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This paper presents a modification to the cNMF for unmixing where the image is first segmented and the cNMF is applied to individual segments for endmember extraction. Extracted spectral endmembers from individual segments are clustered in endmember classes to describe the entire image. The approach is compared with the global cNMF. The segmentation-based cNMF better captures subtle differences between...
Individual tree-based species maps are valuable for sustainable forest practices from both economic and ecological perspectives. Recent advances in high spatial resolution remote sensing provide the opportunity to map trees species with greater resolution and accuracy. This study aims to classify tree species at the individual tree level by using multi-seasonal WorldView-3 images. Our study site is...
When performing point target detection in hyperspectral imagery, one often uses the spectral inverse covariance matrix to whiten the natural noise of the image. Since the cube is not necessarily stationary, we wish to understand when segmentation is worthwhile to provide different covariance matrices for different areas of the cube. Using simulations and several new analytical tools, we propose general...
Classification of remotely sensed data is an important task for many practical applications. However, it is not always possible to get the ground truth for supervised learning methods. Thus unsupervised methods form a valuable tool in such situations. Such methods are referred to as clustering methods. There exists several strategies for clustering the given data — K-means, density based methods,...
While Golomb-Rice codes are optimal for geometrically distributed source, the practically achievable coding efficiency depends on the accuracy of the coding parameter estimated from the input data. Most existing methods are based on the assumption of geometric distribution and thus would suffer from a loss in coding efficiency if the underlying distribution deviates from the geometric distribution,...
In this paper, a novel spectral-spatial low-rank subspace clustering (SS-LRSC) algorithm is presented for clustering of hyperspectral images (HSI). Generally, employing the traditional LRSC framework directly cannot fully exploit the sample correlations in original spatial domain. Therefore, the proposed method utilizes a novel modulation strategy to modify the low rank representation matrix, which...
A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral version of the SLIC superpixel algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership Latent Dirichlet Allocation (sPM-LDA) to obtain a final segmentation. The proposed method is applied to two...
Superpixel has been widely applied in hyperspectral image processing as a pre-processing step for over-segmentation. However, most superpixel algorithms are difficult to control the segmentation balance between fragmentation and accuracy. In this paper, we propose a superpixel aggregation model to cluster the over-segmentations. Based on the own importance and interrelationship of superpixels, a two-step...
We formulate hyperspectral target detection in terms of a local context by modeling the relationship of individual pixels with the annuli of pixels that surround them. A prediction of the center pixel in terms of the annulus pixels provides an estimate of the target-free pixel value, and this estimate can be used as a baseline against which a measurement of that pixel is compared. When the measurement...
In this paper, a new classifier under Bayesian framework is proposed to explore homogeneous region based low rank representation in hidden field for classification of hyperspectral imagery (HSI). This classifier integrates low rank representation and superpixel segmentation simultaneously, in which the HSI data is assumed to be lying in a low rank subspace within each homogeneous region of an estimated...
Recently, low-rank matrix recovery has been demonstrated to be an effective tool in hyperspectral images (HSIs) denoising. However, the previous low-rank matrix recovery method with a window of the fixed-shape cannot adaptively exploit spatial structure information and nonlocal similarity. In this paper, multiscale low-rank matrix recovery (MC-LRMR) is proposed to recover HSI corrupted by different...
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