Improvement of a mineral discrimination method using multispectral image and surrounding hyperspectral image

Nakayama, Kazuki; Tonooka, Hideyuki

doi:10.1117/1.jrs.15.040501

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…On the other hand, the improved HT method did not show any decrease in accuracy due to misalignment because of its ability to suppress the effect of misalignment. This is due to the ability to search for the best performance of the endmember pixels from the surrounding pixels of the HS endmember [24].…”

Section: Discussionmentioning

confidence: 99%

“…The improved HT method selects the best MS-based endmember pixel from around the MS pixel at the exact location as the HS-based endmember pixel in the overlap region between the HS and MS images to obtain the MS endmember spectrum [24]. Then, the mineral of each pixel is identified by the SAM method given the obtained MS endmember spectra.…”

Section: Hs and Ms Images Usedmentioning

confidence: 99%

“…However, it also causes errors in mineral identification if there is a misalignment between HS and MS images [22]. Therefore, Nakayama and Tonooka [24] proposed an improved HT method that determines MS-based endmember pixels from the neighborhood of the endmember pixels on the HS image and also optimizes the mineral selection by the SAM method. The features of this improved HT method are that it is robust to misalignment between HS and MS images and can automatically set threshold values for each mineral.…”

Section: Introductionmentioning

confidence: 99%

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Region Expansion of a Hyperspectral-Based Mineral Map Using Random Forest Classification with Multispectral Data

Tsubomatsu,

Tonooka

2023

Minerals

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Observation images from hyperspectral (HS) sensors on satellites and aircraft can be used to map minerals in greater detail than those from multispectral (MS) sensors. However, the coverage of HS images is much less than that of MS images, so there are often cases where MS images cover the entire area of interest while HS images cover only a part of it. In this study, we propose a new method to more reasonably expand the mineral map of an HS image with an MS image in such cases. The method uses various mineral indices from the MS image and MS sensor’s band values as the input and HS image-based mineral classes as the output. Random forest (RF) two-class classification is then applied iteratively to determine the distribution of each mineral in turn, starting with the minerals that are most consistent with the HS image-based mineral map. The method also involves the correction of misalignment between HS and MS images and the selection of input variables by RF multiclass classification. The method was evaluated in comparison with other methods in the Cuprite area, Nevada, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Imager Suite (HISUI) as HS sensors and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) as MS sensors. As a result, all of the evaluated region-expansion methods with an HS–MS image pair, including the proposed method, showed better performance than the method using only an MS image. The proposed method had the highest performance, and the inter-mineral averages of the F1-scores for the overlap and non-overlap areas were 85.98% and 46.46% for the AVIRIS–ASTER image pair and 82.78% and 42.60% for the HISUI–ASTER image pair, respectively. Although the performance in the non-overlap region was lower than in the overlap region, the method showed high precision and high accuracy for almost all minerals, including minerals with only a few pixels. Misalignment between the HS–MS images is a factor that degrades accuracy and requires precise alignment, but the misalignment correction in the proposed method could suppress the effect of misalignment. Validation studies using different regions and different sensors will be carried out in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Hs and Ms Images Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Region Expansion of a Hyperspectral-Based Mineral Map Using Random Forest Classification with Multispectral Data

Tsubomatsu,

Tonooka

2023

Minerals

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Two-stage multi-dimensional convolutional stacked autoencoder network model for hyperspectral images classification

Bai

Sun

et al. 2023

Multimed Tools Appl

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Deep learning models have been widely used in hyperspectral images classification. However, the classification results are not satisfactory when the number of training samples is small. Focused on above-mentioned problem, a novel Two-stage Multi-dimensional Convolutional Stacked Autoencoder (TMC-SAE) model is proposed for hyperspectral images classification. The proposed model is composed of two sub-models SAE-1 and SAE-2. The SAE-1 is a 1D autoencoder with asymmetric structre based on full connection layers and 1D convolution layers to reduce spectral dimensionality. The SAE-2 is a hybrid autoencoder composed of 2D and 3D convolution operations to extract spectral-spatial features from the reduced dimensionality data by SAE-1. The SAE-1 is trained with raw data by unsupervised learning and the encoder of SAE-1 is employed to reduce spectral dimensionality of raw data. The data after dimension reduction is used to train the SAE-2 by unsupervised learning. The fine-tuning of SAE-2 encoder and the training of classifier are implemented simultaneously with small number of samples by supervised learning. Comparative experiments are performed on three widely used hyperspectral remote sensing data. The extensive comparative experiments demonstrate that the proposed architecture can effectively extract deep features and maintain high classification accuracy with small number of training samples.

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Bibliometric analysis of global trends and characteristics of remote sensing for mineral exploration in the early 21st century

Ju,

Liu,

Liu

et al. 2024

All Earth

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Improvement of a mineral discrimination method using multispectral image and surrounding hyperspectral image

Cited by 3 publications

References 15 publications

Region Expansion of a Hyperspectral-Based Mineral Map Using Random Forest Classification with Multispectral Data

Region Expansion of a Hyperspectral-Based Mineral Map Using Random Forest Classification with Multispectral Data

Two-stage multi-dimensional convolutional stacked autoencoder network model for hyperspectral images classification

Bibliometric analysis of global trends and characteristics of remote sensing for mineral exploration in the early 21st century

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