Identification and mapping of clay minerals in the region of Djebel Meni (Northwestern Algeria) using hyperspectral imaging, EO-1 Hyperion sensor

Zazi, Lyes; Boutaleb, Abdelhak; Guettouche, Mohamed Saïd

doi:10.1007/s12517-017-3015-z

Cited by 12 publications

(7 citation statements)

References 19 publications

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“…Hyperion data contained 242 bands, and many of them were found unsuitable for the analyses in this study due to the low signal-to-noise ratio. Thus, 147 bands were selected for analysis based on previous studies [8,10,41]. All bands were separately visualized, and bands that were not calibrated or had very high noise were eliminated following visual inspection.…”

Section: Preprocessingmentioning

confidence: 99%

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

Lim

Kim

Jin

2019

IJGI

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Remote sensing (RS) has been used to monitor inaccessible regions. It is considered a useful technique for deriving important environmental information from inaccessible regions, especially North Korea. In this study, we aim to develop a tree species classification model based on RS and machine learning techniques, which can be utilized for classification in North Korea. Two study sites were chosen, the Korea National Arboretum (KNA) in South Korea and Mt. Baekdu (MTB; a.k.a., Mt. Changbai in Chinese) in China, located in the border area between North Korea and China, and tree species classifications were examined in both regions. As a preliminary step in developing a classification algorithm that can be applied in North Korea, common coniferous species at both study sites, Korean pine (Pinus koraiensis) and Japanese larch (Larix kaempferi), were chosen as targets for investigation. Hyperion data have been used for tree species classification due to the abundant spectral information acquired from across more than 200 spectral bands (i.e., hyperspectral satellite data). However, it is impossible to acquire recent Hyperion data because the satellite ceased operation in 2017. Recently, Sentinel-2 satellite multispectral imagery has been used in tree species classification. Thus, it is necessary to compare these two kinds of satellite data to determine the possibility of reliably classifying species. Therefore, Hyperion and Sentinel-2 data were employed, along with machine learning techniques, such as random forests (RFs) and support vector machines (SVMs), to classify tree species. Three questions were answered, showing that: (1) RF and SVM are well established in the hyperspectral imagery for tree species classification, (2) Sentinel-2 data can be used to classify tree species with RF and SVM algorithms instead of Hyperion data, and (3) training data that were built in the KNA cannot be used for the tree classification of MTB. Random forests and SVMs showed overall accuracies of 0.60 and 0.51 and kappa values of 0.20 and 0.00, respectively. Moreover, combined training data from the KNA and MTB showed high classification accuracies in both regions; RF and SVM values exhibited accuracies of 0.99 and 0.97 and kappa values of 0.98 and 0.95, respectively.

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Section: Preprocessingmentioning

confidence: 99%

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

Lim

Kim

Jin

2019

IJGI

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“…HSI includes spectral data and image data. SMACC was chosen to build the image visualization because SMACC can determine the abundance of endmembers (Yang et al., 2014; Zazi et al., 2017). For Spectral information, in this study, a circular area smaller than the petri dish was selected as the region of interest (ROI), and the original image was cropped to the petri dish shape.…”

Section: Methodsmentioning

confidence: 99%

Fast recognition model of Fusarium in agaric based on hyperspectral imaging

et al. 2023

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Agaric is a dual‐purpose fungus for medicine and food, which is considered a healthcare product. However, in cultivation and production, agaric is contaminated by a variety of molds, resulting in a decline in quality and yield. Fusarium, as one of the poisonous filamentous fungi, seriously threatens the healthy development of the agaric industry and even affects human health. Therefore, it is necessary to establish timely and accurate identification and rapid detection of pathogens and crops. In this study, the visible near infrared hyperspectral imaging was used to obtain the image and spectral information, and the best classification model was selected and established to quickly distinguish Fusarium oxysporum from Fusarium verticillioides, which was also successfully applied for the preliminary identification of agaric infection. This approach could provide a nondestructive manner for rapid identification and detection of pathogenic fungi contamination in agaric.

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“…The atmospheric correction of these multispectral data was conducted using the ENVI FLAASH model. The bad bands and bad columns of the Hyperion image were removed because many of their bands show low signal-to-noise ratio or other problems [ 59 , 71 ]. Atmospheric correction was then also implemented using the FLAASH algorithm.…”

Section: Methodsmentioning

confidence: 99%

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

Zhou

Pan

2018

Sensors

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This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

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Identification and mapping of clay minerals in the region of Djebel Meni (Northwestern Algeria) using hyperspectral imaging, EO-1 Hyperion sensor

Cited by 12 publications

References 19 publications

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

Tree Species Classification Using Hyperion and Sentinel-2 Data with Machine Learning in South Korea and China

Fast recognition model of Fusarium in agaric based on hyperspectral imaging

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

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