Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing

Dong, Le; Yuan, Yuan

doi:10.3390/rs13081473

Cited by 10 publications

(2 citation statements)

References 44 publications

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“…The sparse representation can eliminate unimportant features from high-dimensional data and leave effective features [31]. Therefore, this paper introduces the L1 norm on the basis of the multiclass logistic regression model, and the parameter estimation is in the following form:…”

Section: Autonomous Learning Based On Sparse Multiclass Logistic Regressionmentioning

confidence: 99%

Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

et al. 2021

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In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.

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Section: Autonomous Learning Based On Sparse Multiclass Logistic Regressionmentioning

confidence: 99%

Autonomous Learning Interactive Features for Hyperspectral Remotely Sensed Data

et al. 2021

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“…Hyperspectral images (HSIs), which have the characteristics of a wide spectral range and high spectral resolution, are widely utilized to discriminate physical properties of different materials [1]. Benefitting from the rich spectral information, HSIs are active in the field of image classification [2,3], hyperspectral unmixing [4,5], band selection [6,7], anomaly detection [8,9] and target detection [10,11]. Among these applications, hyperspectral anomaly detection (HAD), aiming to excavate the pixels with significant spectral difference relative to surrounding pixels [12], attracts particular interest.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision

et al. 2022

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Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the effect of hyperspectral anomaly detection (HAD). In this paper, a dual dictionaries construction method via two-stage complementary decision (DDC–TSCD) for HAD is proposed. In the first stage, an adaptive inner window–based saliency detection was proposed to yield a coarse binary map, acting as the indicator to select pure background pixels. For the second stage, a background estimation network was designed to generate a fine binary map. Finally, the coarse binary map and fine binary map worked together to construct a pure background dictionary and potential anomaly dictionary in the guidance of the superpixels derived from the first stage. The experiments conducted on public datasets (i.e., HYDICE, Pavia, Los Angeles, San Diego-I, San Diego-II and Texas Coast) demonstrate that DDC–TSCD achieves satisfactory AUC values, which are separately 0.9991, 0.9951, 0.9968, 0.9923, 0.9986 and 0.9969, as compared to four typical methods and three state-of-the-art methods.

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