Cited by 1,007Remote-Sensing Image ClassificationAdvanced Image and Video Retrieval TechniquesAdvanced Neural Network Applications

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Multi-class geospatial object detection and geographic image classification based on collection of part detectors

Cheng, Gong, Han, Junwei, Zhou, Peicheng, Guo, Lei·2014·ISPRS Journal of Photogrammetry and Remote Sensing

DOI 10.1016/j.isprsjprs.2014.10.002

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Histograms of Oriented Gradients for Human Detection
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Object Detection with Discriminatively Trained Part-Based Models
Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.167 · Cited by 9,853
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CCMANet: A Cross-Layer Cascade Network With Multiattention Mechanisms for Remote Sensing Object Detection
Mei, J., Lyu, W., Guo, Q., Xu, Y., & Deng, Z. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3663387 · Cited by 1
“2) NWPU VHR-10 Dataset [44]: The NWPU VHR-10 dataset, a high-resolution remote sensing dataset released by Northwestern Polytechnical University in 2014, is now widely used.”
A Spatio-Temporal Hierarchical Diffusion Framework for Training-Free Perceptual Remote Sensing Image Compression
Cheng, Y., Meng, F., Qi, H., Shen, H., Zhang, Z., Wang, Y., & Liang, Y. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3675642 · Cited by 1
“1) Datasets: We evaluate our method on a variety of RS imagery datasets, including RSICD [32], UCMerced [33], Real-RSSRD [34], AID [35], VRSBench [36], DOTA [37], [38], fMoW [39], DIOR [40], and NWPU-VHR-10 [41], [42].”
LMEFA-YOLO: Remote sensing image object detection network based on lightweight feature enhancement and efficient fusion
Zhai, Z., Li, Y., Xu, C., & Su, S. (2026) · Digital Signal Processing · doi:10.1016/j.dsp.2026.106180 · Cited by 1
RFE-YOLO: A Lightweight Receptive Field-Enhanced Network for UAV Imagery Object Detection
Peng, Y., & Ge, X. (2026) · Sensors · doi:10.3390/s26092903 · Cited by 1
“…dynamic micro-scale entities such as pedestrians, bicycles, and motorcycles (which frequently appear as “tiny” objects in VisDrone), as well as structurally diverse objects including vehicles, ships, airplanes, and bridges (which exhibit extreme scale variations in UAVDT [39] and NWPU VHR-10 [40]).”
HSD-DETR: An Efficient Hybrid Scale Dynamic Network for Small Object Detection in Remote Sensing Images
Xu, J., Liu, W., Tian, R., Wang, C., & Zhang, Y. (2026) · Remote Sensing · doi:10.3390/rs18101577 · Cited by 1
A multi-scale cross-level channel enhancement network for remote sensing object detection
Fan, Y., Zhang, M., Yu, D., Tang, Q., Yu, L., & Liu, X. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae6e93 · Cited by 1
“To validate the effectiveness of the experiment, we conducted a series of experiments on four publicly available datasets: DIOR [6], TGRS-HRRSD [42], RSOD [43], and NWPU-VHR-10 [44].”
Recent advances of YOLO-based object detection in remote sensing imagery: A comprehensive review
Liu, S., Shao, F., & Xue, J. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115171 · Cited by 1
Full-Scale Feature Pyramid Information Propagation for Postearthquake Landslide Detection
Li, H., Xu, K., Liu, X., Wang, J., Zhou, G., Zhang, M., Wang, S., & Sun, M. (2026) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2026.3699793 · Cited by 1
“E ARTHQUAKE-INDUCED landslides represent one of the most destructive secondary hazards, posing serious threats to human life, infrastructure, and ecological systems [1], [2], [3], [4], [5], [6].”
YOLO26 for Object Detection in Very High-Resolution Remote Sensing Imagery: A Systematic Evaluation on NWPU VHR-10
Cao, Q., Xia, Y., & Li, Z. (2026) · International Conference on Intelligent Computing and Signal Processing (ICSP) · doi:10.1109/icsp69961.2026.11540762 · Cited by 1
“The NWPU VHR-10 dataset [3], released by Northwestern Polytechnical University, is a widely used benchmark for remote sensing object detection.”
