multispectral images
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2022 ◽  
Vol 277 ◽  
pp. 108419
Author(s):  
Qi Yang ◽  
Liangsheng Shi ◽  
Jingye Han ◽  
Zhuowei Chen ◽  
Jin Yu

2022 ◽  
Vol 14 (2) ◽  
pp. 331
Author(s):  
Xuewei Zhang ◽  
Kefei Zhang ◽  
Yaqin Sun ◽  
Yindi Zhao ◽  
Huifu Zhuang ◽  
...  

The leaf area index (LAI) is of great significance for crop growth monitoring. Recently, unmanned aerial systems (UASs) have experienced rapid development and can provide critical data support for crop LAI monitoring. This study investigates the effects of combining spectral and texture features extracted from UAS multispectral imagery on maize LAI estimation. Multispectral images and in situ maize LAI were collected from test sites in Tongshan, Xuzhou, Jiangsu Province, China. The spectral and texture features of UAS multispectral remote sensing images are extracted using the vegetation indices (VIs) and the gray-level co-occurrence matrix (GLCM), respectively. Normalized texture indices (NDTIs), ratio texture indices (RTIs), and difference texture indices (DTIs) are calculated using two GLCM-based textures to express the influence of two different texture features on LAI monitoring at the same time. The remote sensing features are prescreened through correlation analysis. Different data dimensionality reduction or feature selection methods, including stepwise selection (ST), principal component analysis (PCA), and ST combined with PCA (ST_PCA), are coupled with support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) to build the maize LAI estimation models. The results reveal that ST_PCA coupled with SVR has better performance, in terms of the VIs + DTIs (R2 = 0.876, RMSE = 0.239) and VIs + NDTIs (R2 = 0.877, RMSE = 0.236). This study introduces the potential of different texture indices for maize LAI monitoring and demonstrates the promising solution of using ST_PCA to realize the combining of spectral and texture features for improving the estimation accuracy of maize LAI.


2022 ◽  
Author(s):  
Roberto Pierdicca ◽  
Marina Paolanti

Abstract. Researchers have explored the benefits and applications of modern artificial intelligence (AI) algorithms in different scenario. For the processing of geomatics data, AI offers overwhelming opportunities. Fundamental questions include how AI can be specifically applied to or must be specifically created for geomatics data. This change is also having a significant impact on geospatial data. The integration of AI approaches in geomatics has developed into the concept of Geospatial Artificial Intelligence (GeoAI), which is a new paradigm for geographic knowledge discovery and beyond. However, little systematic work currently exists on how researchers have applied AI for geospatial domains. Hence, this contribution outlines AI-based techniques for analysing and interpreting complex geomatics data. Our analysis has covered several gaps, for instance defining relationships between AI-based approaches and geomatics data. First, technologies and tools used for data acquisition are outlined, with a particular focus on RGB images, thermal images, 3D point clouds, trajectories, and hyperspectral/multispectral images. Then, how AI approaches have been exploited for the interpretation of geomatic data is explained. Finally, a broad set of examples of applications are given, together with the specific method applied. Limitations point towards unexplored areas for future investigations, serving as useful guidelines for future research directions.


2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Pei Wang ◽  
Shuwei Wang ◽  
Yuan Zhang ◽  
Xiaoyan Duan

The objectives of this study were to improve the efficiency and accuracy of early clinical diagnosis of cervical cancer and to explore the application of tissue classification algorithm combined with multispectral imaging in screening of cervical cancer. 50 patients with suspected cervical cancer were selected. Firstly, the multispectral imaging technology was used to collect the multispectral images of the cervical tissues of 50 patients under the conventional white light waveband, the narrowband green light waveband, and the narrowband blue light waveband. Secondly, the collected multispectral images were fused, and then the tissue classification algorithm was used to segment the diseased area according to the difference between the cervical tissues without lesions and the cervical tissues with lesions. The difference in the contrast and other characteristics of the multiband spectrum fusion image would segment the diseased area, which was compared with the results of the disease examination. The average gradient, standard deviation (SD), and image entropy were adopted to evaluate the image quality, and the sensitivity and specificity were selected to evaluate the clinical application value of discussed method. The fused spectral image was compared with the image without lesions, it was found that there was a clear difference, and the fused multispectral image showed a contrast of 0.7549, which was also higher than that before fusion (0.4716), showing statistical difference ( P < 0.05 ). The average gradient, SD, and image entropy of the multispectral image assisted by the tissue classification algorithm were 2.0765, 65.2579, and 4.974, respectively, showing statistical difference ( P < 0.05 ). Compared with the three reported indicators, the values of the algorithm in this study were higher. The sensitivity and specificity of the multispectral image with the tissue classification algorithm were 85.3% and 70.8%, respectively, which were both greater than those of the image without the algorithm. It showed that the multispectral image assisted by tissue classification algorithm can effectively screen the cervical cancer and can quickly, efficiently, and safely segment the cervical tissue from the lesion area and the nonlesion area. The segmentation result was the same as that of the doctor's disease examination, indicating that it showed high clinical application value. This provided an effective reference for the clinical application of multispectral imaging technology assisted by tissue classification algorithm in the early screening and diagnosis of cervical cancer.


