Classification of Satellite Images Using the Cellular Automata Approach

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

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Classification of Surface Natural Resources based On U-NET and GF-1 Satellite Images

2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) ◽

10.1109/iccwamtip51612.2020.9317315 ◽

2020 ◽

Author(s):

Mu-Jie Li ◽

Ming-Cang Zhu ◽

Zhigang Ma ◽

Peng-Shan Li ◽

Xiao-Bo Zhang ◽

...

Keyword(s):

Natural Resources ◽

Satellite Images

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Improving Land Cover Classification Using Genetic Programming for Feature Construction

Remote Sensing ◽

10.3390/rs13091623 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1623

Author(s):

João E. Batista ◽

Ana I. R. Cabral ◽

Maria J. P. Vasconcelos ◽

Leonardo Vanneschi ◽

Sara Silva

Keyword(s):

Land Cover ◽

Genetic Programming ◽

Satellite Images ◽

State Of The Art ◽

Binary Classification ◽

Feature Construction ◽

Classification Problems ◽

Construction Methods ◽

Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

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