superiority of one algorithm over another cannot be claimed. In addition, they have pointed out that statistical and neural algorithms often require expensive design phases to attain high classification accuracy. In this paper, the combination of neural and statistical algorithms is proposed as a method to obtain high accuracy values after much shorter design phases and to improve the accuracy–rejection tradeoff over those allowed by single algorithms
Proceedings of the 2011 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images (Multi-Temp)
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A robust nonlinear scale space change detection approach for SAR images
Full text linkIn this paper, we propose a change detection approach based on nonlinear scale space analysis of change images for robust detection of various changes incurred by natural phenomena and/or human activities in Synthetic Aperture Radar (SAR) images using Maximally Stable Extremal Regions (MSERs). To achieve this, a variant of the log-ratio image of multitemporal images is calculated which is followed by Feature Preserving Despeckling (FPD) to generate nonlinear scale space images exhibiting different trade-offs in terms of speckle reduction and shape detail preservation. MSERs of each scale space image are found and then combined through a decision level fusion strategy, namely "selective scale fusion" (SSF), where contrast and boundary curvature of each MSER are considered. The performance of the proposed method is evaluated using real multitemporal high resolution TerraSAR-X images and synthetically generated multitemporal images composed of shapes with several orientations, sizes, and backscatter amplitude levels representing a variety of possible signatures of change. One of the main outcomes of this approach is that different objects having different sizes and levels of contrast with their surroundings appear as stable regions at different scale space images thus the fusion of results from scale space images yields a good overall performance
Experimental analysis of the use of grey level co-occurrence statistics for SAR-image classification
No full textAn experimental analysis of the effectiveness of Gray-Level Co-Occurrence (GLC) statistics to characterize texture of SAR images for classification purposes is reported. The analysis was carried out on simulated SAR images
Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data
No full textThe identification of tree species is an important issue in forest management. In recent years, many studies have explored this topic using hyperspectral, multispectral, and LiDAR data. In this study we analyzed two multi-sensor set-ups: 1) airborne high spatial resolution hyperspectral images combined with LiDAR data; and 2) high spatial resolution satellite multispectral images combined with LiDAR data. Two LiDAR acquisitions were considered: low point density (approx. 0.48 points per m2) and high point density (approx. 8.6 points per m2). The aims of this work were: i) to understand what level of classification accuracy can be achieved using a high spectral and spatial resolution multi-sensor data set-up (very high spatial and spectral resolution airborne hyperspectral images integrated with high point density LiDAR data), over a mountain
area characterized by many species, both broadleaf and coniferous; ii) to understand the implications of a downgrading of the data characteristics (in terms of spectral resolution of spectral data and point density
of LiDAR data), on species separability, with respect to the previous set-up; and iii) to understand the differences between high- and low-point density LiDAR acquisitions on tree species classification. The study region was a mountain area in the Southern Alps characterized by many tree species (7 species and a “non-forest” class), either coniferous or broadleaf. For each set-up a specific processing chain was adopted, from the pre-processing of the raw data to the classification (two classifiers were used: support vector machine and random forest). Different class definitions were tested, including general macro-classes, forest types, and finally single tree species. Experimental results showed that the set-up based on hyperspectral data was effective with general macro-classes, forest types, and single species, reaching high kappa accuracies (93.2%, 82.1% and 76.5%, respectively). The use of multispectral data produced a reduction in the classification accuracy, which was sharp for single tree species, and still high for forest types. Considering general macro-classes, the multispectral set-up was still very accurate (85.8%). Regarding LiDAR data, the experimental analysis showed that high density LiDAR data provided more information for tree species classification with respect
to low density data, when combined with either hyperspectral or multispectral data
A simple upper bound to the Bayes error probability for feature selection
Full text linksummary:In this paper, feature selection in multiclass cases for classification of remote-sensing images is addressed. A criterion based on a simple upper bound to the error probability of the Bayes classifier for the minimum error is proposed. This criterion has the advantage of selecting features having a link with the error probability with a low computational load. Experiments have been carried out in order to compare the performances provided by the proposed criterion with the ones of some of the widely used feature-selection criteria presented in the remote-sensing literature. These experiments confirm the effectiveness of the proposed criterion, which performs slightly better than all the others considered in the paper
Data fusion experience:from industrial visual inspection to space remote-sensing applications
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