TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

Li, N.; Liu, C.; Pfeifer, Norbert; Yin, Jiaming; Liao, Zhenyu; Zhou, Yufen

doi:10.5194/isprs-archives-xli-b3-283-2016

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Li ve diğ. , LiDAR verisini [21] ve LiDAR noktalar bulutunu [22] kullanarak yüksek seviyeli tensör özelliklerini elde etmişler ve bunları sınıflandırma başarımını artırmak için kullanmışlardır. Xiong ve diğ.…”

Section: Introductionunclassified

Hyperspectral unmixing with LiDAR-Based total variation regularized non-negative tensor factorization

Atas¹,

Kaya²,

KAHRAMAN³

2023

Pamukkale J Eng Sci

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Spektral karışım giderimi hiperspektral görüntülemenin temel araştırma alanlarından birisidir. Son yıllarda Negatif-olmayan Tensör Faktörizasyonuna dayalı yaklaşımlar, bilgi kaybına uğratmadığı ve hiperspektral görüntüleri daha iyi temsil edebildiği için uzaktan algılamada büyük bir önem kazanmıştır. Toplam Değişinti yaklaşımı ise, parçalı pürüzsüzlüğü sağlarken kenar bilgisini de korumaktadır. Öte yandan, kızılötesi algılayıcısı gözlemlenen sahne hakkında yükseklik bilgisini veren Dijital Yüzey Modeli verisini sağlamaktadır. Bu çalışmada, LiDAR Dijital Yüzey Modeli bilgisiyle Toplam Değişinti kısıtı birleştirilerek hiperspektral görüntülerin uzamsal çözünürlüğünü artırmak için tensör faktörizasyonuna dayalı karışım giderimi gerçekleştirilmiştir. Deneysel çalışmalar simülasyon ve gerçek veri setleri üzerinde denenmiş ve uzamsal çözünürlüğü artırılmış hiperspektral görüntüler elde edilmiştir. Elde edilen sonuçlar, literatürdeki en yakın çalışma olan Toplam Değişinti kısıtlı Negatifolmayan Matris-Vektör Tensor Faktörüzasyonu yöntemi ile karşılaştırılmış ve önerilen yöntemin daha iyi performans sergilediği gözlemlenmiştir.Spectral unmixing is one of the main research areas of hyperspectral image analysis. In recent years, Non-Negative Tensor Factorization based approaches have gained great importance in remote sensing as they do not lose information and can better represent hyperspectral images. The Total Variation approach preserves the edge information while providing piece-wise smoothness. On the other hand, the Light Detection and Ranging sensor provides Digital Surface Model information that gives height information about the observed scene. In this study, hyperspectral unmixing based on tensor factorization is performed to increase the spatial resolution of hyperspectral images by combining LiDAR Digital Surface Model information with Total Variation constrained. Experimental studies are carried out on simulation and real data sets and high spatial resolution hyperspectral images is obtained. The obtained results is compared with the state of the art Total Variation constrained Matrix-Vector Non-Negative Tensor Factorization approach and it is observed that the proposed method obtain better performance.

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Section: Introductionunclassified

Hyperspectral unmixing with LiDAR-Based total variation regularized non-negative tensor factorization

Atas¹,

Kaya²,

KAHRAMAN³

2023

Pamukkale J Eng Sci

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“…In the most active area, with three-dimensional information, such as a 3D object [29], hyperspectral cube [30], or gait video sequence [31] with three modes following the x-axis, y-axis, and z-axis, a third-order tensor, has been noted as an important point of study [32][33][34]. Individual identification using ECG signals can be also considered as a multilinear tensor space with temporal dimension.…”

Section: Introductionmentioning

confidence: 99%

Multilinear EigenECGs and FisherECGs for Individual Identification from Information Obtained by an Electrocardiogram Sensor

et al. 2018

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In this study, we present a third-order tensor-based multilinear eigenECG (MEECG) and multilinear Fisher ECG (MFECG) for individual identification based on the information obtained by an electrocardiogram (ECG) sensor. MEECG and MFECG are based on multilinear principal component analysis (MPCA) and multilinear linear discriminant analysis (MLDA) in the field of multilinear subspace learning (MSL), respectively. MSL directly extracts features without the vectorization of input data, while MSL extracts features without vectorizing the input data while maintaining most of the correlations shown in the original structure. In contrast with unsupervised linear subspace learning (LSL) techniques such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), it is less susceptible to small-data problems because it learns more compact and potentially useful representations, and it can efficiently handle large tensors. Here, the third-order tensor is formed by reordering the one-dimensional ECG signal into a two-dimensional matrix, considering the time frame. The MSL consists of four steps. The first step is preprocessing, in which input samples are centered. The second step is initialization, in which eigen decomposition is performed and the most significant eigenvectors are selected. The third step is local optimization, in which input data is applied by eigenvectors from the second step, and new eigenvectors are calculated using the applied input data. The final step is projection, in which the resultant feature tensors after projection are obtained. The experiments are performed on two databases for performance evaluation. The Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, and Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The experimental results revealed that the tensor-based MEECG and MFECG showed good identification performance in comparison to PCA and LDA of LSL.

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TENSOR MODELING BASED FOR AIRBORNE LiDAR DATA CLASSIFICATION

Cited by 2 publications

References 16 publications

Hyperspectral unmixing with LiDAR-Based total variation regularized non-negative tensor factorization

Hyperspectral unmixing with LiDAR-Based total variation regularized non-negative tensor factorization

Multilinear EigenECGs and FisherECGs for Individual Identification from Information Obtained by an Electrocardiogram Sensor

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