Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data

Gibril, Mohamed Barakat A.; Idrees, Mohammed Oludare; Yao, Kouame; Shafri, Helmi Zulhaidi Mohd

doi:10.1117/1.jrs.12.016036

Cited by 31 publications

(25 citation statements)

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“…Recently, cameras mounted on UAVs have enabled the acquisition of higher quality images from remote locations, especially those of wet and cropland images. Machine learning has also played an important role, where algorithms such as Support Vector Machine (SVM), Logistic Regression and Artificial Neural Networks (ANN) have been used to perform automatic land classification [23,24]. Lie et al [25] used high-quality images with OBIA based on multi-view information.…”

Section: Related Studiesmentioning

confidence: 99%

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

et al. 2019

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In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)-digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense.

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Section: Related Studiesmentioning

confidence: 99%

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

et al. 2019

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“…Experimentation was undertaken with unsupervised clustering: k-means, fuzzy C-means, watershed, and ELKI (Environment for Loping KDD-applications supported by Index Structures) [77][78][79][80][81]. Machine learning techniques, Random Forest (RF) [82][83][84][85] and Naïve Bayes were further utilized to classify and provide greater clarity to the feature distribution. The RF technique, which was found to yield the clearest results, is a nonparametric method for modelling the continuous and discrete data of decision tree methods and is a well-established and reliable process [86][87][88].…”

Section: Advanced Processingmentioning

confidence: 99%

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

Brooke

Clutterbuck

2019

Remote Sensing

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There is a long history of the use of aerial imagery for archaeological research, but the application of multisensor image data has only recently been facilitated by the development of unmanned aerial vehicles (UAVs). Two archaeological sites in the East Midlands U.K. that differ in age and topography were selected for survey using multisensor imaging from a fixed-wing UAV. The aim of this study was to determine optimum methodology for the use of UAVs in examining archaeological sites that have no obvious surface features and examine issues of ground control target design, thermal effects, image processing and advanced filtration. The information derived from the range of sensors used in this study enabled interpretation of buried archaeology at both sites. For any archaeological survey using UAVs, the acquisition of visible colour (RGB), multispectral, and thermal imagery as a minimum are advised, as no single technique is sufficient to attempt to reveal the maximum amount of potential information.

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“…In fact, GEOBIA has extensively been used in the literature as a fundamental approach for feature extraction from VHSR images, due to its advantages over the traditional per-pixel classifiers [13][14][15]. While pixel-based classification methods only consider the spectral properties of individual pixels, GEOBIA enables the recognition of multiscale objects from a single image or across several images, and makes the best use of integration between spectral, spatial, textural, thermal, and backscattering values, vector data, and contextual information to accurately extract natural and human-made features [16][17][18][19]. However, GEOBIA performance might be affected by the image segmentation quality, the selection of the most relevant features, the selection of the representative training samples, and the classification method [20].…”

Section: Introductionmentioning

confidence: 99%

“…One of the most popular image segmentation algorithms is MRS. MRS is controlled by three user-defined parameters: (a) scale, (b) shape/color weight, and (c) compactness/smoothness weight. The size and the shape of the created image objects are critically dependent on the combinations of these parameters [16,22]. Therefore, changing these combinations using a trial-and-error approach can be a very subjective and time-consuming process, leading to various choices of delineating the features of interest that may not produce meaningful segments [23,24].…”

Section: Introductionmentioning

confidence: 99%

Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery

et al. 2018

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Accurate mapping of date palm trees is essential for their sustainable management, yield estimation, and environmental studies. In this study, we integrated geographic object-based image analysis, class-specific accuracy measures, fractional factorial design, metaheuristic feature-selection technique, and rule-based classification to detect and map date palm trees from very-high-spatial-resolution (VHSR) aerial images of two study areas. First, multiresolution segmentation was optimized through the synergy of the F1-score accuracy measure and the robust Taguchi design. Second, ant colony optimization (ACO) was adopted to select the most significant features. Out of 31 features, only 12 significant color invariants and textural features were selected. Third, based on the selected features, the rule-based classification with the aid of a decision tree algorithm was applied to extract date palm trees. The proposed methodology was developed on a subset of the first study area, and ultimately applied to the second study area to investigate its efficiency and transferability. To evaluate the proposed classification scheme, various supervised object-based algorithms, namely random forest (RF), support vector machine (SVM), and k-nearest neighbor (k-NN), were applied to the first study area. The result of image segmentation optimization demonstrated that segmentation optimization based on an integrated F1-score class-specific accuracy measure and Taguchi statistical design showed improvement compared with objective function, along with the Taguchi design. Moreover, the result of the feature selection by ACO outperformed, with almost 88% overall accuracy, several feature-selection techniques, such as chi-square, correlation-based feature selection, gain ratio, information gain, support vector machine, and principal component analysis. The integrated framework for palm tree detection outperformed RF, SVM, and k-NN classification algorithms with an overall accuracy of 91.88% and 87.03%, date palm class-specific accuracies of 0.91 and 0.89, and kappa coefficients of 0.90 and 0.85 for the first and second study areas, respectively. The proposed integrated methodology demonstrated a highly efficient and promising tool to detect and map date palm trees from VHSR aerial images.

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Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data

Cited by 31 publications

References 50 publications

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Land Cover Classification from fused DSM and UAV Images Using Convolutional Neural Networks

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

Image Segmentation Parameter Selection and Ant Colony Optimization for Date Palm Tree Detection and Mapping from Very-High-Spatial-Resolution Aerial Imagery

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