Classification of Spectrally-Similar Land Cover Using Multi-Spectral Neural Image Fusion and the Fuzzy ARTMAP Neural Classifier

2006 ◽  
Author(s):  
M. Pugh ◽  
A. Waxman
Keyword(s):  
Land Cover ◽  
Image Fusion ◽  
Fuzzy Artmap ◽  
Neural Classifier

scholarly journals Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

2015 ◽  
Vol 45 (2) ◽  
pp. 195-202 ◽  
Author(s):  
Luiz Felipe de Almeida FURTADO ◽  
Thiago Sanna Freire SILVA ◽  
Pedro José Farias FERNANDES ◽  
Evelyn Márcia Leão de Moraes NOVO
Keyword(s):  
Land Cover ◽  
Image Fusion ◽  
Land Cover Classification ◽  
Classification Method ◽  
Data Mining Algorithm ◽  
Automated Classification ◽  
Kappa Index ◽  
Optical Images ◽  
Fusion Methods

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

scholarly journals CLASSIFICATION OF LAND-COVER THROUGH MACHINE LEARNING ALGORITHMS FOR FUSION OF SENTINEL-2A AND PLANETSCOPE IMAGERY

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 361-368
Author(s):  
M. A. Zaraza Aguilera
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Image Fusion ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
High Spectral Resolution ◽  
Variational Model ◽  
Support Vector ◽  
Sentinel 2A

Abstract. To monitor and manage the changes in the land use and land cover, is vital the process of classification; machine learning offers the potential for effective and efficient classification of remotely sensed imagery. However, not many articles have explicitly dealt with the effects of image fusion on land-cover classification accuracy. Although some studies have compared thematic mapping accuracy produced using different classification algorithms, there are no currently many studies that utilize image fusion for assessing different machine learning algorithms for classification purposes. The main aim of this study is to compare different machine learning algorithm for pixel classification of imagery fused with sensors Sentinel-2A and PlanetScope. The method used for image fusion is a variational model, the high spectral resolution of Sentinel-2A imagery and the high spatial resolution of PlanetScope imagery was fused; the machine learning algorithms evaluated are six that have been widely used in the remote sensing community: DT (Decision Tree), Boosted DT, RF (Random Forest), SVM radial base (Support Vector Machine), ANN (Artificial Neural Networks), KNN (k-Nearest Neighbors), for the classification four spectral indices (NDVI, NDMI, NDBI, MSAVI) were included, derived of the image fusion. The results show that the highest accuracy was produced by SVM radial base (OA: 87.8%, Kappa: 87%) respect to the other methods, nevertheless the methods RF, Boosted DT and KNN shown to be very powerful methods for classification of the study area.

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.

scholarly journals Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
James Brinkhoff ◽  
Justin Vardanega ◽  
Andrew J. Robson
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Image ◽  
Resource Planning ◽  
Support Vector ◽  
Perennial Crop ◽  
Perennial Crops ◽  
Object Based ◽  
Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

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