Cotton extraction method of integrated multi-features based on multi-temporal Landsat 8 images

Wang, W. H.; ZHANG, Xia; Zhao, Yonghui; Wang, Shudong

doi:10.11834/jrs.20175317

Cited by 8 publications

(3 citation statements)

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“…Researchers and analysts propose different techniques and methods to estimate an accurate map of LU/LC. For example, using spectral indexes is one of the methods that have been widely adopted in imagery classification of both low, medium, and high spectral and spatial resolutions of remotely sensed datasets, such as Vegetation Indexes [16,17], Water Index [18][19][20], normalized-difference building index [16], ecological-index [17], normalized-difference vegetation index [17,21], and derivative indices, e.g., the re-normalizeddifference vegetation index [17,22], a growing-season-normalized-difference-vegetation-index [23,24]. Short periods of each low, medium, and high spectral and spatial-resolution imagery have several advantages, and several analysts have adopted Spectral-Indices to a Time-Series-Image [18,25,26].…”

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confidence: 99%

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Dibs¹,

Jaber²,

Al‐Ansari³

2023

Emerg Sci J

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Producing accurate Land-Use and Land-Cover (LU/LC) maps using low-spatial-resolution images is a difficult task. Pan-sharpening is crucial for estimating LU/LC patterns. This study aimed to identify the most precise procedure for estimating LU/LC by adopting two fusion approaches, namely Color Normalized Brovey (BM) and Gram-Schmidt Spectral Sharpening (GS), on high-spatial-resolution Multi-sensor and Multi-spectral images, such as (1) the Unmanned Aerial Vehicle (UAV) system, (2) the WorldView-2 satellite system, and (3) low-spatial-resolution images like the Sentinel-2 satellite, to generate six levels of fused images with the three original multi-spectral images. The Maximum Likelihood method (ML) was used for classifying all nine images. A confusion matrix was used to evaluate the accuracy of each single classified image. The obtained results were statistically compared to determine the most reliable, accurate, and appropriate LU/LC map and procedure. It was found that applying GS to the fused image, which integrated WorldView-2 and Sentinel-2 satellite images and was classified by the ML method, produced the most accurate results. This procedure has an overall accuracy of 88.47% and a kappa coefficient of 0.85. However, the overall accuracies of the three classified multispectral images range between 86.84% to 76.49%. Furthermore, the accuracy assessment of the fused images by the Brovey method and the rest of the GS method and classified by the ML method ranges between 85.75% to 76.68%. This proposed procedure shows a lot of promise in the academic sphere for mapping LU/LC. Previous researchers have mostly used satellite images or datasets with similar spatial and spectral resolution, at least for tropical areas like the study area of this research, to detect land surface patterns. However, no one has previously investigated and examined the use and application of different datasets that have different spectral and spatial resolutions and their accuracy for mapping LU/LC. This study has successfully adopted different datasets provided by different sensors with varying spectral and spatial levels to investigate this. Doi: 10.28991/ESJ-2023-07-04-013 Full Text: PDF

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confidence: 99%

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Dibs¹,

Jaber²,

Al‐Ansari³

2023

Emerg Sci J

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“…Shaban et al [23] extracted three texture features, including the gray level co-occurrence matrix (GLCM), gray level difference histogram (GLDH), and difference histogram (SADH), which were combined with spectral features to significantly improve the accuracy of IS extraction. Wang et al [24] used the normalized difference vegetation index (NDVI) time series, reflectance spectral features, and spatial texture features as the feature input of support vector machine classification and extracted IS. The overall classification accuracy of the method was 93.66%.…”

Section: Introductionmentioning

confidence: 99%

Impervious Surface Mapping Based on Remote Sensing and an Optimized Coupled Model: The Dianchi Basin as an Example

Xue

et al. 2023

Land

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Accurately extracting impervious surfaces (IS) and continuously monitoring their dynamics are crucial practices for promoting sustainable development in regional ecological environments and resources. In this context, we conducted experiments to extract IS of the Dianchi Lake Basin by utilizing various features extracted from remote sensing images and applying three different machine learning algorithms. Through this process, we obtained the optimal combination of features and a machine learning algorithm. Utilizing this model, our objective is to map the evolution of IS in the Dianchi Lake Basin, from 2000 to 2022, and analyze its dynamic changes. Our results showed the following: (1) The optimal model for IS extraction in the Dianchi Lake Basin was IMG-SPESVM based on the support vector machine, remote sensing images, and spectral features. (2) From 2000 to 2022, the spatial distribution and shape of the IS in the Dianchi Lake Basin changed significantly, but they all developed in the area around Dianchi Lake. (3) From 2000 to 2015, the rate of expansion of IS gradually accelerated, while from 2015 to 2022, it contracted. (4) From 2000 to 2022, the center of mass of IS moved to the northeast, and the standard deviation ellipse shifted greatly in the south–north direction. (5) Natural factors negatively affected the expansion of IS, while social factors positively affected the distribution of the IS.

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“…It was found that the spectral characteristics of deciduous trees changed with seasonal regularity, while those of evergreen trees did not change significantly during the year. Reflectance of Landsat 8 image at optimal temporal were combined with NDVI time series by rough set method to classify cotton, the classification accuracy was greatly improved compared to the unoptimized NDVI time series [12].…”

Section: Introductionmentioning

confidence: 99%

Optimum Phenological Phases for Deciduous Species Recognition: A Case Study on Quercus acutissima and Robinia pseudoacacia in Mount Tai

Liu

Zhu

et al. 2022

Forests

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Tree species recognition is important for remote–sensing mapping and dynamic monitoring of forest resource. However, the complex phenological cycle poses a challenge to remote–sensing recognition of deciduous tree species in mountainous areas, and the selection of temporal phase is particularly important to improve recognition accuracy. Multispectral images of Ziyuan–1 02C (ZY–1 02C) and Ziyuan–3 (ZY–3) at three phenological phases of spring, autumn and winter (12 May, 29 September and 7 December, recorded as T5–12, T9–29 and T12–7) are selected to optimize sensitive spectral indices. Support vector machine (SVM) and maximum likelihood model (MLE) are constructed to explore the optimum phase of recognizing on Quercus acutissima (O. acutissima ) and Robinia pseudoacacia (R. pseudoacacia) in Mount Tai. The results showed the average spectral reflection intensity of O. acutissima was higher than that of R. pseudoacacia Compared to other phenological periods, the most significant spectral differences between O. acutissima and R. pseudoacacia were found in the spring (12 May), which was identified as the optimum phenological phase. Band 4 is the most sensitive band in all the three phases for the tree species recognition. Moreover, the overall recognition accuracy of deciduous tree species on 12 May reached 89.25%, which was significantly higher than the other two phases. On 12 May, the recognition accuracies of SVM based on sensitive spectral indices of up to 93.59% for O. acutissima and 85.44% for R. pseudoacacia, were higher overall than that of the MLE. Sensitive spectral indices introduced were shown to significantly improve the recognition accuracy for tree species over a single sensitive band. The study is expected to facilitate the precise recognition and forestry management on Mount Tai.

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Cotton extraction method of integrated multi-features based on multi-temporal Landsat 8 images

Cited by 8 publications

References 0 publications

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis

Impervious Surface Mapping Based on Remote Sensing and an Optimized Coupled Model: The Dianchi Basin as an Example

Optimum Phenological Phases for Deciduous Species Recognition: A Case Study on Quercus acutissima and Robinia pseudoacacia in Mount Tai

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