Relative Attribute Based Unmixing

Zhao, Genping; Cheng, Lianglun; Wu, Heng; Li, Hui; Li, Xin

doi:10.1109/igarss.2018.8518428

Cited by 2 publications

(5 citation statements)

References 12 publications

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“…In the AA unmixing model, the original data samples are expressed as a linearly weighted sum of these archetypes, and the archetype is generated from the original data in an additive manner. When the AA method [28] is introduced into spectral unmixing, the obtained archetypes can be used as endmembers which are generated as a linear combination of the original data [29,30]. This process can be formulated as:…”

Section: Archetypal Analysis Spectral Unmixingmentioning

confidence: 99%

“…With regard to the first issue encountered by using MV-NMF unmixing method for AD, the first objective of this study is to introduce the model of Archetypal Analysis (AA) [28] to implement spectral unmixing and to aid in sparse representation-based AD. In fact, this model has been demonstrated to be of a great potential for spectral unmixing [29,30]. AA explicitly provides the generation of a mutual relationship between the endmembers and the original data.…”

Section: Introductionmentioning

confidence: 99%

“…Prior to MV-NMF, the good model interpretability enables one to control the generation of the virtual endmember to obtain more accurate endmembers and abundances. Moreover, the initialization strategy of the archetypes also makes it less possible to get the local optimum rather than the MV-NMF with a random initialization [29]. Instead of non-negative constraints, AA is imposed with simplex constraints to optimize the principle convex hull, which in fact satisfies the non-negative and sum-to-one constraints as linear unmixing and leads to both low-rank and sparse representation effects.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

Zhao

Li²,

Zhang³

et al. 2021

Remote Sensing

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Hyperspectral images (HSIs) often contain pixels with mixed spectra, which makes it difficult to accurately separate the background signal from the anomaly target signal. To mitigate this problem, we present a method that applies spectral unmixing and structure sparse representation to accurately extract the pure background features and to establish a structured sparse representation model at a sub-pixel level by using the Archetypal Analysis (AA) scheme. Specifically, spectral unmixing with AA is used to unmix the spectral data to obtain representative background endmember signatures. Moreover the unmixing reconstruction error is utilized for the identification of the target. Structured sparse representation is also adopted for anomaly target detection by using the background endmember features from AA unmixing. Moreover, both the AA unmixing reconstruction error and the structured sparse representation reconstruction error are integrated together to enhance the anomaly target detection performance. The proposed method exploits background features at a sub-pixel level to improve the accuracy of anomaly target detection. Comparative experiments and analysis on public hyperspectral datasets show that the proposed algorithm potentially surpasses all the counterpart methods in anomaly target detection.

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Section: Archetypal Analysis Spectral Unmixingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

Zhao

Li²,

Zhang³

et al. 2021

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…In this study, the relative attribute learning (RAL) algorithm integrated with the rank support vector machine (rankSVM) ranking model [38,39] was deployed to learn the relative relation of the age attribute (denoted as a function of the successional stage, as a) of a given forest. Given the training data set X ∈ R N×G with G selected metric features, the RAL uses the relative relation between sample pairs and is treated as a "learn-to-rank" problem on the rankSVM framework (Equation ( 9) [38]).…”

Section: Relative Attribute Learning For Tdfs Succession Age Attributementioning

confidence: 99%

“…The first objective was to develop a fast nonlinear archetypal analysis model to conduct different metric configurations for forest-age-attribute learning. The second objective was to use "relative attribute learning" (RAL) [38,39], which is formulated as a "learn-to-rank" problem and uses a rank support vector machine (SVM) framework, to calculate the relative score/belonging levels of a sample to a certain class attribute. This allowed us to learn and predict the associate score of the succession age attribute by using the smallest set of informative spectral indices, as well as LiDAR metrics.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages

et al. 2021

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Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the deterministic successional stages in a TDF. These successional stages, classified as early, intermediate, and late, are considered a proxy for mapping the age since the abandonment of a given forest area. Expanding on the need for more accurate successional forest mapping, our study considers the age attributes of a TDF study area as a continuous expression of relative attribute scores/levels that vary along the process of ecological succession. Specifically, two remote-sensing data sets: HyMap (hyperspectral) and LVIS (waveform LiDAR), were acquired at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS) in Costa Rica, were used to generate age-attribute metrics. These metrics were then used as entry-level variables on a randomized nonlinear archetypal analysis (RNAA) model to select the most informative metrics from both data sets. Next, a relative attribute learning (RAL) algorithm was adapted for both independent and fused metrics to comparatively learn the relative attribute levels of the forest ages of the study area. In this study, four HyMap indices and five LVIS metrics were found to have the potential to map the forest ages of the study area, and compared with these results, a significant improvement was found through the fusion of the metrics on the accuracy of the generated forest age maps. By linking the age group mapping and the relative attribute mapping results, a dynamic gradient of the age-attribute transition patterns emerged.

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Relative Attribute Based Unmixing

Cited by 2 publications

References 12 publications

Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

Archetypal Analysis and Structured Sparse Representation for Hyperspectral Anomaly Detection

Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages

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