A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery

Xu, Zhiwu; Guan, Kaiyu; Casler, Nathan; Peng, Bin; Wang, Shaowen

doi:10.1016/j.isprsjprs.2018.08.005

Cited by 63 publications

(29 citation statements)

References 31 publications

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“…whether a neighborhood can be characterized as compact or as open built-up area) and also indicate industrial zones where usually the highest land surface temperature appears (Huang and Wang, 2019). Additionally, using the simplified land cover classification results as basis, a complete LCZ classification can be achieved by adding multi-sensor and multi-temporal information, such as that provided by LiDAR and satellite images acquired by other sensors (Xu et al, 2018). This way, comprehensive studies regarding urban heat islands and energy resilience can benefit from the mapping results.…”

Section: Discussionmentioning

confidence: 99%

Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network

Qiu

Mou

Schmitt

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

135

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The local climate zone (LCZ) scheme was originally proposed to provide an interdisciplinary taxonomy for urban heat island (UHI) studies. In recent years, the scheme has also become a starting point for the development of higher-level products, as the LCZ classes can help provide a generalized understanding of urban structures and land uses. LCZ mapping can therefore theoretically aid in fostering a better understanding of spatio-temporal dynamics of cities on a global scale. However, reliable LCZ maps are not yet available globally. As a first step toward automatic LCZ mapping, this work focuses on LCZ-derived land cover classification, using multi-seasonal Sentinel-2 images. We propose a recurrent residual network (Re-ResNet) architecture that is capable of learning a joint spectral-spatial-temporal feature representation within a unitized framework. To this end, a residual convolutional neural network (ResNet) and a recurrent neural network (RNN) are combined into one end-to-end architecture. The ResNet is able to learn rich spectral-spatial feature representations from single-seasonal imagery, while the RNN can effectively analyze temporal dependencies of multi-seasonal imagery. Cross validations were carried out on a diverse dataset covering seven distinct European cities, and a quantitative analysis of the experimental results revealed that the combined use of the multi-temporal information and Re-ResNet results in an improvement of approximately 7 percent points in overall accuracy. The proposed framework has the potential to produce consistent-quality urban land cover and LCZ maps on a large scale, to support scientific progress in fields such as urban geography and urban climatology.

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Section: Discussionmentioning

confidence: 99%

Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network

Qiu

Mou

Schmitt

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

135

View full text Add to dashboard Cite

show abstract

“…CNNs have produced outstanding results in these areas. However, most of the research focuses on how to improve the model accuracy and design good CNN structures [46]- [48], and insufficient attention has been placed on the adversarial example problem of RSI scene classification systems. Since the adversarial example was proposed by Szegedy et al [40], the security of deep learning has been the subject of widespread discussion.…”

Section: Related Workmentioning

confidence: 99%

Attack Selectivity of Adversarial Examples in Remote Sensing Image Scene Classification

Chen

Guang

et al. 2020

IEEE Access

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Remote sensing image (RSI) scene classification is the foundation and important technology of ground object detection, land use management and geographic analysis. During recent years, convolutional neural networks (CNNs) have achieved significant success and are widely applied in RSI scene classification. However, crafted images that serve as adversarial examples can potentially fool CNNs with high confidence and are hard for human eyes to interpret. For the increasing security and robust requirements of RSI scene classification, the adversarial example problem poses a serious problem for the classification results derived from systems using CNN models, which has not been fully recognized by previous research. In this study, to explore the properties of adversarial examples of RSI scene classification, we create different scenarios by testing two major attack algorithms (i.e., the fast gradient sign method (FGSM) and basic iterative method (BIM)) trained on different RSI benchmark datasets to fool CNNs (i.e., InceptionV1, ResNet and a simple CNN). In the experiment, our results show that CNNs of RSI scene classification are also vulnerable to adversarial examples, and some of them have a fooling rate of over 80%. These adversarial examples are affected by the architecture of CNNs and the type of RSI dataset. InceptionV1 has a fooling rate of less than 5%, which is lower than the others. Adversarial examples generated on the UCM dataset are easier than other datasets. Importantly, we also find that the classes of adversarial examples have an attack selectivity property. Misclassifications of adversarial examples of RSIs are related to the similarity of the original classes in the CNN feature space. Attack selectivity reveals potential classes of adversarial examples and provides insights into the design of defensive algorithms in future research.

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“…The application of convolutional neural network methods in land cover mapping is progressing very rapidly [44,45]. Xu et al [46] developed a three-dimensional convolutional neural network method for land cover classification using intensity information from LiDAR (Light Detection And Ranging) and multi-temporal Landsat images. The method is believed to be able to capture a wide range of complex features of various land cover types; however, it requires a large training sample library, and Xu did not present an automated construction scheme for such training datasets.…”

Section: Comparison With Other Cnn Modelsmentioning

confidence: 99%

A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China

Zhang

et al. 2018

Remote Sensing

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Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively.

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A 3D convolutional neural network method for land cover classification using LiDAR and multi-temporal Landsat imagery

Cited by 63 publications

References 31 publications

Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network

Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network

Attack Selectivity of Adversarial Examples in Remote Sensing Image Scene Classification

A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China

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