SOE-YOLO: towards efficient and accurate small object detection in optical remote sensing imagery
Peng, Y., & Ge, X. (2026) · Frontiers in Remote Sensing · doi:10.3389/frsen.2026.1869435 · Cited by 1
Collaborative Optimization of Receptive Field and Texture Preservation for Remote Sensing Small Object Detection
Zhou, S., Qi, Q., Wu, J., He, W. P., Chen, S., & Tu, B. (2026) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2026.3715676 · Cited by 1
“The spatial resolution of the images varies from 0.5 to 30 m. 3) NWPU VHR-10 [26]: The NWPU VHR-10 dataset is a multiscale optical remote sensing dataset, where image sizes range from 680 × 380 to 1700 × 1000 pixels.”
Influential
LDDF-YOLO: improved YOLOv8 lightweight detail dynamic fusion algorithm for remote sensing object detection
Bo, S., Wei, Z., Lu, Y., Hao, X., Wang, Y., & Zhou, P. (2026) · Journal of Real-Time Image Processing · doi:10.1007/s11554-026-01946-5 · Cited by 1
Self-citation
IC-EO: Interpretable Code-Based Assistant for Earth Observation
Lahouel, L., Lopata, L., Gruening, S., Meoni, G., Petit, G., & Lobry, S. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31397-3_14 · Cited by 1
CGWT-DETR: Context-Guided Wavelet Transform DETR for Small Object Detection in Aerial RGB and Thermal Infrared Imagery
Hussain, S., Mumtaz, I., Ahmad, U., Li, L., Jia, Z., Lv, M., Zhao, X., Ma, H., & Wang, C. (2026) · Remote Sensing · doi:10.3390/rs18162715 · Cited by 1
Mining Mid-Level Visual Elements for Object Detection in High-Resolution Remote Sensing Images
Liu, X., Yan, H., Huo, H., & Fang, T. (2018) · IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS) · doi:10.1109/prrs.2018.8486179 · Cited by 1
“Such image patches could correspond to object parts [1], regions [2], or whole objects [3].”
Background
Deformable Part Models for Complex Object Detection in Remote Sensing Imagery
Pool, N., & Vatsavai, R. R. (2018) · International Workshop on Analytics for Big Geospatial Data · doi:10.1145/3282834.3282843 · Cited by 1
“This representation is a slight variation of the HOG feature pyramids mentioned in [8] and [4]; instead of convolving HOG filters with the images, this representation is learned by a CNN.”
BackgroundMethod
Vehicle detection using improved morphological reconstruction for QuickBird images
Liu, D., Han, L., & Li, Z. (2018) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.12.045010 · Cited by 1
“EQ-TARGET;temp:intralink-;e008;116;445 rb 1⁄4 ρlbðJbmÞ: (8)”
Mining discriminative spatial cues for aerial image quality assessment towards big data
Zhang, X., Gu, N., Chang, J., Ye, H., Lin, C., & Shen, J. (2019) · Signal Processing Image Communication · doi:10.1016/j.image.2019.115646 · Cited by 1
An End-to-End Scalable Object Detection Network for Remote Sensing Images
Duan, Y., Teng, Z., Zhang, B., & Jianping Fan, B. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9324571 · Cited by 1
“(3)We execute extensive experiments on NWPU VHR-10[9] and DOTA[10] datasets, which demonstrates the per-(cid:1)(cid:2)(cid”
Method
Using of Clay as Heterogeneous Catalysts for Organic Syntheses; A Review
Almadani, E. A., Haron, F., & Ibrahim, D. M. (2021) · Sumerianz Journal of Scientific Research · doi:10.47752/sjsr.42.32.36 · Cited by 1
Small target detection based on image enhancement in remote sensing images
Zhai, W., Chen, J., & Yang, M. (2022) · International Conference on Sensors and Information Technology (ICSI) · doi:10.1117/12.2637667 · Cited by 1
“At this time, many small targets have been enhanced and are easier to be detected; the fourth step is based on the NWPU VHR-10 data set The above verifies the effectiveness of the method in this paper.”
