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  • Zhuo Zheng
Zhuo Zheng

Zhuo Zheng
Wuhan University | WHU · State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing

PhD Candidate
Remote Sensing Visual Perception

About

37
Publications
14,831
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409
Citations

Publications

Publications (37)
Article
Full-text available
Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning-based HSI classifiers follow a patch-based learning framework by dividing the image into overlapping patches. As such, these methods are local learning methods, which have a high computational cost. In this article,...
Conference Paper
Full-text available
Geospatial object segmentation, as a particular semantic segmentation task, always faces with larger-scale variation, larger intra-class variance of background, and foreground-background imbalance in the high spatial resolution (HSR) remote sensing imagery. However, general semantic segmentation methods mainly focus on scale variation in the natura...
Article
Full-text available
Multisensor Earth observation has significantly accelerated the development of multisensor collaborative remote sensing applications such as all-weather mapping using synthetic aperture radar (SAR) images and optical images. However, in the real-world application scenarios, not all data sources may be available, namely, the missing-modality problem...
Article
Sudden-onset natural and man-made disasters represent a threat to the safety of human life and property. Rapid and accurate building damage assessment using bitemporal high spatial resolution (HSR) remote sensing images can quickly and safely provide us with spatial distribution information and statistics of the damage degree to assist with humanit...
Article
Full-text available
Urban land-cover information is essential for resource allocation and sustainable urban development. Recently, deep learning algorithms have shown promising results in land-cover mapping with high spatial resolution (HSR) imagery. However, the limitation of the annotation and the divergence of the multi-sensor images always challenge the transferab...
Article
Full-text available
Multi-temporal high spatial resolution earth observation makes it possible to detect complex urban land surface changes, which is a significant and challenging task in remote sensing communities. Previous works mainly focus on binary change detection (BCD) based on modern technologies, e.g., deep fully convolutional network (FCN), whereas the deep...
Article
Remote sensing image scene classification is a challenging task. With the development of deep learning, methods based on convolutional neural networks (CNNs) have made great achievements in remote sensing image scene classification. Since the training of a CNN requires a large number of labeled samples, a generative adversarial network (GAN) for sa...
Article
Full-text available
Greenhouses have revolutionized farming all over the world. To estimate vegetable yields, greenhouse mapping using high spatial resolution (HSR) remote sensing imagery is very important. Although automatic greenhouse mapping methods have been proposed, they are often applied in limited small-scale areas (i.e. a parcel, a city, or a province). Large...
Conference Paper
Full-text available
Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping. However, urban and rural scenes can show completely different geographical landscapes, and the inadequate generalizability of these algorithms hinders city-level or national-level mapping. Most of the existing HSR land-cover dat...
Preprint
Full-text available
Deep learning approaches have shown promising results in remote sensing high spatial resolution (HSR) land-cover mapping. However, urban and rural scenes can show completely different geographical landscapes, and the inadequate generalizability of these algorithms hinders city-level or national-level mapping. Most of the existing HSR land-cover dat...
Article
Full-text available
Road detection based on convolutional neural networks (CNNs) has achieved remarkable performances for very high resolution (VHR) remote sensing images. However, this approach relies on massive annotated samples, and the problem of limited generalization for unseen images still remains. The manual pixel-level labeling process is also extremely time-...
Preprint
Full-text available
For high spatial resolution (HSR) remote sensing images, bitemporal supervised learning always dominates change detection using many pairwise labeled bitemporal images. However, it is very expensive and time-consuming to pairwise label large-scale bitemporal HSR remote sensing images. In this paper, we propose single-temporal supervised learning (S...
Article
Full-text available
Reliable urban road vector maps are essential for urban analysis because the spatial distribution of road networks reflects urban development under the combined effects of nature and socio-economics. Diverse very high resolution (VHR) remote sensing images are now available, enabling explicit extraction of urban road vector maps over wide areas. Ur...
Article
Full-text available
The small object semantic segmentation task is aimed at automatically extracting key objects from high-resolution remote sensing (HRS) imagery. Compared with the large-scale coverage areas for remote sensing imagery, the key objects such as cars, ships, etc. in HRS imagery often contain only a few pixels. In this paper, to tackle this problem, the...
Preprint
Full-text available
Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large parameter space and requires a large number of labeled data. Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast pa...
Article
Full-text available
Deep learning techniques have been widely applied to hyperspectral image (HSI) classification and have achieved great success. However, the deep neural network model has a large parameter space and requires a large number of labeled data. Deep learning methods for HSI classification usually follow a patchwise learning framework. Recently, a fast pa...
Article
Full-text available
Road detection from very high-resolution (VHR) remote sensing imagery is of great importance in a broad array of applications. However, the most advanced deep learning based methods often produce fragmented road segments, due to the complex backgrounds of the images, such as the occlusions and shadows caused by trees and buildings, or the surroundi...
Preprint
Full-text available
Geospatial object segmentation, as a particular semantic segmentation task, always faces with larger-scale variation, larger intra-class variance of background, and foreground-background imbalance in the high spatial resolution (HSR) remote sensing imagery. However, general semantic segmentation methods mainly focus on scale variation in the natura...
Preprint
Full-text available
Deep learning techniques have provided significant improvements in hyperspectral image (HSI) classification. The current deep learning based HSI classifiers follow a patch-based learning framework by dividing the image into overlapping patches. As such, these methods are local learning methods, which have a high computational cost. In this paper, a...
Preprint
Full-text available
With the acceleration of the urban expansion, urban change detection (UCD), as a significant and effective approach, can provide the change information with respect to geospatial objects for dynamical urban analysis. However, existing datasets suffer from three bottlenecks: (1) lack of high spatial resolution images; (2) lack of semantic annotation...
Article
Full-text available
Faced with the problem of the large scale variation, geospatial object detection in multiple spatial resolution (MSR) remote sensing imagery is a challenging task. To avoid the scale problem, the current convolutional neural network (CNN) based object detectors use multi-scale structures in the convolutional layer level to improve the detection per...
Article
Full-text available
Deep learning algorithms, especially convolutional neural networks (CNNs), have recently emerged as a dominant paradigm for high spatial resolution remote sensing (HRS) image recognition. A large amount of CNNs have already been successfully applied to various HRS recognition tasks, such as land-cover classification, scene classification, etc. Howe...
Article
Full-text available
Monitoring airports using remote sensing imagery require us to first detect the airports and then perform airplane detection. Detecting airports and airplanes with large-scale remote sensing imagery are significant and challenging tasks in the field of remote sensing. Although many detection algorithms have been developed for detecting airports and...
Article
Road detection in very-high-resolution remote sensing imagery is a hot research topic. However, the high resolution results in highly complex data distributions, which lead to much noise for road detection—for example, shadows and occlusions caused by disturbance on the roadside make it difficult to accurately recognize road. In this article, a nov...
Article
Full-text available
Road detection and centerline extraction from very high-resolution (VHR) remote sensing imagery are of great significance in various practical applications. Road detection and centerline extraction operations depend on each other, to a certain extent. The road detection constrains the appearance of the centerline, and the centerline enhances the li...
Article
Full-text available
Remote sensing (RS) scene classification is important for RS imagery semantic interpretation. Although tremendous strides have been made in RS scene classification, one of the remaining open challenges is recognizing RS scenes in low quality variance (e.g., various scales and noises). This paper proposes a deep salient feature based anti-noise tran...

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Projects (2)
Project
Build a family of ChangeX
Project
Towards a general land-cover classification framework