4,445 research outputs found
Multilevel Image Segmentation Based on Fractional-Order Darwinian Particle Swarm Optimization
Hyperspectral remote sensing images contain hundreds of data channels. Due to the high dimensionality of the hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for such imagery. In this paper, a new multilevel thresholding method is introduced for the segmentation of hyperspectral and multispectral images. The new method is based on fractional-order Darwinian particle swarm optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. In this paper, the so-called Otsu problem is solved for each channel of the multispectral and hyperspectral data. Therefore, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results are favorable for the FODPSO when compared to other bioinspired methods for multilevel segmentation of multispectral and hyperspectral images. The FODPSO presents a statistically significant improvement in terms of both CPU time and fitness value, i.e., the approach is able to find the optimal set of thresholds with a larger between-class variance in less computational time than the other approaches. In addition, a new classification approach based on support vector machine (SVM) and FODPSO is introduced in this paper. Results confirm that the new segmentation method is able to improve upon results obtained with the standard SVM in terms of classification accuracies.Sponsored by: IEEE Geoscience and Remote Sensing SocietyRitrýnt tímaritPeer reviewedPre prin
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods
Hyperspectral images show similar statistical properties to natural grayscale
or color photographic images. However, the classification of hyperspectral
images is more challenging because of the very high dimensionality of the
pixels and the small number of labeled examples typically available for
learning. These peculiarities lead to particular signal processing problems,
mainly characterized by indetermination and complex manifolds. The framework of
statistical learning has gained popularity in the last decade. New methods have
been presented to account for the spatial homogeneity of images, to include
user's interaction via active learning, to take advantage of the manifold
structure with semisupervised learning, to extract and encode invariances, or
to adapt classifiers and image representations to unseen yet similar scenes.
This tutuorial reviews the main advances for hyperspectral remote sensing image
classification through illustrative examples.Comment: IEEE Signal Processing Magazine, 201
Programmable Spectrometry -- Per-pixel Classification of Materials using Learned Spectral Filters
Many materials have distinct spectral profiles. This facilitates estimation
of the material composition of a scene at each pixel by first acquiring its
hyperspectral image, and subsequently filtering it using a bank of spectral
profiles. This process is inherently wasteful since only a set of linear
projections of the acquired measurements contribute to the classification task.
We propose a novel programmable camera that is capable of producing images of a
scene with an arbitrary spectral filter. We use this camera to optically
implement the spectral filtering of the scene's hyperspectral image with the
bank of spectral profiles needed to perform per-pixel material classification.
This provides gains both in terms of acquisition speed --- since only the
relevant measurements are acquired --- and in signal-to-noise ratio --- since
we invariably avoid narrowband filters that are light inefficient. Given
training data, we use a range of classical and modern techniques including SVMs
and neural networks to identify the bank of spectral profiles that facilitate
material classification. We verify the method in simulations on standard
datasets as well as real data using a lab prototype of the camera
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