BackgroundInfluential
MPGI‐Terminal defect detection based on M‐FRCNN
Liu, Q., Wang, M., Wang, H., & HANAJIMA, N. (2022) · IET Image Processing · doi:10.1049/ipr2.12643 · Cited by 1
Improved YOLOv5 network method for remote sensing image objects detection
Xu, C., Yin, H., Song, Z., Ye, Z., & Yang, W. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10055580 · Cited by 1
Enhanced multihead self-attention block network for remote sensing image scene classification
Li, Y., Wang, J., Chen, S.-B., Tang, J., & Luo, B. (2023) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.17.016517 · Cited by 1
“The practical application of RSISC has prompted a significant amount of study during the last decade, such as urban planning,(1,2) land-cover,(3,4) vegetation mapping,(5,6) natural disaster detection,(7,8) and geospatial target classification.(9) However, due to the complex background information of remote sensing ima…”
Background
MultiScale Object Detection in Remote Sensing Images using Deep Learning
Radhika, C., P.Nikhila, D. S., Shivani, D. S., SiriPriya, B., & Reddy, B. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.54643 · Cited by 1
Dense Contrastive Learning Based Object Detection for Remote Sensing Images
Liu, S., Zou, H., Li, M., Cao, X., He, S., Wei, J., Sun, L. G., & Zhang, Y. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10282252 · Cited by 1
Pixel Attention Feature Pyramid Network for Few-Shot Object Detection in Remote Sensing Images
Li, Z., Yang, F., Wang, Y., Jiang, Z., Wang, K., & Huo, X. (2023) · Asia Conference on Artificial Intelligence, Machine Learning and Robotics (AIMLR) · doi:10.1145/3625343.3625362 · Cited by 1
Distortion Magnitude Control: A Dynamic Augmentation Optimization Approach for Few-Shot Learning in Remote Sensing Scene Classification
Dong, Z. H., Lin, B., & Xie, F. (2023) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2023.3343251 · Cited by 1
“Early methods for RS scene classification primarily relied on manually designed features [4], [5], [6], offering simplicity and efficiency.”
Method
A multi-scale dataset with comprehensive analysis for building detection from remote sensing images
Zhang, H., Bi, M., Xu, T., & Zang, J. (2023) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei60920.2023.10373371 · Cited by 1
Multi-scale Target Detection Algorithm of Optical Remote Sensing Image Based on Improved YOLOv8
Wang, H., Yang, H., Wang, J., Zhou, X., Xu, Y., & Zhang, H. (2024) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlviii-1-2024-649-2024 · Cited by 1
DiffuPrompter: Pixel-Level Automatic Annotation for High-Resolution Remote Sensing Images with Foundation Models
Li, H., Wei, Y. J., Peng, H., & Zhang, W. (2024) · Remote Sensing · doi:10.3390/rs16112004 · Cited by 1
Research on remote sensing small object detection algorithm based on improved YOLOv8
Wang, S. X., Zhu, D., zhang, W., & xie, J. (2024) · International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP) · doi:10.1117/12.3029814 · Cited by 1
“The confidence loss function of the YOLOv8 algorithm is represented by equation (1), where c on f L denotes the confidence loss function and p represents the confidence of the sample[4].”
Method
Small target detection from DIKW model perspective
Wang, Y., Peng, L., Yu, R., Hu, J., Yan, S., & Zhang, B. (2024) · International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP) · doi:10.1117/12.3029865 · Cited by 1
“In addition to the widely used MSCOCO dataset [10], common data sets are as follows: • NWPUVHR-10 satellite image dataset [11, 12, 13].”
Method
SIGKD: A Structured Instance Graph Distillation Method for Efficient Object Detection in Remote Sensing Images
Liu, F., Zhao, W., Qi, H., & Zhou, G. (2024) · Remote Sensing · doi:10.3390/rs16234443 · Cited by 1
Feature Weighting and Fusion for Few-shot Object Detection in Remote Sensing Images
Deng, L., Song, Y., Xie, H., Ye, Y., Yuan, Z., & Xiao, J. (2024) · IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI) · doi:10.1109/dtpi61353.2024.10778833 · Cited by 1
RandommaskFormer: Light Weight Remote Sensing Scene Classification with Masked Transformer
Hu, X., Wu, W., & Zhu Li, B. (2024) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3696409.3700166 · Cited by 1
“Using feature descriptors like color histogram [5], texture descriptors [6], scale-invariant feature transform [7], and histogram of oriented gradients [8], low-level features of RS images were employed for scene classification.”
