Neural networks classifiers based on geocoded data and multispectral images for satellite image interpretation

Environmental education is an important function of Protected Areas among them nature reserves. It includes development of visible and available materials on tours and routes, on geographical and environmental features of a territory, and on unique species of flora and fauna. Interactive map of vegetation “Along Doppelmair’s trail” is an example of scientific information visualization. It was made for a distant and restricted core area in Barguzinsky Nature Reserve. We applied traditional geographical approaches and methods (field research, geobotanical descriptions) and modern GIS technologies (creation of unit database on landscape foundation, satellite image interpretation, infogram visualization) to create the map. As a result a GIS product is created with ArcMAP and located on the ArcGIS Online platform. This map shows characteristic vegetation types, succession stages of pyrogenic dynamics of forest geosystems formed during a century period. Infograms demonstrate information on sites with wild animal encounters, vegetation distribution, landscape features, and photographical materials. This interactive map is a way for environment protection popularization and solving some educational tasks for Protected Areas. It gives an opportunity to study changes in vegetation from Lake Baikal shoreline to mountain peaks of Bаrguzinskii Range, learn typical flora and fauna species, including endangered ones, find interesting historical facts about the reserve, and, thus, get an idea of uniqueness and fragility of nature and the importance of protection attempts.

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Symmetry perception with spiking neural networks

Scientific Reports ◽

10.1038/s41598-021-85232-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jonathan K. George ◽

Cesare Soci ◽

Mario Miscuglio ◽

Volker J. Sorger

Keyword(s):

Neural Networks ◽

Mirror Symmetry ◽

Satellite Image ◽

Image Data ◽

Coincidence Detection ◽

Spiking Neural Networks ◽

Feed Forward Neural Network ◽

Neuromorphic Computing ◽

Symmetry Perception ◽

High Level

AbstractMirror symmetry is an abundant feature in both nature and technology. Its successful detection is critical for perception procedures based on visual stimuli and requires organizational processes. Neuromorphic computing, utilizing brain-mimicked networks, could be a technology-solution providing such perceptual organization functionality, and furthermore has made tremendous advances in computing efficiency by applying a spiking model of information. Spiking models inherently maximize efficiency in noisy environments by placing the energy of the signal in a minimal time. However, many neuromorphic computing models ignore time delay between nodes, choosing instead to approximate connections between neurons as instantaneous weighting. With this assumption, many complex time interactions of spiking neurons are lost. Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. We further demonstrate that the addition of noise improves feature detectability of an image through coincidence point generation. The ability to obtain mirror symmetry from spiking neural networks can be a powerful tool for applications in image-based rendering, computer graphics, robotics, photo interpretation, image retrieval, video analysis and annotation, multi-media and may help accelerating the brain-machine interconnection. More importantly it enables a technology pathway in bridging the gap between the low-level incoming sensor stimuli and high-level interpretation of these inputs as recognized objects and scenes in the world.

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Pyramid Convolutional Neural Networks and Bottleneck Residual Modules for Classification of Multispectral Images

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss39084.2020.9324314 ◽

2020 ◽

Author(s):

Yukun Huang ◽

Jingbo Wei ◽

Wenchao Tang ◽

Chaoqi He

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Multispectral Images

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Utilization of neural networks for the estimation of aboveground forest biomass from Ikonos satellite image and multi-source geo-scientific data

2007 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2007.4423812 ◽

2007 ◽

Author(s):

P. Migolet ◽

L. Coulibaly ◽

H.G. Adegbidi ◽

E. Hervet

Keyword(s):

Neural Networks ◽

Satellite Image ◽

Forest Biomass ◽

Scientific Data

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Possibilities of deep learning neural networks for satellite image recognition

Journal of Physics Conference Series ◽

10.1088/1742-6596/1703/1/012031 ◽

2020 ◽

Vol 1703 ◽

pp. 012031

Author(s):

A N Averkin ◽

S A Yarushev

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Image Recognition ◽

Satellite Image

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Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

Journal of Science Natural Science ◽

10.18173/2354-1059.2021-0021 ◽

2021 ◽

Vol 66 (1) ◽

pp. 175-187

Author(s):

Duong Phung Thai ◽

Son Ton

Keyword(s):

Satellite Images ◽

Satellite Image ◽

Image Interpretation ◽

Image Data ◽

Vegetation Coverage ◽

Landsat 8 ◽

Mangrove Forests ◽

Forest Change ◽

Shrimp Ponds ◽

Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

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