Spectral matching approaches in hyperspectral image processing

Sanjeevi, S.; SrinivasaPerumal, Padma

doi:10.1080/01431161.2014.980922

Cited by 54 publications

(24 citation statements)

References 72 publications

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“…The spectral similarity value (SSV, SSV ∈ [0, √ 2]) evaluates the similarity of both the brightness and spectral behaviour of the retrieved hyperspectral reflectance with standard and observed aerosol (Shanmugam and SrinivasaPerumal, 2014;Granahan and Sweet, 2001). Figure 8 shows the NRMSE and SSV vs. the τ 550 for the three ROIs, where the water-leaving reflectance accuracy decreases with growing aerosol loading, particularly when an unsuitable aerosol type is selected.…”

Section: Ed(λmentioning

confidence: 99%

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The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

et al. 2015

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Abstract.Hyperspectral imaging provides quantitative remote sensing of ocean colour by the high spectral resolution of the water features. The HICO ™ (Hyperspectral Imager for the Coastal Ocean) is suitable for coastal studies and monitoring. The accurate retrieval of hyperspectral waterleaving reflectance from HICO ™ data is still a challenge. The aim of this work is to retrieve the water-leaving reflectance from HICO ™ data with a physically based algorithm, using the local microphysical properties of the aerosol in order to overcome the limitations of the standard aerosol types commonly used in atmospheric correction processing. The water-leaving reflectance was obtained using the HICO@CRI (HICO ATmospherically Corrected Reflectance Imagery) atmospheric correction algorithm by adapting the vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) radiative transfer code. The HICO@CRI algorithm was applied on to six HICO ™ images acquired in the northern Mediterranean basin, using the microphysical properties measured by the Acqua Alta Oceanographic Tower (AAOT) AERONET site. The HICO@CRI results obtained with AERONET products were validated with in situ measurements showing an accuracy expressed by r 2 = 0.98. Additional runs of HICO@CRI on the six images were performed using maritime, continental and urban standard aerosol types to perform the accuracy assessment when standard aerosol types implemented in 6SV are used. The results highlight that the microphysical properties of the aerosol improve the accuracy of the atmospheric correction compared to standard aerosol types. The normalized root mean square (NRMSE) and the similar spectral value (SSV) of the water-leaving reflectance show reduced accuracy in atmospheric correction results when there is an increase in aerosol loading. This is mainly when the standard aerosol type used is characterized with different optical properties compared to the local aerosol. The results suggest that if a water quality analysis is needed the microphysical properties of the aerosol need to be taken into consideration in the atmospheric correction of hyperspectral data over coastal environments, because aerosols influence the accuracy of the retrieved water-leaving reflectance.

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Section: Ed(λmentioning

confidence: 99%

“…The metric for the accuracy assessment is based on the euclidean distance (ED), which is sensitive to the magnitude of the water-leaving reflectance (Shanmugam and SrinivasaPerumal, 2014) for each HICO ™ channel with central wavelength λ i .…”

Section: Standard Aerosol Types In Hico@crimentioning

confidence: 99%

The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

et al. 2015

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“…Different absorption feature mapping/matching techniques have been used by the remote sensing community since hyperspectral data were made available. Among these the spectral feature fitting [73] or its improved version-multi-range spectral feature fitting (MRSFF)-are frequently used [74,75]. From the recently developed toolboxes integrating absorption feature matching techniques the Tetracoder [76] or EnGeoMAP 2.0 toolbox [77] should be listed.…”

Section: Discussionmentioning

confidence: 99%

Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping

Kopačková

Koucká

2017

Remote Sensing

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Merging hyperspectral data from optical and thermal ranges allows a wider variety of minerals to be mapped and thus allows lithology to be mapped in a more complex way. In contrast, in most of the studies that have taken advantage of the data from the visible (VIS), near-infrared (NIR), shortwave infrared (SWIR) and longwave infrared (LWIR) spectral ranges, these different spectral ranges were analysed and interpreted separately. This limits the complexity of the final interpretation. In this study a presentation is made of how multiple absorption features, which are directly linked to the mineral composition and are present throughout the VIS, NIR, SWIR and LWIR ranges, can be automatically derived and, moreover, how these new datasets can be successfully used for mineral/lithology mapping. The biggest advantage of this approach is that it overcomes the issue of prior definition of endmembers, which is a requested routine employed in all widely used spectral mapping techniques. In this study, two different airborne image datasets were analysed, HyMap (VIS/NIR/SWIR image data) and Airborne Hyperspectral Scanner (AHS, LWIR image data). Both datasets were acquired over the Sokolov lignite open-cast mines in the Czech Republic. It is further demonstrated that even in this case, when the absorption feature information derived from multispectral LWIR data is integrated with the absorption feature information derived from hyperspectral VIS/NIR/SWIR data, an important improvement in terms of more complex mineral mapping is achieved.

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“…One consists of original similarity indices such as spectral angle mapper (SAM), the spectral correlation angle (SCA), spectral information divergence (SID), and the Jeffries-Matusita (JM) distance; the other is a similarity index, which is defined by combining original single methods. They are effective in discriminating between spectral differences by overcoming the limitations of the original indices [28,29]. This study compared the various hybrid spectral similarity indices, e.g., SIDSAM (a combination of SID with SAM), SIDSCA (a combination of SID with SCA), and JMSAM (a combination of JM with SAM).…”

Section: Generating Label Datamentioning

confidence: 99%

Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

Song

Kim

2020

Remote Sensing

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Change detection (CD) networks based on supervised learning have been used in diverse CD tasks. However, such supervised CD networks require a large amount of data and only use information from current images. In addition, it is time consuming to manually acquire the ground truth data for newly obtained images. Here, we proposed a novel method for CD in case of a lack of training data in an area near by another one with the available ground truth data. The proposed method automatically entails generating training data and fine-tuning the CD network. To detect changes in target images without ground truth data, the difference images were generated using spectral similarity measure, and the training data were selected via fuzzy c-means clustering. Recurrent fully convolutional networks with multiscale three-dimensional filters were used to extract objects of various sizes from unmanned aerial vehicle (UAV) images. The CD network was pre-trained on labeled source domain data; then, the network was fine-tuned on target images using generated training data. Two further CD networks were trained with a combined weighted loss function. The training data in the target domain were iteratively updated using he prediction map of the CD network. Experiments on two hyperspectral UAV datasets confirmed that the proposed method is capable of transferring change rules and improving CD results based on training data extracted in an unsupervised way.

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Spectral matching approaches in hyperspectral image processing

Cited by 54 publications

References 72 publications

The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

Integration of Absorption Feature Information from Visible to Longwave Infrared Spectral Ranges for Mineral Mapping

Transfer Change Rules from Recurrent Fully Convolutional Networks for Hyperspectral Unmanned Aerial Vehicle Images without Ground Truth Data

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