Urban Land Cover Classification With Missing Data Modalities Using Deep Convolutional Neural Networks

Kampffmeyer, Michael; Salberg, Arnt-Børre; Jenssen, Robert

doi:10.1109/jstars.2018.2834961

Cited by 52 publications

(28 citation statements)

References 34 publications

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“…In this study, two machine learning algorithms including RF and SVM were applied to crop classification. Recently, deep learning algorithms including convolutional neural network (CNN) were widely applied to remote sensing data classification [54][55][56]. Despite the promising performance of CNN, Kim et al [57] reported that the training sample size has greater effects on the accuracy of CNN than that of SVM in crop classification, indicating a need for numerous training samples for improved CNN classification performance.…”

Section: Classification Methodsmentioning

confidence: 99%

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Kwak

Park

2019

Applied Sciences

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Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are available for crop classification. In this study, we investigate the potential of gray-level co-occurrence matrix (GLCM)-based texture information for crop classification with time-series UAV images and machine learning classifiers including random forest and support vector machine. In particular, the impact of combining texture and spectral information on the classification performance is evaluated for cases that use only one UAV image or multi-temporal images as input. A case study of crop classification in Anbandegi of Korea was conducted for the above comparisons. The best classification accuracy was achieved when multi-temporal UAV images which can fully account for the growth cycles of crops were combined with GLCM-based texture features. However, the impact of the utilization of texture information was not significant. In contrast, when one August UAV image was used for crop classification, the utilization of texture information significantly affected the classification performance. Classification using texture features extracted from GLCM with larger kernel size significantly improved classification accuracy, an improvement of 7.72%p in overall accuracy for the support vector machine classifier, compared with classification based solely on spectral information. These results indicate the usefulness of texture information for classification of ultra-high-spatial-resolution UAV images, particularly when acquisition of time-series UAV images is difficult and only one UAV image is used for crop classification.

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Section: Classification Methodsmentioning

confidence: 99%

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Kwak

Park

2019

Applied Sciences

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“…In recent years, deep neural network has shown excellent performance in the task of remote sensing image classification and segmentation [16,[39][40][41]. Feature maps output from different layers in neural network reflect different characteristics of remote sensing images.…”

Section: Urban Functional Regions Classification Based On Remote Sensmentioning

confidence: 99%

A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

Qing

Han³

et al. 2020

Remote Sensing

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For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.

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“…Note that VHR data may not be available in all areas every year. This requires DNN able to handle missing modalities (Kampffmeyer et al, 2018). Alternatively, low-level fusion is insufficiently addressed.…”

Section: Optimal Exploitation Of Data Sourcesmentioning

confidence: 99%

Current Challenges in Operational Very High Resolution Land-Cover Mapping

Mallet

Bris

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Many land-cover products have been made available for a large range of end-users over the last ten years, even at global scales. In particular, remote sensing data analysis has proved to be the most feasible solution for automation purposes, at multiple spatial scales. However, current solutions are not sufficient for designing better products, adapted to real-case applications, operational constraints, and the generation of services, built upon these core layers. In this paper, we review the main requirements and the recent changes in remote sensing for the specific case of very high resolution land-cover mapping. We also comment current and evaluate challenges for the optimal exploitation of Earth Observation images with the aim of automatically generating maps tailored to specific end-users’ needs. We advocate for more challenging large-scale benchmarks and for human-in-the-loop solutions.

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Urban Land Cover Classification With Missing Data Modalities Using Deep Convolutional Neural Networks

Cited by 52 publications

References 34 publications

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

Impact of Texture Information on Crop Classification with Machine Learning and UAV Images

A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

Current Challenges in Operational Very High Resolution Land-Cover Mapping

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