Zhuo Zheng

All Publications

• Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection INTERNATIONAL JOURNAL OF COMPUTER VISION Zheng, Z., Zhong, Y., Ma, A., Zhang, L. 2024

  View details for DOI 10.1007/s11263-024-02141-4

  View details for Web of Science ID 001250359600001

• FarSeg++: Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery. IEEE transactions on pattern analysis and machine intelligence Zheng, Z., Zhong, Y., Wang, J., Ma, A., Zhang, L. 2023; PP

  Abstract

  Geospatial object segmentation, a fundamental Earth vision task, always suffers from scale variation, the larger intra-class variance of background, and foreground-background imbalance in high spatial resolution (HSR) remote sensing imagery. Generic semantic segmentation methods mainly focus on the scale variation in natural scenarios. However, the other two problems are insufficiently considered in large area Earth observation scenarios. In this paper, we propose a foreground-aware relation network (FarSeg++) from the perspectives of relation-based, optimization-based, and objectness-based foreground modeling, alleviating the above two problems. From the perspective of the relations, the foreground-scene relation module improves the discrimination of the foreground features via the foreground-correlated contexts associated with the object-scene relation. From the perspective of optimization, foreground-aware optimization is proposed to focus on foreground examples and hard examples of the background during training to achieve a balanced optimization. Besides, from the perspective of objectness, a foreground-aware decoder is proposed to improve the objectness representation, alleviating the objectness prediction problem that is the main bottleneck revealed by an empirical upper bound analysis. We also introduce a new large-scale high-resolution urban vehicle segmentation dataset to verify the effectiveness of the proposed method and push the development of objectness prediction further forward. The experimental results suggest that FarSeg++ is superior to the state-of-the-art generic semantic segmentation methods and can achieve a better trade-off between speed and accuracy. The code and model are available at: https://github.com/Z-Zheng/FarSeg.

  View details for DOI 10.1109/TPAMI.2023.3296757

  View details for PubMedID 37467086

• Scalable Multi-Temporal Remote Sensing Change Data Generation via Simulating Stochastic Change Process Zheng, Z., Tian, S., Ma, A., Zhang, L., Zhong, Y., IEEE IEEE COMPUTER SOC. 2023: 21761-21770

  View details for DOI 10.1109/ICCV51070.2023.01994

  View details for Web of Science ID 001169500506036

• ChangeMask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Zheng, Z., Zhong, Y., Tian, S., Ma, A., Zhang, L. 2022; 183: 228-239

  View details for DOI 10.1016/j.isprsjprs.2021.10.015

  View details for Web of Science ID 000782582900002

• Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters REMOTE SENSING OF ENVIRONMENT Zheng, Z., Zhong, Y., Wang, J., Ma, A., Zhang, L. 2021; 265

  View details for DOI 10.1016/j.rse.2021.112636

  View details for Web of Science ID 000697024400003

• Deep multisensor learning for missing-modality all-weather mapping ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Zheng, Z., Ma, A., Zhang, L., Zhong, Y. 2021; 174: 254-264

  View details for DOI 10.1016/j.isprsjprs.2020.12.009

  View details for Web of Science ID 000640987800017

• Change is Everywhere: Single-Temporal Supervised Object Change Detection in Remote Sensing Imagery Zheng, Z., Ma, A., Zhang, L., Zhong, Y., IEEE IEEE. 2021: 15173-15182

  View details for DOI 10.1109/ICCV48922.2021.01491

  View details for Web of Science ID 000798743205035

• FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Zheng, Z., Zhong, Y., Ma, A., Zhang, L. 2020; 58 (8): 5612-5626

  View details for DOI 10.1109/TGRS.2020.2967821

  View details for Web of Science ID 000552371900031

• HyNet: Hyper-scale object detection network framework for multiple spatial resolution remote sensing imagery ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Zheng, Z., Zhong, Y., Ma, A., Han, X., Zhao, J., Liu, Y., Zhang, L. 2020; 166: 1-14

