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—Band selection is a common approach to reduce the data dimensionality of hyperspectral imagery. It extracts several bands of importance in some sense by taking advantage of high spectral correlation. Driven by detection or classification accuracy, one would expect that, using a subset of original bands, the accuracy is unchanged or tolerably degraded,(More)
—Principal component analysis (PCA) is widely used for spectral decorrelation in the JPEG2000 compression of hyper-spectral imagery. However, due to the data-dependent nature of principal components, the principal component transform matrix is stored in the JPEG2000 bitstream, constituting an overhead that is often negligible if the spatial size of the(More)
—Band selection is often applied to reduce the di-mensionality of hyperspectral imagery. When the desired object information is known, it can be achieved by finding the bands that contain the most object information. It is expected that these bands can provide an overall satisfactory detection and classification performance. In this letter, we propose a new(More)
—A decision fusion approach is developed to combine the results from supervised and unsupervised classifiers. The final output takes advantage of the power of a support-vector-machine-based supervised classification in class separation and the capability of an unsupervised classifier, such as K-means clustering , in reducing trivial spectral variation(More)
In this paper, we propose a joint optical flow and principal component analysis (PCA) method for motion detection. PCA is used to analyze optical flows so that major optical flows corresponding to moving objects in a local window can be better extracted. This joint approach can efficiently detect moving objects and more successfully suppress small(More)
The high dimensionality of hyperspectral imagery challenges image processing and analysis. It has been shown that hyperspectral compression can be achieved by principal component analysis (PCA) for spectral decorrelation followed by the JPEG2000-based coding. This approach, referred to as PCA+JPEG2000, provides superior rate-distortion performance and can(More)
—Band clustering is applied to dimensionality reduction of hyperspectral imagery. Different from unsupervised clustering using all the pixels or supervised clustering requiring labeled pixels, the proposed semisupervised band clustering needs class spectral signatures only. After clustering, a cluster selection step is applied to select clusters to be used(More)
Decision fusion has been used to increase classification accuracy of remotely sensed images beyond the level achieved by individual classifiers. The main reason for the use of multiple classifiers is that some classifiers may perform better in terms of accuracies for some classes, while others possibly provide better results for other classes. If the best(More)
In this paper, we show that adding a small application cost to a social assistance program can substantially improve targeting because of the self-selection it induces. We conduct a randomized experiment within Indonesia's Conditional Cash Transfer program that compares two of the most common methods of targeting welfare programs in the developing world: in(More)