scholarly journals Modeling global geometric spatial information for rotation invariant classification of satellite images

PLoS ONE ◽  
2019 ◽  
Vol 14 (7) ◽  
pp. e0219833 ◽  
Author(s):  
Nouman Ali ◽  
Bushra Zafar ◽  
Muhammad Kashif Iqbal ◽  
Muhammad Sajid ◽  
Muhammad Yamin Younis ◽  
...  
Keyword(s):  
Satellite Images ◽  
Spatial Information ◽  
Rotation Invariant

scholarly journals Deep Convolutional Neural Network for Mapping Smallholder Agriculture Using High Spatial Resolution Satellite Image

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2398 ◽  
Author(s):  
Bin Xie ◽  
Hankui K. Zhang ◽  
Jie Xue
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Land Cover ◽  
Convolutional Neural Network ◽  
Satellite Images ◽  
Spatial Information ◽  
Deep Convolutional Neural Network ◽  
Crop Types ◽  
Pooling Strategy

In classification of satellite images acquired over smallholder agricultural landscape with complex spectral profiles of various crop types, exploring image spatial information is important. The deep convolutional neural network (CNN), originally designed for natural image recognition in the computer vision field, can automatically explore high level spatial information and thus is promising for such tasks. This study tried to evaluate different CNN structures for classification of four smallholder agricultural landscapes in Heilongjiang, China using pan-sharpened 2 m GaoFen-1 (meaning high resolution in Chinese) satellite images. CNN with three pooling strategies: without pooling, with max pooling and with average pooling, were evaluated and compared with random forest. Two different numbers (~70,000 and ~290,000) of CNN learnable parameters were examined for each pooling strategy. The training and testing samples were systematically sampled from reference land cover maps to ensure sample distribution proportional to the reference land cover occurrence and included 60,000–400,000 pixels to ensure effective training. Testing sample classification results in the four study areas showed that the best pooling strategy was the average pooling CNN and that the CNN significantly outperformed random forest (2.4–3.3% higher overall accuracy and 0.05–0.24 higher kappa coefficient). Visual examination of CNN classification maps showed that CNN can discriminate better the spectrally similar crop types by effectively exploring spatial information. CNN was still significantly outperformed random forest using training samples that were evenly distributed among classes. Furthermore, future research to improve CNN performance was discussed.

scholarly journals The accounting method of secrecy properties of image in remote sensing of the Earth

2021 ◽  
Vol 937 (4) ◽  
pp. 042073
Author(s):  
V S Khramov ◽  
S A Kramarov ◽  
A R Belyaev ◽  
E Golubenko ◽  
E Grebenyuk
Keyword(s):  
Computer Games ◽  
Satellite Images ◽  
Spatial Information ◽  
Fine Art ◽  
Practical Implementation ◽  
Geometric Means ◽  
The Earth ◽  
The Many ◽  
Information Objects

Abstract Among the many ways of representing and processing space information, polygonal ones are not so often found, although polygons, as geometric means of describing spatial information objects, are very common in wide internet content. In computer games, design, even in fine art and many other applications, the polygonal description of such objects is not only widely present, but also received from developers, both theory and practical implementation, a fairly wide set of mathematical and software tools. In this article, it is proposed to extend some well-proven developments in the analysis and transformation of computer images to real digitized satellite images, as well as to use combinations of satellite processing methods and polygonal methods which are characteristic for virtual reality conditions. In addition, the paper provides approaches for detecting hidden and being hidden artifacts – information bookmarks. The definitions of the main directions of intel-latency are given, as an important property of complex images of the Earth’s surface obtained during space monitoring of problem-oriented territories. A variant of the classification of the types of manifestation of this property is given, as well as its description in the language of soft models.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

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.

scholarly journals Improving Land Cover Classification Using Genetic Programming for Feature Construction

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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