Few-Shot Object Detection in Remote Sensing: Mitigating Label Inconsistencies and Navigating Category Variations
Si, T., & Kong, S. (2025) · IEEE Access · doi:10.1109/access.2025.3527881 · Cited by 3
Remote sensing few-shot object detection using adaptive feature modification and Gaussian dynamic dilated convolution
Zhang, Z., Wang, J., Chen, P., & He, C. (2025) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.3057865 · Cited by 1
“V2 [4] datasets, the effect of this method on few-shot object detection in remote sensing is evaluated, and finally good results are achieved.”
CH-YOLO-Lite: a lightweight object detection model with context-aware progressive aggregation and hierarchical feature aggregation for aerial imagery
Wang, L., Jiang, W., Dharejo, F. A., Sun, M., Timofte, R., & Mao, G. (2025) · Journal of Electronic Imaging · doi:10.1117/1.jei.34.2.023026 · Cited by 1
Ground Target Recognition Technology of UAV Based on YOLOv10
Wang, H., Zhu, L., Ma, T., Zhong, C., & Dai, R. (2025) · International Conference on Computer Vision, Image and Deep Learning (CVIDL) · doi:10.1109/cvidl65390.2025.11085523 · Cited by 1
“ Multi - level feature enhancement transmits semantic info top - down and boosts details bottom - up, addressing the information loss issue in traditional FPN for small - target detection[12].”
Scene classification of remote sensing images based on hierarchical sparse coding
Xu, J., Lv, Q., Liu, H., & He, J. (2018) · The Journal of Engineering · doi:10.1049/joe.2018.8268 · Cited by 1
Utilization of Deep Convolutional Neural Networks for Remote Sensing Scenes Classification
Luo, C., Huang, H., Wang, Y., & Wang, S. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.81982 · Cited by 1
Object Classification of Remote Sensing Images Based on Partial Randomness Supervised Discrete Hashing
Kang, T., Liu, Y., & Sun, Q. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545365 · Cited by 1
“However, description ability of linear PCA features is limited [22], and it is difficult to obtain more abstract representations.”
Background
S3OD: Single Stage Small Object Detector from Scratch for Remote Sensing Images
Yang, F., Li, W., Li, W., & Wang, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34113-8_29 · Cited by 1
Remote Sensing Image Dam Detection Based on Dual Threshold Network
Caigang, Z., Lei, Z., & Suidong, L. (2020) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc49329.2020.9164639 · Cited by 1
“Including the above methods, other conventional methods[12-14] mostly follow a similar processing flow, that is, first extracting the features of the sliding window and thereby training the classifier, and detecting the target on the sliding window during the inference process.”
Method
Multi-scale Dense Object Detection in Remote Sensing Imagery Based on Keypoints
Guo, Q., Liu, Y., Yin, H., Li, Y., & Li, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60633-6_9 · Cited by 1
Research on UAV Image Ship Recognition Based on Fine-grained Classification Data Set
Su, C., Pan, J. H., Jiang, L., Sun, Y., Yu, W., & Cao, Y. (2021) · IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC) · doi:10.1109/icftic54370.2021.9647199 · Cited by 1
Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils
Jagan, J., Gundlapalli, P., & Samui, P. (2018) · Intelligent Systems · doi:10.4018/978-1-5225-5643-5.ch066 · Cited by 1
Unsupervised Representation High-Resolution Remote Sensing Image Scene Classification via Contrastive Learning Convolutional Neural Network
Li, F., Li, J., Han, W., Feng, R., & Wang, L. (2021) · Photogrammetric Engineering & Remote Sensing · doi:10.14358/pers.87.8.577 · Cited by 1
Detection of Solar Panel Farms Using Machine Learning
Srivastava, V. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-0098-3_18 · Cited by 1
Remote Sensing Image Classification Using Transfer Learning Based Convolutional Neural Networks: An Experimental OverviewPreprint
Baral, S., Aryal, J., & Sharma, N. (2023) · doi:10.36227/techrxiv.22581457.v2 · Cited by 1
A Dense Residual Correlation Attention Multi-scale Network for Remote Sensing Scene ClassificationPreprint
Dai, W., Shi, F., Wang, X., Xu, H., Yuan, L., & Wen, X. (2024) · Research Square · doi:10.21203/rs.3.rs-3823437/v1 · Cited by 1
Detection of Green Roofs through Satellite Imagery Using Faster R-CNN
Marcos, L. (2024) · doi:10.32920/25266730.v1 · Cited by 1
Open-Vocabulary Object Detection for Remote Sensing ImagesPreprint
Li, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5393893 · Cited by 1
Transfer multiple sources knowledge for surface target detection
Dan, Z., Huang, H., & Chen, Y. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2205221
“INTRODUCTION Surface target detection in remote sensing images is very important for a wide range of applications such as environment monitoring([1]), image analysis and classification([2-4]), change detection([5]),and geographic image retrieval([6]).”