  View details for DOI 10.1016/j.isprsjprs.2020.04.019

  View details for Web of Science ID 000551268300001

• Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery Zheng, Z., Zhong, Y., Wang, J., Ma, A., IEEE IEEE. 2020: 4095-4104

  View details for DOI 10.1109/CVPR42600.2020.00415

  View details for Web of Science ID 000620679504037

• Global road extraction using a pseudo-label guided framework: from benchmark dataset to cross-region semi-supervised learning GEO-SPATIAL INFORMATION SCIENCE Lu, X., Zhong, Y., Zheng, Z., Wang, J., Chen, D., Su, Y. 2024

  View details for DOI 10.1080/10095020.2024.2362760

  View details for Web of Science ID 001251618600001

• EarthVQANet: Multi-task visual question answering for remote sensing image understanding ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Wang, J., Ma, A., Chen, Z., Zheng, Z., Wan, Y., Zhang, L., Zhong, Y. 2024; 212: 422-439

  View details for DOI 10.1016/j.isprsjprs.2024.05.001

  View details for Web of Science ID 001243433300001

• LoveNAS: Towards multi-scene land-cover mapping via hierarchical searching adaptive network ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Wang, J., Zhong, Y., Ma, A., Zheng, Z., Wan, Y., Zhang, L. 2024; 209: 265-278

  View details for DOI 10.1016/j.isprsjprs.2024.01.011

  View details for Web of Science ID 001200052300001

• Temporal-agnostic change region proposal for semantic change detection ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Tian, S., Tan, X., Ma, A., Zheng, Z., Zhang, L., Zhong, Y. 2023; 204: 306-320

  View details for DOI 10.1016/j.isprsjprs.2023.06.017

  View details for Web of Science ID 001086073500001

• Explicable Fine-Grained Aircraft Recognition Via Deep Part Parsing Prior Framework for High-Resolution Remote Sensing Imagery. IEEE transactions on cybernetics Chen, D., Zhong, Y., Ma, A., Zheng, Z., Zhang, L. 2023; PP

  Abstract

  Aircraft recognition is crucial in both civil and military fields, and high-spatial resolution remote sensing has emerged as a practical approach. However, existing data-driven methods fail to locate discriminative regions for effective feature extraction due to limited training data, leading to poor recognition performance. To address this issue, we propose a knowledge-driven deep learning method called the explicable aircraft recognition framework based on a part parsing prior (APPEAR). APPEAR explicitly models the aircraft's rigid structure as a pixel-level part parsing prior, dividing it into five parts: 1) the nose; 2) left wing; 3) right wing; 4) fuselage; and 5) tail. This fine-grained prior provides reliable part locations to delineate aircraft architecture and imposes spatial constraints among the parts, effectively reducing the search space for model optimization and identifying subtle interclass differences. A knowledge-driven aircraft part attention (KAPA) module uses this prior to achieving a geometric-invariant representation for identifying discriminative features. Part features are generated by part indexing in a specific order and sequentially embedded into a compact space to obtain a fixed-length representation for each part, invariant to aircraft orientation and scale. The part attention module then takes the embedded part features, adaptively reweights their importance to identify discriminative parts, and aggregates them for recognition. The proposed APPEAR framework is evaluated on two aircraft recognition datasets and achieves superior performance. Moreover, experiments with few-shot learning methods demonstrate the robustness of our framework in different tasks. Ablation analysis illustrates that the fuselage and wings of the aircraft are the most effective parts for recognition.

