Changed Detection of Landsat 8 Imagery using Object Based Image Analysis with Particle Swarm Optimization

2020 ◽  
Vol 7 (1) ◽  
pp. 1
Author(s):  
Goutam Saha ◽  
Amitabha Nath ◽  
Amos Bortiew
Keyword(s):  
Image Analysis ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

Object-based image analysis of suburban landscapes using Landsat-8 imagery

2018 ◽  
Vol 12 (6) ◽  
pp. 720-736 ◽  
Author(s):  
Ming Shang ◽  
Shixin Wang ◽  
Yi Zhou ◽  
Cong Du ◽  
Wenliang Liu
Keyword(s):  
Image Analysis ◽  
Landsat 8 ◽  
Object Based Image Analysis ◽  
Object Based

scholarly journals Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

2019 ◽  
Vol 11 (21) ◽  
pp. 2583 ◽  
Author(s):  
Payam Najafi ◽  
Hossein Navid ◽  
Bakhtiar Feizizadeh ◽  
Iraj Eskandari ◽  
Thomas Blaschke
Keyword(s):  
Image Analysis ◽  
Agricultural Research ◽  
Soil Surface ◽  
Control Measures ◽  
Landsat 8 ◽  
Surface Residue ◽  
Ground Truth Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2A

Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.

Multi-Thresholding Based on Symmetric Tsallis-Cross Entropy and Particle Swarm Optimization

2013 ◽  
Vol 760-762 ◽  
pp. 1457-1461
Author(s):  
Jun Yin ◽  
Yi Quan Wu ◽  
Li Zhu
Keyword(s):  
Image Analysis ◽  
Particle Swarm Optimization ◽  
Shannon Entropy ◽  
Particle Swarm ◽  
Cross Entropy ◽  
Automatic Image Analysis ◽  
Criterion Function ◽  
Swarm Optimization ◽  
Optimal Thresholds ◽  
Thresholding Algorithm

Multi-thresholding is an important step for automatic image analysis. In this paper, a multi-thresholding method based on symmetric Tsallis-cross entropy and uniform searching particle swarm optimization (UPSO) is proposed. The criterion function using symmetric Tsallis-cross entropy can make the grayscale within the background cluster and the object cluster uniform. Since the exhaustive multi-thresholding algorithm would be too time-consuming, UPSO algorithm is adopted to find the optimal thresholds quickly and accurately. A large number of experimental results show that, compared with related multi-thresholding methods based on Shannon entropy and Tsallis entropy, the proposed method is effective and rapid. It can obtain more accurate boundary shape and clearer details of object.

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