Background
Utilizing Shape-Based Feature and Discriminative Learning for Building Detection
Zhang, S., Shen, H., & Huo, C. (2017) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2016edl8138
“Traditional method [2] only utilizes a simple classifier (i.”
Method
Scene classification of remote sensing image based on deep convolutional neural network
Yang, Z., Mu, X.-D., & Zhao, F. (2018) · International Conference on Digital Image Processing · doi:10.1117/12.2502942
Partial randomness hashing applied to remote sensing object classification
Kang, T., Liu, Y., & Sun, Q. (2018) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.12.035020
Improving object recognition in aerial image and ambulatory assessment analysis by deep learning
Sun, P. (2019) · doi:10.32469/10355/72231
Research on Low Altitude Object Detection Based on Deep Convolution Neural Network
Qi, Y., Gu, J., Tian, Z., Dengchao, F., & Su, Y. (2020) · Studies in distributed intelligence · doi:10.1007/978-3-030-45099-1_13
Remote Sensing Image Detection Based on FasterRCNN
Liu, S., Ma, Z.-M., & Chen, B. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-8599-9_44
A Multi-stage Network for Improving the Sample Quality in Aerial Image Object Detection
Han, W., Feng, R., Wang, L., Li, F., & Wu, L. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9324014
“NWPU VHR-10 [9] dataset is an available 10-class geospatial object detection dataset used for research purposes.”
BackgroundMethod
Object Detection of Remote Sensing Image Based on Multi-level Domain Adaption
Kang, P., Ma, X., & Wang, H. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-15-8411-4_116
Concentrations of Ground-Level Ozone on Petroleum Filling Stations in Urban West Region- Zanzibar
Mohamed, S. S., Mohamed, A. A. J., & Okafor, C. J. (2021) · Sumerianz Journal of Scientific Research · doi:10.47752/sjsr.42.37.44
Deep Learning in Satellite Imaging: A Survey
Goni, I., Ahmadu, A. S., & Malgwi, Y. M. (2021) · Sumerianz Journal of Scientific Research · doi:10.47752/sjsr.42.45.51
A Small Target Detection Research Based on Dynamic Convolution Neural NetworkPreprint
Bai, C., Wu, K., Wang, D., Li, H., Yan, M., & Huang, T. (2021) · Research Square · doi:10.21203/rs.3.rs-916747/v1
“[3] Cheng G, Han J W, Zhou P C, et al.”
Background
Inspection of Electric Power Distribution Systems with SCRDet
Restani, G. D. M., Coelho, L. D. S., & Filho, J. F. B. (2021) · Congresso Brasileiro de Inteligência Computacional (CBIC) · doi:10.21528/cbic2021-90
One-Stage Deep Channels Attention Network for Remote Sensing Images Object Detection
Tang, J., Zhang, W., Zhang, G., Liang, R., & Lu, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25198-6_36
Object detection with deep neural networks under constrained scenarios
Zhang, G. (2022) · DR-NTU (Nanyang Technological University) · doi:10.32657/10356/164687
Multi-layered PCM Method for Detecting Occluded Object in Secluded Remote Sensing Image
Rao, B. N. K., Kamarsu, A., Returi, K. D., Mohan, V., & Madhavi, K. R. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-8563-8_63
Remote Sensing Image Classification Using Transfer Learning Based Convolutional Neural Networks: An Experimental OverviewPreprint
Baral, S., & Sharma, N. (2023) · doi:10.36227/techrxiv.22581457
Super pixel segmentation and classification of SAR images for brightness enhancement
Harathi, & Reddy, B. M. K. (2021) · International Journal of Computing Programming and Database Management · doi:10.33545/27076636.2021.v2.i2a.31
Automatic Feature Extraction from Satellite Imagery for Remote Sensing
Mary, D. D., Nair, N. S., Mohana, Revathy, S., Gladence, L. M., & Bernatin, T. (2023) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei56765.2023.10125969
A Semi-supervised Classification Method for 6G Remote Sensing Images Based on Pseudo-label and False Representation Recognition
Meng, X., Xi, L., & Liu, L. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-36014-5_2
Research on Ship Detection Method Based on Band Screening
Su, C. (2023) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis58954.2023.10213198
“CONSTRUCTION OF SINGLE BAND DATASETS Currently, most scholars use publicly available datasets such as NWPU VHR-10 [7], DOTA [8], DIOR [9], ImageNet [10], HRSC2016 [11] for ship recognition in optical remote sensing images.”