  View details for DOI 10.1109/TCYB.2023.3293033

  View details for PubMedID 37552595

• Large-scale agricultural greenhouse extraction for remote sensing imagery based on layout attention network: A case study of China ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Chen, D., Ma, A., Zheng, Z., Zhong, Y. 2023; 200: 73-88

  View details for DOI 10.1016/j.isprsjprs.2023.04.020

  View details for Web of Science ID 001007013800001

• Large-scale deep learning based binary and semantic change detection in ultra high resolution remote sensing imagery: From benchmark datasets to urban application ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Tian, S., Zhong, Y., Zheng, Z., Ma, A., Tan, X., Zhang, L. 2022; 193: 164-186

  View details for DOI 10.1016/j.isprsjprs.2022.08.012

  View details for Web of Science ID 000876743000004

• Cross-sensor domain adaptation for high spatial resolution urban land-cover mapping: From airborne to spaceborne imagery REMOTE SENSING OF ENVIRONMENT Wang, J., Ma, A., Zhong, Y., Zheng, Z., Zhang, L. 2022; 277

  View details for DOI 10.1016/j.rse.2022.113058

  View details for Web of Science ID 000804945700001

• GRE AND BEYOND: A GLOBAL ROAD EXTRACTION DATASET Lu, X., Zhong, Y., Zheng, Z., Chen, D., IEEE IEEE. 2022: 3035-3038

  View details for DOI 10.1109/IGARSS46834.2022.9883915

  View details for Web of Science ID 000920916603057

• Cascaded Multi-Task Road Extraction Network for Road Surface, Centerline, and Edge Extraction IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Lu, X., Zhong, Y., Zheng, Z., Chen, D., Su, Y., Ma, A., Zhang, L. 2022; 60

  View details for DOI 10.1109/TGRS.2022.3165817

  View details for Web of Science ID 000794217400005

• A Supervised Progressive Growing Generative Adversarial Network for Remote Sensing Image Scene Classification IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Ma, A., Yu, N., Zheng, Z., Zhong, Y., Zhang, L. 2022; 60

  View details for DOI 10.1109/TGRS.2022.3151405

  View details for Web of Science ID 000773300900029

• National-scale greenhouse mapping for high spatial resolution remote sensing imagery using a dense object dual-task deep learning framework: A case study of China ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Ma, A., Chen, D., Zhong, Y., Zheng, Z., Zhang, L. 2021; 181: 279-294

  View details for DOI 10.1016/j.isprsjprs.2021.08.024

  View details for Web of Science ID 000704454800009

• Cross-domain road detection based on global-local adversarial learning framework from very high resolution satellite imagery ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Lu, X., Zhong, Y., Zheng, Z., Wang, J. 2021; 180: 296-312

  View details for DOI 10.1016/j.isprsjprs.2021.08.018

  View details for Web of Science ID 000697167200020

• FactSeg: Foreground Activation-Driven Small Object Semantic Segmentation in Large-Scale Remote Sensing Imagery IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Ma, A., Wang, J., Zhong, Y., Zheng, Z. 2022; 60

  View details for DOI 10.1109/TGRS.2021.3097148

  View details for Web of Science ID 000732756800001

• Urban road mapping based on an end-to-end road vectorization mapping network framework ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Chen, D., Zhong, Y., Zheng, Z., Ma, A., Lu, X. 2021; 178: 345-365

  View details for DOI 10.1016/j.isprsjprs.2021.05.016

  View details for Web of Science ID 000686383900001

• A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification IEEE TRANSACTIONS ON CYBERNETICS Zhu, Q., Deng, W., Zheng, Z., Zhong, Y., Guan, Q., Lin, W., Zhang, L., Li, D. 2022; 52 (11): 11709-11723

  Abstract

  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 patch-free global learning (FPGA) architecture was proposed for HSI classification according to global spatial context information. However, FPGA has difficulty in extracting the most discriminative features when the sample data are imbalanced. In this article, a spectral-spatial-dependent global learning (SSDGL) framework based on the global convolutional long short-term memory (GCL) and global joint attention mechanism (GJAM) is proposed for insufficient and imbalanced HSI classification. In SSDGL, the hierarchically balanced (H-B) sampling strategy and the weighted softmax loss are proposed to address the imbalanced sample problem. To effectively distinguish similar spectral characteristics of land cover types, the GCL module is introduced to extract the long short-term dependency of spectral features. To learn the most discriminative feature representations, the GJAM module is proposed to extract attention areas. The experimental results obtained with three public HSI datasets show that the SSDGL has powerful performance in insufficient and imbalanced sample problems and is superior to other state-of-the-art methods.

