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Spectral unmixing is to decompose the hyperspectral data into endmembers and abundances. It has been known to be a challenging and ill-posed task due to the corruption of noise as well as complex environmental conditions. In this paper, we propose a part-based denoising autoencoder with unique structure that solves the unmixing challenges. The effective l21 norm and denoising constraints are applied...
Pansharpening refers to the fusion of a high spatial resolution panchromatic image with high spectral resolution multispectral or hyperspectral images (MSI or HSI) to yield high resolution data in both spectral and spatial domains. It has been widely adopted as a primary preprocessing step for numerous applications. In this paper, we perform a literature survey of various pansharpening algorithms...
Anomaly detection has been known to be a challenging, ill-posed problem due to the uncertainty of anomaly and the interference of noise. In this paper, we propose a novel low rank anomaly detection algorithm in hyperspectral images (HSI), where three components are involved. First, due to the highly mixed nature of pixels in HSI, instead of using the raw pixel directly for anomaly detection, the proposed...
Anomaly detection becomes increasingly important in hyper-spectral image analysis, since it can now uncover many material substances which were previously unresolved by multi-spectral sensors. In this paper, we propose a Low-rank Tensor Decomposition based anomaly Detection (LTDD) algorithm for Hyperspectral Imagery. The HSI data cube is first modeled as a dense low-rank tensor plus a sparse tensor...
Traditional hyperspectral anomaly detection methods either model the global background or the local neighborhood, that bring some apparent drawbacks, such as the unreasonable assumption of uni-modular background in global detectors, or the high false alarms by sliding windows in local detectors. In this paper, a source component-based anomaly detection approach is proposed. It first extracts the source...
Hyperspectral image unmixing is the process of estimating pure source signals (endmemebers) and their proportions (abundances) from highly mixed spectroscopic images. Due to model inaccuracies and observation noise, unmixing has been a very challenging problem. In this paper, we exploit the potential of using autoencoder to tackle the unmixing challenges. Two important facts are considered in the...
Unsupervised spectral unmixing (i.e., endmember extraction and abundance estimation) of nonlinear mixture is a very challenging subject in hyperspectral image analysis. In this paper, we present a new interpretation of the reflectance mixture by normalizing the absolute reflectance value into a unit L1 norm vector, such that the spectral reading can be treated as a probability distribution. The abundance...
Hyperspectral images consist of large number of spectral bands but many of which contain redundant information. Therefore, band selection has been a common practice to reduce the dimensionality of the data space for cutting down the computational cost and alleviating from the Hughes phenomenon. This paper presents a new technique for band selection where a sparse representation of the hyperspectral...
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