Multiscale texture features for the retrieval of high resolution satellite images

Author(s):  
Samia Bouteldja ◽  
Assia Kourgli
2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Ruizhe Wang ◽  
Wang Xiao

Since the traditional adaptive enhancement algorithm of high-resolution satellite images has the problems of poor enhancement effect and long enhancement time, an adaptive enhancement algorithm of high-resolution satellite images based on feature fusion is proposed. The noise removal and quality enhancement areas of high-resolution satellite images are determined by collecting a priori information. On this basis, the histogram is used to equalize the high-resolution satellite images, and the local texture features of the images are extracted in combination with the local variance theory. According to the extracted features, the illumination components are estimated by Gaussian low-pass filtering. The illumination components are fused to complete the adaptive enhancement of high-resolution satellite images. Simulation results show that the proposed algorithm has a better adaptive enhancement effect, higher image definition, and shorter enhancement time.


2019 ◽  
Vol 75 ◽  
pp. 01003 ◽  
Author(s):  
Egor Dmitriev ◽  
Vladimir Kozoderov ◽  
Sergey Donskoy ◽  
Petr Melnik ◽  
Anton Sokolov

A method for automated processing high spatial resolution satellite images is proposed to retrieve inventory and bioproductivity parameters of forest stands. The method includes effective learning classifiers, inverse modeling, and regression modeling of the estimated parameters. Spectral and texture features are used to classify forest species. The results of test experiments for the selected area of Savvatievskoe forestry (Russia, Tver region) are presented. Accuracy estimates obtained using ground-based measurements demonstrate the effectiveness of using the proposed techniques to automate the process of updating information for the State Forest Inventory program of Russia.


2021 ◽  
pp. 1-11
Author(s):  
Yasser Mostafa ◽  
Mahmoud Nokrashy O. Ali ◽  
Faten Mostafa ◽  
Mohamed Yousef

2018 ◽  
Vol 50 ◽  
pp. 02007
Author(s):  
Cecile Tondriaux ◽  
Anne Costard ◽  
Corinne Bertin ◽  
Sylvie Duthoit ◽  
Jérôme Hourdel ◽  
...  

In each winegrowing region, the winegrower tries to value its terroir and the oenologists do their best to produce the best wine. Thanks to new remote sensing techniques, it is possible to implement a segmentation of the vineyard according to the qualitative potential of the vine stocks and make the most of each terroir to improve wine quality. High resolution satellite images are processed in several spectral bands and algorithms set-up specifically for the Oenoview service allow to estimate vine vigour and a heterogeneity index that, used together, directly reflect the vineyard oenological potential. This service is used in different terroirs in France (Burgundy, Languedoc, Bordeaux, Anjou) and in other countries (Chile, Spain, Hungary and China). From this experience, we will show how remote sensing can help managing vine and wine production in all covered terroirs. Depending on the winegrowing region and its specificities, its use and results present some differences and similarities that we will highlight. We will give an overview of the method used, the advantage of implementing field intra-or inter-selection and how to optimize the use of amendment and sampling strategy as well as how to anticipate the whole vineyard management.


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