  View details for DOI 10.1109/TCYB.2021.3070577

  View details for Web of Science ID 000732244900001

  View details for PubMedID 34033562

• GAMSNet: Globally aware road detection network with multi-scale residual learning ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING Lu, X., Zhong, Y., Zheng, Z., Zhang, L. 2021; 175: 340-352

  View details for DOI 10.1016/j.isprsjprs.2021.03.008

  View details for Web of Science ID 000644695700024

• RSNet: The Search for Remote Sensing Deep Neural Networks in Recognition Tasks IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Wang, J., Zhong, Y., Zheng, Z., Ma, A., Zhang, L. 2021; 59 (3): 2520-2534

  View details for DOI 10.1109/TGRS.2020.3001401

  View details for Web of Science ID 000622319000049

• COLOR: Cycling, Offline Learning, and Online Representation Framework for Airport and Airplane Detection Using GF-2 Satellite Images IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Zhong, Y., Zheng, Z., Ma, A., Lu, X., Zhang, L. 2020; 58 (12): 8438-8449

  View details for DOI 10.1109/TGRS.2020.2987907

  View details for Web of Science ID 000594389800015

• Edge-Reinforced Convolutional Neural Network for Road Detection in Very-High-Resolution Remote Sensing Imagery PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING Lu, X., Zhong, Y., Zheng, Z., Zhao, J., Zhang, L. 2020; 86 (3): 153-160

  View details for DOI 10.14358/PERS.86.3.153

  View details for Web of Science ID 000516713700003

• A NOVEL GLOBAL-AWARE DEEP NETWORK FOR ROAD DETECTION OF VERY HIGH RESOLUTION REMOTE SENSING IMAGERY Lu, X., Zhong, Y., Zheng, Z., IEEE IEEE. 2020: 2579-2582

  View details for DOI 10.1109/IGARSS39084.2020.9323155

  View details for Web of Science ID 000664335302151

• Multi-Scale and Multi-Task Deep Learning Framework for Automatic Road Extraction IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING Lu, X., Zhong, Y., Zheng, Z., Liu, Y., Zhao, J., Ma, A., Yang, J. 2019; 57 (11): 9362-9377

  View details for DOI 10.1109/TGRS.2019.2926397

  View details for Web of Science ID 000496155200076

• S3NET: TOWARDS REAL-TIME HYPERSPECTRAL IMAGERY CLASSIFICATION Zheng, Z., Zhong, Y., IEEE IEEE. 2019: 3293-3296

  View details for Web of Science ID 000519270603077

• POP-NET: ENCODER-DUAL DECODER FOR SEMANTIC SEGMENTATION AND SINGLE-VIEW HEIGHT ESTIMATION Zheng, Z., Zhong, Y., Wang, J., IEEE IEEE. 2019: 4963-4966

  View details for Web of Science ID 000519270604212

• Deep Salient Feature Based Anti-Noise Transfer Network for Scene Classification of Remote Sensing Imagery REMOTE SENSING Gong, X., Xie, Z., Liu, Y., Shi, X., Zheng, Z. 2018; 10 (3)

  View details for DOI 10.3390/rs10030410

  View details for Web of Science ID 000428280100056

• COLOR: CYCLING OFFLINE LEARNING AND ONLINE REPRESENTING FOR REMOTE SENSING DATAFLOW Zheng, Z., Zhong, Y., IEEE IEEE. 2018: 4093-4096

  View details for Web of Science ID 000451039804016

• Multi-channel Pose-aware Convolution Neural Networks for Multi-view Facial Expression Recognition Liu, Y., Zeng, J., Shan, S., Zheng, Z., IEEE IEEE. 2018: 458-465

  View details for DOI 10.1109/FG.2018.00074

  View details for Web of Science ID 000454996700064