Deep neural network ensembles for remote sensing land cover and land use classification

Ekim, Burak; Sertel, Elif

doi:10.1080/17538947.2021.1980125

Cited by 24 publications

(7 citation statements)

References 24 publications

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“…Each input image pixel is assigned to a pre-determined object category or LULC class in the semantic segmentation process, which is not limited to only one object category such as roads or buildings but considers various classes simultaneously [2,4]. The increase in the number and complexity of LULC categories to be determined makes this problem more challenging [5]. The semantic segmentation output includes the boundaries of objects and their related classes that provide both spatial and thematic information on the region of interest.…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

et al. 2022

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Deep learning-based segmentation of very high-resolution (VHR) satellite images is a significant task providing valuable information for various geospatial applications, specifically for land use/land cover (LULC) mapping. The segmentation task becomes more challenging with the increasing number and complexity of LULC classes. In this research, we generated a new benchmark dataset from VHR Worldview-3 images for twelve distinct LULC classes of two different geographical locations. We evaluated the performance of different segmentation architectures and encoders to find the best design to create highly accurate LULC maps. Our results showed that the DeepLabv3+ architecture with an ResNeXt50 encoder achieved the best performance for different metric values with an IoU of 89.46%, an F-1 score of 94.35%, a precision of 94.25%, and a recall of 94.49%. This design could be used by other researchers for LULC mapping of similar classes from different satellite images or for different geographical regions. Moreover, our benchmark dataset can be used as a reference for implementing new segmentation models via supervised, semi- or weakly-supervised deep learning models. In addition, our model results can be used for transfer learning and generalizability of different methodologies.

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Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

et al. 2022

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“…is conclusion pointed out that the support vector machine method has better accuracy. Ekim et al [17] proposed a land remote sensing data classification method based on the semantic segmentation method. Deep neural network technology is used by him in remote sensing data classification, and the data set can come from multiple types of target remote sensing data sources.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Data Processing of Urban Land Using Based on Artificial Neural Network

Zhang

2022

Scientific Programming

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With the rapid development of urbanization, the utilization rate of land has become the focus of attention. Remote sensing technology can provide a large amount of data for the prediction of urban land. It is also a thorny problem to find the correlation between the complex data of land change. The neural network technology has obvious advantages in finding the mapping relationship between high-dimensional and nonlinear data. This paper combines the dynamic changes of urban land and neural network methods to analyze the utilization rate and coverage of urban land in the future. In this paper, the data obtained by remote sensing technology is normalized and clustered to classify different types of urban land. Convolutional neural networks and long-short-term memory neural networks are used to extract the spatial and temporal dynamic characteristics of urban land use. The research results show that the clustering method used in this paper can reasonably classify different urban land types, especially the classification of buildings. The method of predicting the future trend of land use is also in line with the dynamic process of land use. The largest prediction error comes from the prediction of the building, and the largest error is only 2.56%, which is a reasonable error range. The smallest error does not exceed 1%, and the correlation coefficient between the real and predicted values of urban land use types reaches 0.9698.

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“…Notably, Cao et al [47] bolstered the classification accuracy of the ISPRS 2-D dataset by harnessing complementarities between diverse models, coalescing to jointly learn the solution domains of multiple semantic segmentation models. Additionally, Ekim et al [48], premised on the training process of three loss optimization methods, honed the integration of distinct model outputs to fortify the robustness of the landcover classification model.…”

Section: 2 Research On Fine Landcover Classification With Semantic Se...mentioning

confidence: 99%

ABNet: An Aggregated Backbone Network Architecture for Fine Landcover Classification

Si,

Wang,

et al. 2024

Remote Sensing

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High-precision landcover classification is a fundamental prerequisite for resource and environmental monitoring and land-use status surveys. Imbued with intricate spatial information and texture features, very high spatial resolution remote sensing images accentuate the divergence between features within the same category, thereby amplifying the complexity of landcover classification. Consequently, semantic segmentation models leveraging deep backbone networks have emerged as stalwarts in landcover classification tasks owing to their adeptness in feature representation. However, the classification efficacy of a solitary backbone network model fluctuates across diverse scenarios and datasets, posing a persistent challenge in the construction or selection of an appropriate backbone network for distinct classification tasks. To elevate the classification performance and bolster the generalization of semantic segmentation models, we propose a novel semantic segmentation network architecture, named the aggregated backbone network (ABNet), for the meticulous landcover classification. ABNet aggregates three prevailing backbone networks (ResNet, HRNet, and VoVNet), distinguished by significant structural disparities, using a same-stage fusion approach. Subsequently, it amalgamates these networks with the Deeplabv3+ head after integrating the convolutional block attention mechanism (CBAM). Notably, this amalgamation harmonizes distinct scale features extracted by the three backbone networks, thus enriching the model’s spatial contextual comprehension and expanding its receptive field, thereby facilitating more effective semantic feature extraction across different stages. The convolutional block attention mechanism primarily orchestrates channel adjustments and curtails redundant information within the aggregated feature layers. Ablation experiments demonstrate an enhancement of no less than 3% in the mean intersection over union (mIoU) of ABNet on both the LoveDA and GID15 datasets when compared with a single backbone network model. Furthermore, in contrast to seven classical or state-of-the-art models (UNet, FPN, PSPNet, DANet, CBNet, CCNet, and UPerNet), ABNet evinces excellent segmentation performance across the aforementioned datasets, underscoring the efficiency and robust generalization capabilities of the proposed approach.

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Deep neural network ensembles for remote sensing land cover and land use classification

Cited by 24 publications

References 24 publications

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 Images

Remote Sensing Data Processing of Urban Land Using Based on Artificial Neural Network

ABNet: An Aggregated Backbone Network Architecture for Fine Landcover Classification

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