Expanding an urban structure type mapping approach from a subarea to the entire city of Berlin

Voltersen, M.; Berger, Christian; Hese, S.; Schmullius, C.

doi:10.1109/jurse.2015.7120462

Cited by 8 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…W ITH the development of satellite imaging technology, the number of RS images increases rapidly, especially the acquisition of high-resolution RS images. Analyzing and understanding these remote sensing images, such as identification or classification, brings new opportunities for more accurate surface monitoring and management and has received extensive attention [1], [2]. Especially, RS image scene classification, which attempts to allocate a label to the RS image based on a variety of semantic categories, has been widely used in the urban planning [3], environment monitoring [4],…”

Section: Introductionmentioning

confidence: 99%

Attention-Aware Deep Feature Embedding for Remote Sensing Image Scene Classification

Chen

Han

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Due to the wide application of Remote Sensing (RS) image scene classification, more and more scholars activate great attention to it. With the development of the Convolutional Neural Network (CNN), the CNN-based methods of RS image scene classification have made impressive progress. In the existing works, most of the architectures just considered the global information of the RS images. However, the global information contains a large number of redundant areas that diminish the classification performance and ignore the local information that reflects more fine spatial details of local objects. Furthermore, most CNN-based methods assign the same weights to each feature vector causing the mode to fail to discriminate the crucial features. In this paper, a novel method by Two-branch Deep Feature Embedding with a Dual Attention-Aware module for RS image scene classification is proposed. In order to mine more complementary information, we extract global semanticbased features of high level and local object-based features of low level by the Two-branch Deep Feature Embedding (TDFE) module. Then, to focus selectively on the key global-semantics feature maps as well as the key local regions, we propose a Dual Attention-Aware (DAA) module to attain those key information. We conduct extensive experiments to verify the superiority of our proposed method, and the experimental results obtained on two widely used RS scene classification benchmarks demonstrate the effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Attention-Aware Deep Feature Embedding for Remote Sensing Image Scene Classification

Chen

Han

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Remote sensing scene classification is one of the most important remote sensing applications [1]. With the advance of remote sensing technology, it is more accessible to collect high resolution satellite imagery, which provides more feasibility to get Land-Use/Land-Cover (LULC) information for spatial pattern analysis [2]- [5], such as urban planning, precision agriculture, and natural environmental monitoring. The core technique of scene classification is high-efficiency to construct discriminating feature representation between raw visual data and its semantic category.…”

Section: Introductionmentioning

confidence: 99%

Multi-Layers Feature Fusion of Convolutional Neural Network for Scene Classification of Remote Sensing

Sha

2019

IEEE Access

View full text Add to dashboard Cite

Remote sensing scene classification is still a challenging task in remote sensing applications. How to effectively extract features from a dataset with limited scale is crucial for improvement of scene classification. Recently, convolutional neural network (CNN) performs impressively in different fields of computer vision and has been used for remote sensing. However, most works focus on the feature maps of the last convolution layer and pay little attention to the benefits of additional layers. In fact, the feature information hidden in different layers has potential for feature discrimination capacity. The most attention of this work is how to explore the potential of multiple layers from a CNN model. Therefore, this paper proposes multi-layers feature fusion based on CNN and designs a fusion module to solve relevant issues of fusion. In this module, firstly, all the feature maps are transformed to match sizes mutually due to infeasible fusion of feature maps with different scales; then, two fusion methods are introduced to integrate feature maps from different layers instead of the last convolution layer only; finally, the fusion of features are delivered to the next layer or classifier as the routine CNN does. The experimental results show that the suggested methods achieve promising performance on public datasets.

show abstract

“…With regard to the derivation of a global database on UST, the complexity of urban built forms, the heterogeneity of materials, and the multiplicity of spectral properties has impeded progress using the available satellite information. UST studies to date have focussed on only individual cities where the data used are not generic enough to be applied on a global basis (Heiden et al, 2012;Voltersen et al, 2015). There are a number of UST typologies that have been developed for specific cities but a common and generic typology is a necessary attribute for universal mapping.…”

Section: Introductionmentioning

confidence: 99%

Towards Consistent Mapping of Urban Structures – Global Human Settlement Layer and Local Climate Zones

Bechtel¹,

Pesaresi²,

See³

et al. 2016

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

View full text Add to dashboard Cite

ABSTRACT:Although more than half of the Earth's population live in urban areas, we know remarkably little about most cities and what we do know is incomplete (lack of coverage) and inconsistent (varying definitions and scale). While there have been considerable advances in the derivation of a global urban mask using satellite information, the complexity of urban structures, the heterogeneity of materials, and the multiplicity of spectral properties have impeded the derivation of universal urban structural types (UST). Further, the variety of UST typologies severely limits the comparability of such studies and although a common and generic description of urban structures is an essential requirement for the universal mapping of urban structures, such a standard scheme is still lacking. More recently, there have been two developments in urban mapping that have the potential for providing a standard approach: the Local Climate Zone (LCZ) scheme (used by the World Urban Database and Access Portal Tools project) and the Global Human Settlement Layer (GHSL) methodology by JRC. In this paper the LCZ scheme and the GHSL LABEL product were compared for selected cities. The comparison between both datasets revealed a good agreement at city and coarse scale, while the contingency at pixel scale was limited due to the mismatch in grid resolution and typology. At a 1 km scale, built-up as well as open and compact classes showed very good agreement in terms of correlation coefficient and mean absolute distance, spatial pattern, and radial distribution as a function of distance from town, which indicates that a decomposition relevant for modelling applications could be derived from both. On the other hand, specific problems were found for both datasets, which are discussed along with their general advantages and disadvantages as a standard for UST classification in urban remote sensing.

show abstract

Expanding an urban structure type mapping approach from a subarea to the entire city of Berlin

Cited by 8 publications

References 12 publications

Attention-Aware Deep Feature Embedding for Remote Sensing Image Scene Classification

Attention-Aware Deep Feature Embedding for Remote Sensing Image Scene Classification

Multi-Layers Feature Fusion of Convolutional Neural Network for Scene Classification of Remote Sensing

Towards Consistent Mapping of Urban Structures – Global Human Settlement Layer and Local Climate Zones

Contact Info

Product

Resources

About