A Triplet Semisupervised Deep Network for Fusion Classification of Hyperspectral and LiDAR Data

Li, Jiaojiao; Ma, Yinle; Song, Rui; Xi, Bobo; Hong, Danfeng; Du, Qian

doi:10.1109/tgrs.2022.3213513

Cited by 22 publications

(4 citation statements)

References 44 publications

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“…Additionally, Li et al [15] proposed a triplet semi-supervised deep network (TSDN) for the fusion classification of hyperspectral and LiDAR data. They introduced a pseudolabel acquisition strategy to increase the number of available training samples in the semi-supervised learning setting.…”

Section: Related Workmentioning

confidence: 99%

Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data

Tarsha Kurdi,

Amakhchan,

Gharineiat

et al. 2023

Sensors

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The use of a Machine Learning (ML) classification algorithm to classify airborne urban Light Detection And Ranging (LiDAR) point clouds into main classes such as buildings, terrain, and vegetation has been widely accepted. This paper assesses two strategies to enhance the effectiveness of the Deep Learning (DL) classification algorithm. Two ML classification approaches are developed and compared in this context. These approaches utilize the DL Pipeline Network (DLPN), which is tailored to minimize classification errors and maximize accuracy. The geometric features calculated from a point and its neighborhood are analyzed to select the features that will be used in the input layer of the classification algorithm. To evaluate the contribution of the proposed approach, five point-clouds datasets with different urban typologies and ground topography are employed. These point clouds exhibit variations in point density, accuracy, and the type of aircraft used (drone and plane). This diversity in the tested point clouds enables the assessment of the algorithm’s efficiency. The obtained high classification accuracy between 89% and 98% confirms the efficacy of the developed algorithm. Finally, the results of the adopted algorithm are compared with both rule-based and ML algorithms, providing insights into the positioning of DL classification algorithms among other strategies suggested in the literature.

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Section: Related Workmentioning

confidence: 99%

Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data

Tarsha Kurdi,

Amakhchan,

Gharineiat

et al. 2023

Sensors

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“…Therefore, as vegetation is more appropriately described by continuous metrics, flexible and robust methods that can map target vegetation variables (invasive species cover) to a continuous scale rather than a discrete class are urgently needed. Recently, the convolutional neural network-based regression (CNNR) model has also been widely applied in the estimation of continuous metrics across multiple disciplines, achieving promising results and outperforming the traditional regression model [46,[69][70][71][72].…”

Section: Introductionmentioning

confidence: 99%

Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network

Xing,

An,

Guo

et al. 2024

Remote Sensing

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Invasive noxious weed species (INWS) are typical poisonous plants and forbs that are considered an increasing threat to the native alpine grassland ecosystems in the Qinghai–Tibetan Plateau (QTP). Accurate knowledge of the continuous cover of INWS across complex alpine grassland ecosystems over a large scale is required for their control and management. However, the cooccurrence of INWS and native grass species results in highly heterogeneous grass communities and generates mixed pixels detected by remote sensors, which causes uncertainty in classification. The continuous coverage of INWS at the pixel level has not yet been achieved. In this study, objective 1 was to test the capability of Senginel-2 imagery at estimating continuous INWS cover across complex alpine grasslands over a large scale and objective 2 was to assess the performance of the state-of-the-art convolutional neural network-based regression (CNNR) model in estimating continuous INWS cover. Therefore, a novel CNNR model and a random forest regression (RFR) model were evaluated for estimating INWS continuous cover using Sentinel-2 imagery. INWS continuous cover was estimated directly from Sentinel-2 imagery with an R2 ranging from 0.88 to 0.93 using the CNNR model. The RFR model combined with multiple features had a comparable accuracy, which was slightly lower than that of the CNNR model, with an R2 of approximately 0.85. Twelve green band-, red-edge band-, and near-infrared band-related features had important contributions to the RFR model. Our results demonstrate that the CNNR model performs well when estimating INWS continuous cover directly from Sentinel-2 imagery, and the RFR model combined with multiple features derived from the Sentinel-2 imager can also be used for INWS continuous cover mapping. Sentinel-2 imagery is suitable for mapping continuous INWS cover across complex alpine grasslands over a large scale. Our research provides information for the advanced mapping of the continuous cover of invasive species across complex grassland ecosystems or, more widely, terrestrial ecosystems over large spatial areas using remote sensors such as Sentinel-2.

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“…However, these two branches just directly send the structure and the texture into the residual dense blocks to make a feature extraction, lacking consideration of the probable error propagation and texture-weakening phenomenon at a large degradation extent. Meanwhile, HSIs can also fuse with the LiDAR to promote the classification accuracy [41,42]. In addition, super-resolution can be further applied in the spectral domain to obtain the rich spectral information via the RGB images [43,44].…”

Section: Introductionmentioning

confidence: 99%

A Gated Content-Oriented Residual Dense Network for Hyperspectral Image Super-Resolution

Zhao

et al. 2023

Remote Sensing

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Limited by the existing imagery sensors, a hyperspectral image (HSI) is characterized by its high spectral resolution but low spatial resolution. HSI super-resolution (SR) aims to enhance the spatial resolution of the HSIs without modifying the equipment and has become a hot issue for HSI processing. In this paper, inspired by two important observations, a gated content-oriented residual dense network (GCoRDN) is designed for the HSI SR. To be specific, based on the observation that the structure and texture exhibit different sensitivities to the spatial degradation, a content-oriented network with two branches is designed. Meanwhile, a weight-sharing strategy is merged in the network to preserve the consistency in the structure and the texture. In addition, based on the observation of the super-resolved results, a gating mechanism is applied as a form of post-processing to further enhance the SR performance. Experimental results and data analysis on both ground-based HSIs and airborne HSIs have demonstrated the effectiveness of the proposed method.

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A Triplet Semisupervised Deep Network for Fusion Classification of Hyperspectral and LiDAR Data

Cited by 22 publications

References 44 publications

Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data

Contribution of Geometric Feature Analysis for Deep Learning Classification Algorithms of Urban LiDAR Data

Mapping the Continuous Cover of Invasive Noxious Weed Species Using Sentinel-2 Imagery and a Novel Convolutional Neural Regression Network

A Gated Content-Oriented Residual Dense Network for Hyperspectral Image Super-Resolution

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