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A Comprehensive Review on Segmentation Techniques for Satellite Images

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Abstract

Segmentation of satellite images is the noteworthy and essential step for better understanding and analysis in various applications such as disaster and crisis management support, agriculture land detection, water body detection, identification of roads, buildings, transformation analysis of forested ecosystems, and translating satellite imagery to maps, where the satellite image can be utilized for remotely monitoring any specified region. This manuscript contemplates the comprehensive and comparative analysis of existing satellite image segmentation techniques with their advantages, disadvantages, experimental results, and futuristic discussion. The comprehensive and comparative analysis provides the basic platform and a new direction of research to perspective readers working in this area. In this review, existing segmentation techniques are extensively analyzed and categorized on the basis of their methodology similarities. In the reviewing process of state-of-the-art satellite image segmentation techniques, it has been noticed that the problems of semantic and instance segmentation are solved effectively using deep learning approaches. The entire review process exhibits the problem of the limited dataset, limited time to train a network, objects appearing differently from different imaging sensors, and class imbalance in semantic and instance segmentation. A fully convolutional network, U-Net, and its variants are utilized to solve these problems by applying transfer learning, synthetic data generation, artificially generated noisy data, and residual networks. This manuscript focuses on the existing work and helps to provide comparative results, challenges, and further improvement areas.

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Abbreviations

ABC:

Artificial Bee Colony

ACDE:

Automatic Clustering using modified Differential Evolution

ACPAHS:

Automatic Clustering due to Parameter Adaptive Harmony Search

ALO:

Ant-Lion Optimization

ARI:

Adjusted Rand Index

AVHRR:

Advanced Very High-Resolution Radiometer

BBM:

Bivariate Beta type-II Mixture

BGAUM:

Bivariate GAUssian Mixture

CNN:

Convolutional Neural Network

CRF:

Conditional Random Field

CS:

Cuckoo Search

DB:

Davies-Bouldin

DCE:

Dual Cross Entropy

DCNN:

Deep Convolutional Neural Network

DCPSO:

Dynamic Clustering using Particle Swarm Optimization

DE:

Differential Evolution

DFL:

Dual Focal Loss

DHHO/M:

Dynamic HHO with Mutation

DSN:

Deeply Supervised Nets

EM:

Expectation-Maximization

EMA:

Exchange Market Algorithm

EMT:

Edge Maximization Technique

FCIDE:

Fuzzy Clustering using Improved Differential Evolution

FCM:

Fuzzy C-Means

FCN:

Fully Convolutional Network

FL:

Focal Loss

FSIM:

Feature Similarity Index

GA:

Genetic Algorithm

GAFC:

Genetic Algorithm based Fuzzy Clustering C-means

GAFPSC:

Genetic Algorithm with the Fuzzy Point Symmetry-based Clustering

GAPS:

Genetic Algorithm with Point Symmetry

GCUK:

Genetic Clustering with an Unknown number of clusters

GHMRF:

Gaussian Hidden Markov Random Field

GOA:

Grasshopper Optimization Algorithm

GWO:

Grey Wolf Optimizer

HE:

Holder Exponent

HHO:

Harris Hawks Optimization

HMS:

Human Mental Search

HNN:

Hopfield Neural network

HSI:

Hue Saturation and Intensity

HSV:

Hue Saturation Value

IDSA:

Improved Differential Search Algorithm

IEPO:

Improved Emperor Penguin Optimization

IOU:

Intersection Over Union

IRS:

Indian Remote Sensing

ISODATA:

Iterative Self-Organizing Data Analysis

KHA:

Krill Herd Algorithm

KI:

Kappa Index

LMVO:

Multi-Verse Optimization Algorithm based on L‘evy flight Improvement

LOG:

Laplacian of Gaussian

MABC:

Modified Artificial Bee Colony Algorithm

MAE:

Mean Absolute Error

MC-WBDN:

Multi-Channel Water Body Detection Network

MCE:

Minimum Cross Entropy

MCVGAPS:

Multicenter based Automatic Clustering Technique

MFO:

Moth Flame Optimization

mIOU:

mean IOU

MLP:

Multi-Layer Perceptron

MNDWI:

Modified Normalized Difference Water Index

MoDEFC:

Modified Differential Evolution-based Fuzzy Clustering

MQE:

Mean Quadratic Error

MSE:

Mean Squared Error

MSEPO:

Multi-strategy Emperor Penguin optimizer

MSS:

Multispectral Scanner System

MTMFO:

Multilevel Thresholding Moth-Flame Optimization

MVO:

Multiverse Optimization

NAE:

Normalized Absolute Error

NCC:

Normalized Cross Correlation

PA-FoV:

Pixel Adaptive Field of View

PAHS:

Parameter Adaptive Harmony Search

PSNR:

Peak Signal-to-Noise Ratio

PSO:

Particle Swarm Optimization

RISA:

Region-based Image Segmentation Algorithm

RMSE:

Root Mean Square Error

SCM:

Shadowed Clustering Method

SDC:

Sorensen-Dice Coefficient

SEM:

Stochastic Expectation-Maximization

SNR:

Signal to Noise Ratio

SSIM:

Structural Similarity Index

SVM:

Support Vector Machine

SVMeFC:

SVM Ensemble Fuzzy clustering

SWIR:

Short-Wave Infrared

Sym-index:

Symmetry Distance-based Cluster Validity Index

TLBO:

Teaching Learning-based Optimization

TLMVO:

Tournament-based L‘evy Multiverse Optimization

TM:

Thematic Mapper

TSOM:

Kohonen Self Organizing Map with Thresholding

UAVs:

Unmanned Aerial Vehicles

VHR:

Very High Resolution

WCE:

Weighted Cross Entropy

WDO:

Wind Driven Optimization

XB:

Xie-Beni

YIQ:

Luminance in Phase Quadrature

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Acknowledgements

We would like to thank the reviewers who devoted their valuable time and effort to reviewing the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped to improve the quality of the manuscript.

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Authors and Affiliations

  1. Dr. A.P.J. Abdul Kalam Technical University, Lucknow, 226031, Uttar Pradesh, India

    Neha Bagwari

  2. Department of Computer Science & Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, 201206, Uttar Pradesh, India

    Sushil Kumar

  3. Department of Computer Science & Engineering, Harcourt Butler Technical University, Kanpur, 208002, Uttar Pradesh, India

    Vivek Singh Verma

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  1. Neha Bagwari
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  2. Sushil Kumar
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  3. Vivek Singh Verma
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Correspondence to Sushil Kumar.

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Bagwari, N., Kumar, S. & Verma, V.S. A Comprehensive Review on Segmentation Techniques for Satellite Images. Arch Computat Methods Eng 30, 4325–4358 (2023). https://doi.org/10.1007/s11831-023-09939-4

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