Method
Detector Consistency Research on Remote Sensing Object Detection
Zhang, Y., & Jin, H. (2023) · Remote Sensing · doi:10.3390/rs15174130
A Method for Detecting Lightweight Optical Remote Sensing Images Using Improved Yolov5n
Zou, C., Jeon, W., Rhee, S., & Cai, M. (2023) · Journal of Multimedia Information System · doi:10.33851/jmis.2023.10.3.215
Features Matter: Cross-Domain Mutual Centralized Learning for Few Shot Remote Sensing Object Detection
Zhang, K., Tan, Z., & Lv, Q. (2023) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss52108.2023.10282953
“The NWPU VHR-10 dataset[17] offers a collection of remote sensing image data with extremely high resolution.”
Background
Mechanisms for carbon stock driving and scenario modeling in typical mountainous watersheds of northeastern ChinaPreprint
Jin, Z., zhang, W., Zhang, X., Li, Y., Feng, M., Qi, P., Huang, Y., Li, R. S., Jiang, M., Zou, Y., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3714501/v1
ShipDC: a new baseline for optical remote sensing ship detection and classification
Li, L., & Zhao, Z. (2024) · International Conference on Geology, Mapping and Remote Sensing (ICGMRS) · doi:10.1117/12.3019841
“Gong et al's NWPU VHR-10 dataset[16], which contains 800 satellite remote sensing image slices, contains 3651 objects in 10 classes.”
Background
Leveled Approach of Context Setting in Semantic Understanding of Remote Sensing Images
Ahuja, S., & Patil, S. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-8289-9_46
Proposition of Remote Sensing Image Object Detection Algorithm Based on EYOLOv3i
Bai, X., Wu, K., & Bai, C. (2023) · International Conference on Data Science and Information Technology (DSIT) · doi:10.1109/dsit60026.2023.00044
Multiwavelet-based Distilled Transformer Network for Remote Sensing Image Super Resolution
Sandana, J. S., Thool, K. V., & Deivalakshmi, S. (2023) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit59427.2023.10430568
“The three standard remote sensing image datasets: the AID test Dataset [17], the NWPU VHR-10 dataset [18], and the UC Merced dataset [19], are used for testing.”
Method
Detection of Green Roofs through Satellite Imagery Using Faster R-CNN
Marcos, L. (2024) · doi:10.32920/25266730
Remote Sensing Target Detection Inspired by Scene Information and Inter-Object Relations
Ding, Y., Lyu, X., Yan, L., Rong, L., & Cheng, K. (2023) · Computing and Informatics · doi:10.31577/cai_2023_6_1404
“NWPU VHR-10 [42] and HRRSD [43] datasets are used to verify the performance of remote sensing image target detection based on target relationship and scene information.”
Method
Multi-scale Semantic Information Refinement and Hybrid Dual Attention Module based Object Detection in Remote Sensing Images
Shi, J., & Wang, J. (2024) · doi:10.1145/3653781.3653818
TGeoYOLO: Leveraging Multi-Scale Features and Enhanced Loss for Remote Sensing Detection
Du, W. F., Ke, M., Jin, K., Tan, S., He, D., & Li, K. (2024) · IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom) · doi:10.1109/edgecom62867.2024.00027
“We conducted experiments on two mainstream remote sensing image detection datasets: NWPU VHR-10 [32]–[34] and VisDrone [35].”
Method
S 3 AHI: Source-Free Domain Adaptive Small Object Detection with Slicing Aided Hyper Inference
Ding, H., Yang, G., Tu, X., Huang, Y., & Ding, X. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651365 · Cited by 1
“Specifically, we use five benchmarks that are HRRSD [35], DOTA [36], xView [37], NWPU [38] and DIOR [39] to form three domain adaptive experiments; 1) HRRSD → xView-ship, 2) DOTA → xView, and 3) DIOR → NWPU.”