2022 ◽  
Vol 14 (1) ◽  
pp. 233
Author(s):  
Weijie Chen ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola K. Kasabov

Compared with single-band remote sensing images, multispectral images can obtain information on the same target in different bands. By combining the characteristics of each band, we can obtain clearer enhanced images; therefore, we propose a multispectral image enhancement method based on the improved dark channel prior (IDCP) and bilateral fractional differential (BFD) model to make full use of the multiband information. First, the original multispectral image is inverted to meet the prior conditions of dark channel theory. Second, according to the characteristics of multiple bands, the dark channel algorithm is improved. The RGB channels are extended to multiple channels, and the spatial domain fractional differential mask is used to optimize the transmittance estimation to make it more consistent with the dark channel hypothesis. Then, we propose a bilateral fractional differentiation algorithm that enhances the edge details of an image through the fractional differential in the spatial domain and intensity domain. Finally, we implement the inversion operation to obtain the final enhanced image. We apply the proposed IDCP_BFD method to a multispectral dataset and conduct sufficient experiments. The experimental results show the superiority of the proposed method over relative comparison methods.


Author(s):  
Honglei Zhu ◽  
Yanwei Huang ◽  
Yingchen Li ◽  
Fei Yu ◽  
Guoyuan Zhang ◽  
...  

Author(s):  
Hugo Rene Lárraga-Altamirano ◽  
Dalia Rosario Hernández-López ◽  
Ana María Piedad-Rubio ◽  
Jesús Antonio Amador-Soni

This research work shows that with the use of remote sensing technology it is possible to more effectively fulfill two of the purposes pursued by farmers in the field; manage crops more efficiently and include environmental care in decision-making. Specifically, remote sensing is applied in the context of precision agriculture through geographic information systems (GIS), unmanned aerial vehicles (UAV), multispectral sensors that capture the reflectance of the infrared band of the light spectrum (for interpretation of the biochemical state of the crop), global geopositioning systems (GPS), among others. This study limits the use of this technology to the processing of multispectral images obtained by aerial photogrammetry, and its subsequent treatment for the generation of orthoimages, the calculation of the NDVI vegetation index and the classification of land cover by clustering. Finally, the effect of classification with RGB and multispectral images is analyzed.


Author(s):  
Arindom Ain

Abstract: Land use and land cover (LULC) provides a way to classify objects on the surface of Earth. This paper aims to identify the varying land cover classes by stacking of 6 spectral bands and 10 different generated indices from those bands together. We have considered the multispectral images of Landsat 7 for our research. It is seen that instead of using only basic spectral bands (blue, green, red, nir, swir1 and swir2) for classification, stacking relevant indices of multiple target classes like ndvi, evi, nbr, BU, etc. with basic bands generates more precise results. In this study, we have used automated clustering techniques for generating 5 different class labels for training the model. These labels are further used to develop a predictive model to classify LULC classes. The proposed classifier is compared with the SVM and KNN classifiers. The results show that this proposed strategy gives preferable outcomes over other techniques. After training the model over 50 epochs, an accuracy of 93.29% is achieved. Keywords: Land use, land cover, CNN, ISODATA, indices


2021 ◽  
Author(s):  
Sarah Becker ◽  
Craig Daughtry ◽  
Andrew Russ

Trees occur in many land cover classes and provide significant ecosystem services. Remotely sensed multispectral images are often used to create thematic maps of land cover, but accurately identifying trees in mixed land-use scenes is challenging. We developed two forest cover indices and protocols that reliably identified trees in WorldView-2 multispectral images. The study site in Maryland included coniferous and deciduous trees associated with agricultural fields and pastures, residential and commercial buildings, roads, parking lots, wetlands, and forests. The forest cover indices exploited the product of either the reflectance in red (630 to 690 nm) and red edge (705 to 745 nm) bands or the product of reflectance in red and near infrared (770 to 895 nm) bands. For two classes (trees versus other), overall classification accuracy was >77 percent for the four images that were acquired in each season of the year. Additional research is required to evaluate these indices for other scenes and sensors.


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