Hyperspectral satellite image classification using small training data from its samples

Fedoseev, Victor

doi:10.1088/1742-6596/1096/1/012042

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…From the point of view of the plant-community analysis, the composites of multitemporal multispectral images obtained during the vegetation season provide a more comprehensive vegetation description because they reflect the differences in vegetation phenology. The classification is based on the spatial processing methods and the pixel-wise classification methods investigated in [17][18]. Originally, the technology was tested for the vegetation classification using hyperspectral data.…”

Section: Supervised Local Classification Technology For Detailed Forementioning

confidence: 99%

“…Image Processing and Earth Remote Sensing A Y Denisova, L M Kavelenova, E S Korchikov, A V Pomogaybin, N V Prokhorova, D A Terentyeva, V A Fedoseev and N V Yankov Finally, spatial post-processing is performed. This kind of spatial post-processing was proposed in [18]. It is organized as the sliding window filter of size M M  and threshold T .…”

Section: Classification Technologymentioning

confidence: 99%

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Recognition of forest and shrub communities on the base of remotely sensed data supported by ground studies

Denisova

Kavelenova

Корчиков³

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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The forest and shrub communities are important components of the environment and provide a wide spectrum of ecological services. In the Samara region the forest and shrub cover is dispersed on the territory what makes its monitoring difficult. The forest areas are limited by natural and anthropogenic reasons since Samara region is a forest-steppe territory with a high level of human activity. The shrub communities are mostly the secondary ecosystems incorporated in natural grassy communities, agricultural fields or enclosing to forests. These specific ecosystems can be recognized on remote sensing data including satellite images supported by preliminary ground surveys. In this article, we present the study of the forest and shrub communities recognition using remote sensing images and ground surveys in the Samara region. We describe a process of the test site selection for remote sensing data verification and discuss the results of applying the author’s classification technology for multispectral remote sensing composites to classify forest communities in the Samara region

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Section: Supervised Local Classification Technology For Detailed Forementioning

confidence: 99%

Section: Classification Technologymentioning

confidence: 99%

Recognition of forest and shrub communities on the base of remotely sensed data supported by ground studies

Denisova

Kavelenova

Корчиков³

et al. 2019

Proceedings of the v International Conference Information Technology and Nanotechnology 2019

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“…For the first stage of thematic classification, we used the previously developed technology [7], which includes the following main steps:…”

Section: Detection Of Forest Belts In Space Imagesmentioning

confidence: 99%

“…Spatial post-processing of classification results using nonlinear filters. The results of our previous studies [7] showed that the performance of this technology is the possibility of achieving high classification accuracy even in the case of a small training set selected from the spatially localized fragments of the analyzed image. It is convenient to use parts of the selected control and measuring polygons as such fragments, which should be supplemented with examples of other samples of the underlying surface.…”

Section: Detection Of Forest Belts In Space Imagesmentioning

confidence: 99%

Methods of detection and ecological state evaluation of protective forest belts using complex ground survey and remote sensing data processing

Kavelenova

Корчиков

Prokhorova

et al. 2018

Collection of Selected Papers of the IV International Conference on Information Technology and Nanotechnology

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The protective forest belt system of Samara region consists of several components created from the late 19th to the early 20th century, in the middle of the 20th century, in the 60-70s of the 20 th century, and more recent plantings. In the forest-steppe and steppe regions with a high level of agricultural transformations, the natural biological equilibrium of ecosystems is significantly disturbed. The protective forest belts play an important role in natural and anthropogenic landscapes conservation in Samara region. Because of numerous dispersed forest belt distribution throughout the Samara region territory, their different ownership and the lack of sufficient funds for their monitoring, it is difficult to organize belt state monitoring on the ground. The use of space imagery, which is processed during verification using data obtained from reference polygons, can help to overcome this situation. The peculiarity of forest belts as an extended object of relatively small width actualizes the task of developing the methods for their condition assessing. In this paper, some results of this work are analyzed for reference areas of field shelterbelts and roadside forest belts of the Samara region.

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“…Water, soil, and vegetation are the three types of objects under investigation. The classification results were investigated for 16 In 2018, Fedoseev conducted a research project to develop an appropriate multi-stage methodology for the conceptual classification of hyper-spectral satellite images [7]. In this study, they used two hyperspectral images: Indian Pines and Pavia University.…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture

2021

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This study has developed a CNN model applied to classify the eight classes of land cover through satellite images. Early detection of deforestation has become one of the study’s objectives. Deforestation is the process of reducing natural forests for logging or converting forest land to non-forest land. The study considered two training models, a simple four hidden layer CNN compare with Alexnet architecture. The training variables such as input size, epoch, batch size, and learning rate were also investigated in this research. The Alexnet architecture produces validation accuracy over 100 epochs of 90.23% with a loss of 0.56. The best performance of the validation process with four hidden layers CNN got 95.2% accuracy and a loss of 0.17. This performance is achieved when the four hidden layer model is designed with an input size of 64 × 64, epoch 100, batch size 32, and learning rate of 0.001. It is expected that this land cover identification system can assist relevant authorities in the early detection of deforestation.

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Hyperspectral satellite image classification using small training data from its samples

Cited by 8 publications

References 10 publications

Recognition of forest and shrub communities on the base of remotely sensed data supported by ground studies

Recognition of forest and shrub communities on the base of remotely sensed data supported by ground studies

Methods of detection and ecological state evaluation of protective forest belts using complex ground survey and remote sensing data processing

Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture

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