Hyperspectral Satellites, Evolution, and Development History

Qian, Shen‐En

doi:10.1109/jstars.2021.3090256

Cited by 92 publications

(36 citation statements)

References 78 publications

(82 reference statements)

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“…The decoder process is formulated as follows. (7) where represents the output of the encoder along with the input of the decoder;…”

Section: Methodsmentioning

confidence: 99%

“…The nonlinear part can be considered as the unfolding of (4), which is implemented by another FCL without biases. The secondary interaction coefficients γ in (7) serve as the training parameters constrained to [0,1]. Thus, the output of the nonlinear component is as follows: (16) where ; and are the ith endmember and abundance (i.e., ith column).…”

Section: Decodermentioning

confidence: 99%

“…Under the influence of limited spatial resolution and the complexity of the cover type distribution, mixed pixels inevitably exist in hyperspectral images, making it difficult to extract ground objects. Therefore, mixed-pixel decomposition has become a significant research topic in remote sensing processing [7]. Hyperspectral unmixing methods can be categorized into linear mixing models (LMMs) and nonlinear mixing models (NLMMs).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Residual Dense Autoencoder Network for Nonlinear Hyperspectral Unmixing

Yang

Chen

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral unmixing is a popular research topic in hyperspectral processing, aiming at obtaining the ground features contained in the mixed pixels and their proportion. Recently, nonlinear mixing models have received particular attention in hyperspectral decomposition since the linear mixing model cannot suitably apply in the situation that exists in multiple scattering. In this study, we constructed a residual dense autoencoder network (RDAE) for nonlinear hyperspectral unmixing in multiple scattering scenarios. First, an encoder was built based on the residual dense network (RDN) and attention layer. The RDN is employed to characterize multi-scale representations, which are further transformed with the attention layer to estimate the abundance maps. Second, we designed a decoder based on the unfolding of a generalized bilinear model to extract endmembers and estimate their second-order scattering interactions. Comparative experiments between the RDAE and six other state-of-the-art methods under synthetic and real hyperspectral datasets demonstrate that the proposed method achieved a better performance in terms of endmember extraction and abundance estimation.

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“…The decoder process is formulated as follows. (7) where represents the output of the encoder along with the input of the decoder;…”

Section: Methodsmentioning

confidence: 99%

Section: Decodermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Residual Dense Autoencoder Network for Nonlinear Hyperspectral Unmixing

Yang

Chen

Wang

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…vision, artificial intelligence) [9] promote the advances of HSI development [10]. After enormous efforts in developing into sensor , carrying platform [24,30,[35][36][37][38][39] and the affiliated image processing research, the technique has been embraced by the Earth-observation (EO) community. Therefore, HSIs find a wide range of EO applications in astronomy [40], airborne/satellite-based remote sensing [41], agriculture [42], forests [43], medical diagnostic [44], geosciences [45], etc.…”

Section: Signal Transmission Each Point Of a Hyperspectralmentioning

confidence: 99%

A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation

Liu¹,

Li²,

Wang³

et al. 2022

Preprint

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The ability of capturing fine spectral discriminative information enables hyperspectral images (HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the captured HSIs may not represent true distribution of ground objects and the received reflectance at imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects and sensors' hardware limitations. These degradations include but are not limited to: complex noise (i.e., Gaussian noise, impulse noise, sparse stripes, and their mixtures), heavy stripes, deadlines, cloud and shadow occlusion, blurring and spatial-resolution degradation and spectral absorption, etc. These degradations dramatically reduce the quality and usefulness of HSIs. Low-rank tensor approximation (LRTA) is such an emerging technique, having gained much attention in HSI restoration community, with ever-growing theoretical foundation and pivotal technological innovation. Compared to low-rank matrix approximation (LRMA), LRTA is capable of characterizing more complex intrinsic structure of high-order data and owns more efficient learning abilities, being established to address convex and non-convex inverse optimization problems induced by HSI restoration. This survey mainly attempts to present a sophisticated, cutting-edge, and comprehensive technical survey of LRTA toward HSI restoration, specifically focusing on the following six topics: Denoising, Destriping, Inpainting, Deblurring, Super-resolution and Fusion. The theoretical development and variants of LRTA techniques are also elaborated. For each topic, the state-of-the-art restoration methods are compared by assessing their performance both quantitatively and visually. Open issues and challenges are also presented, including model formulation, algorithm design, prior exploration and application concerning the interpretation requirements.

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“…There is an abundance of hyperspectral satellites, such as the Project for On-Board Autonomy (PROBA), Sentinel-3A, Greenhouse Gas Satellite-Demonstrator (GHGSat-D), Aalto-1, GomSpace Express-4B (GomX-4B), Indian Mini Satellite-2 (IMS-2), and International Space Station (ISS), to name a few. There are also data products available from satellites that are out of service, such as Earth Observing-1 (EO-1) and Environmental Satellite (Envisat) [ 33 ]. China mainly operates three major satellite systems for land, ocean, and meteorology [ 34 ], and the hyperspectral satellites that are in orbit and widely used include GaoFen-5 (GF-5) [ 35 , 36 , 37 , 38 ], ZiYuan-1 02D (ZY-1 02D) [ 39 ], HuanJing-1A (HJ-1A) [ 40 , 41 , 42 , 43 ], and Orbita Hyperspectral (OHS) [ 44 , 45 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Bamboo Species Based on Extreme Gradient Boosting (XGBoost) Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery

Zhou

Zhao

et al. 2022

Sensors

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Mapping the distribution of bamboo species is vital for the sustainable management of bamboo and for assessing its ecological and socioeconomic value. However, the spectral similarity between bamboo species makes this work extremely challenging through remote sensing technology. Existing related studies rarely integrate multiple feature variables and consider how to quantify the main factors affecting classification. Therefore, feature variables, such as spectra, topography, texture, and vegetation indices, were used to construct the XGBoost model to identify bamboo species using the Zhuhai-1 Orbita hyperspectral (OHS) imagery in the Southern Sichuan Bamboo Sea and its surrounding areas in Sichuan Province, China. The random forest and Spearman’s rank correlation analysis were used to sort the main variables that affect classification accuracy and minimize the effects of multicollinearity among variables. The main findings were: (1) The XGBoost model achieved accurate and reliable classification results. The XGBoost model had a higher overall accuracy (80.6%), kappa coefficient (0.708), and mean F1-score (0.805) than the spectral angle mapper (SAM) method; (2) The optimal feature variables that were important and uncorrelated for classification accuracy included the blue band (B1, 464–468 nm), near-infrared band (B27, 861–871 nm), green band (B5, 534–539 nm), elevation, texture feature mean, green band (B4, 517–523 nm), and red edge band (B17, 711–720 nm); and (3) the XGBoost model based on the optimal feature variable selection showed good adaptability to land classification and had better classification performance. Moreover, the mean F1-score indicated that the model could well balance the user’s and producer’s accuracy. Additionally, our study demonstrated that OHS imagery has great potential for land cover classification and that combining multiple features to enhance classification is an approach worth exploring. Our study provides a methodological reference for the application of OHS images for plant species identification.

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Hyperspectral Satellites, Evolution, and Development History

Cited by 92 publications

References 78 publications

Residual Dense Autoencoder Network for Nonlinear Hyperspectral Unmixing

Residual Dense Autoencoder Network for Nonlinear Hyperspectral Unmixing

A Survey on Hyperspectral Image Restoration: From the View of Low-Rank Tensor Approximation

Identification of Bamboo Species Based on Extreme Gradient Boosting (XGBoost) Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery

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