Semantic segmentation of multispectral satellite images for land use analysis based on embedded information

<p>The accurate land use land cover (LULC) classifications from satellite imagery are prominent for land use planning, climatic change detection and eco-environment monitoring. This paper investigates the accuracy and reliability of Support Vector Machine (SVM) classifier for classifying multi-spectral image of Hyderabad and its surroundings area and also compare its performance with Artificial Neural Network (ANN) classifier. In this paper, a hybrid technique which we refer to as Fuzzy Incorporated Hierarchical clustering has been proposed for clustering the multispectral satellite images into LULC sectors. The experimental results show that overall accuracies of LULC classification of the Hyderabad and its surroundings area are approximately 93.159% for SVM and 89.925% for ANN. The corresponding kappa coefficient values are 0.893 and 0.843. The classified results show that the SVM yields a very promising performance than the ANN in LULC classification of high resolution Landsat-8 satellite images.</p>

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Conditional GANs for semantic segmentation of multispectral satellite images

Image and Signal Processing for Remote Sensing XXIV ◽

10.1117/12.2325601 ◽

2018 ◽

Cited By ~ 2

Author(s):

Vladimir V. Kniaz

Keyword(s):

Satellite Images ◽

Semantic Segmentation ◽

Multispectral Satellite Images

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Land Use and Land Cover Classification Using Deep Belief Network for LISS-III Multispectral Satellite Images

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a1022.1191s19 ◽

2019 ◽

Vol 9 (1S) ◽

pp. 94-98

Keyword(s):

Land Use ◽

Land Cover ◽

Satellite Images ◽

Multispectral Images ◽

Deep Belief Networks ◽

Learning Techniques ◽

Multispectral Satellite Images ◽

Liss Iii ◽

High Level

Land Use and Land Cover (LULC) classification is one of the familiar applications of geographical monitoring. Deep learning techniques like deep belief networks (DBN), are used for the purpose of feature extraction and classification of multispectral images. In this proposed framework, by applying DBN, spatial and spectral features were extracted and classified with high level of classification accuracy. LISS III images of Kottayam district, Kerala were used as experimental images. This proposed framework proved that, DBN has a high ability to extract the feature and classify the multispectral images with high accuracy than traditional methods.

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DEVELOPMENT OF AN OPEN SOURCE, MACHINE LEARNING BASED TOOLSET FOR THE IDENTIFICATION OF DIKES IN SATELLITE IMAGES THROUGH SEMANTIC SEGMENTATION

10.1130/abs/2020am-357672 ◽

2020 ◽

Author(s):

Ryan Gray ◽

◽

Tushar Mittal

Keyword(s):

Machine Learning ◽

Open Source ◽

Satellite Images ◽

Semantic Segmentation

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LULC classification by semantic segmentation of satellite images using FastFCN

2020 2nd International Conference on Advanced Information and Communication Technology (ICAICT) ◽

10.1109/icaict51780.2020.9333522 ◽

2020 ◽

Author(s):

Md. Saif Hassan Onim ◽

Aiman Rafeed Bin Ehtesham ◽

Amreen Anbar ◽

A. K. M. Nazrul Islam ◽

A. K. M. Mahbubur Rahman

Keyword(s):

Satellite Images ◽

Semantic Segmentation

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Forensic Geoarchaeology in the Search for Missing Persons

Forensic Sciences ◽

10.3390/forensicsci1010003 ◽

2021 ◽

Vol 1 (1) ◽

pp. 8-15

Author(s):

Pier Matteo Barone ◽

Rosa Maria Di Maggio ◽

Silvia Mesturini

Keyword(s):

Satellite Images ◽

Social Phenomenon ◽

Geographic Profiling ◽

Missing Persons ◽

The Social ◽

Multispectral Satellite Images

Despite widespread concern over missing persons, there has always been little clarity on what the word “missing” means. Although the category of young runaways is, indeed, an important cluster, other popular concepts related to disappearances describe a portion of missing persons. Thus, the following question persists: What exactly does “missing” mean? In this brief communication, we would like to open a discussion about the social phenomenon of missing persons and the consequent deployment of people and techniques to find those persons. In particular, the benefits of some forensic geoarchaeological approaches that are not yet fully standardized will be highlighted, such as geographic profiling and the use of multispectral satellite images, in order to provide materials for future searching protocols.

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