NDLI: Improved framework of many-objective evolutionary algorithm to handle cloud detection problem in satellite imagery

Content Provider	IET Digital Library
Author	Gupta, Rachana Nanda, Satyasai Jagannath
Abstract	Automatic cloud detection algorithm based on supervised learning approach has emerged due to its effectiveness in extracting weather information in satellite images. However, algorithm requires field-expert intervention with huge database of training samples to evaluate its clustering performance. Moreover, lacking in availability of labelled data makes difficult to train the input samples. Therefore, this article puts forward unsupervised many-objective evolutionary clustering technique to discriminate cloudy regions on varying characteristic of underlying surfaces. The study begins with the modification in search capability of θ -NSGA-III optimisation algorithm by incorporating penalised vector angle concept in associate operator. The analysis of proposed approach has been carried out on benchmark many-objective DTLZ test problems, compared against original θ -NSGA-III and NSGA-III algorithms. The proposed modified θ -NSGA-III has been further utilised as clustering technique to solve unsupervised cloud detection problem. Optimal centroid vector for clustering using proposed approach is obtained through modified crossover operator, mutation operator and environmental selection method. Experimental results reveal that proposed approach outperforms comparative many-objective algorithms, MOEA/D and NSGA-III for Landsat 8, MODIS and NOAA satellite images with lower classification average error of 2.44 % in cloud detection for most of the evaluated test cases.
Starting Page	4795
Ending Page	4807
Page Count	13
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 17, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0535
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/17
Journal	IET Image Processing
Publisher Date	2021-01-18
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Atmospheric Technique Atmospheric/ionospheric/magnetospheric Technique And Equipment Automatic Cloud Detection Algorithm Benchmark Many-objective DTLZ Test Problem Cloud Physics Clouds Cloudy Regions Clustering Performance Computer Vision And Image Processing Technique Data And Information; Acquisition, Processing, Storage And Dissemination in Geophysics Environmental Selection Method Field-expert Intervention Geography And Cartography Computing Geophysical Image Processing Huge Database Image Classification Improved Framework Instrumentation And Technique For Geophysical, Hydrospheric And Lower Atmosphere Research Labelled Data Many-objective Evolutionary Algorithm Many-objective Evolutionary Clustering Technique Modified Crossover Operator NSGA-III Algorithm NSGA-III Optimisation Algorithm Optical, Image And Video Signal Processing Optimal Centroid Vector Satellite Imagery Search Capability Supervised Learning Supervised Learning Approach Training Sample Underlying SurFace Unsupervised Cloud Detection Problem Vector Angle Concept Weather Information
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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