Method
SP-Y OLOX: A lightweight ship target detection algorithm based on remote sensing images
Li, G., Wang, X., Zhang, D., Wu, Z., & Xiao, Y. (2024) · Chinese Control Conference (CCC) · doi:10.23919/ccc63176.2024.10662256
“The NWPU VHR-10 datasets[22], a benchmark dataset for remote sensing target detection from Northwestern Polytechnical University (NWPU), and Dior[23] were selected to test and evaluate the feasibility of the proposed algorithmic model.”
Method
Two-Stage Tripletnet: Light Weight Remote Sensing Scene Classification
Hu, X., Wu, W., Zhu Li, B., Luo, X., & Chen, Z. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10647781
LRSDet: lightweight remote sensing target detection network with local-global information awareness and rapid sample assignment
Fan, F., Zhang, M., Liu, Y., Yu, D., & Li, J. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad82ff
“These three datasets include the DIOR [19], NWPU VHR-10 [20] and RSOD datasets [21].”
A Target Region Based Non-Uniform Remote Sensing Image Process Method Using Data Hiding
Wei, Z. Z., Hui, Z., Li, M., & Yang, G. (2024) · International Conference on Electronic Engineering and Informatics (EEI) · doi:10.1109/eei63073.2024.10696757
“…as usual. a should be greater than max u R in case of max compression ratio max R , therefore the definition of condition 1 is shown as below: Where The total amount NWPU-VHR10 [14] is 800, in which 321 images fulfill condition 0 (over 40% input images) and can be processed by either algorithm.”
Method
Transformed sparsity-boosted low-rank model for image inpainting with non-convex γ -norm regularization and non-local prior
Han, R., Liao, S., Fu, S., & Wang, X. (2024) · Optics & Laser Technology · doi:10.1016/j.optlastec.2024.111865
GUI: A Comprehensive Dataset of Global Urban Infrastructure Based on Geospatial Visual Foundation Models
Han, Z., Zhang, X., Xi, Y., Luo, Y., Xia, T., & Li, Y. (2024) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3678717.3691242
“Besides, the currently available satellite dataset usually concentrates on a few cities [3, 11], which are insufficient in capturing the global diversity of urban infrastructure.”
FDSS-YOLO: A More Accurate Remote Sensing Object Detection Method
Wang, F., & Zheng, H. (2024) · International Conference on Internet of Things and Cloud Computing Technology (IoTCCT) · doi:10.1145/3702879.3702933
Weakly Supervised Instance Segmentation in Aerial Images via Comprehensive Spatial Adaptation
Xu, J., Luo, P., & Mu, D. (2024) · Remote Sensing · doi:10.3390/rs16244757
Lightweight Remote Sensing Object Detection Model Based on Spiking Neural Networks
Zhang, X., Xie, C., Chen, Z., & Liu, D. (2024) · International Conference on Pattern Recognition and Artificial Intelligence (PRAI) · doi:10.1109/prai62207.2024.10827660
“The experiments of QS-YOLO are conducted on two datasets: NWPU VHR-10 [2], [34], [54] and SSDD [35], [36].”
Sahd_Yolonet: A Novel Optimized Model for Robust Target Detection in Complex Remote Sensing ImagesPreprint
Liu, Y., Wang, S., Shi, F., & Wei, Y. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5125876
MultiF‐RSNet: Distributed interpretation for multi‐platform collaborative in remote sensing
Zhang, L., Lu, X., He, Y., Sun, Z., Duan, S., & Zhao, L. (2025) · Electronics Letters · doi:10.1049/ell2.70156
Few-Shot Fine-Grained Ship Detection
Lan, J., Wang, Z., Liu, M., & Qin, B. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-1809-5_20

Authors: Cheng, G., Han, J., Zhou, P., & Guo, L.
Affiliations: Northwestern Polytechnical University (CN)
Corresponding author: Han, Junwei
ORCID: Cheng, Gong
Han, Junwei
Zhou, Peicheng
Guo, Lei
Venue: ISPRS Journal of Photogrammetry and Remote Sensing
Publisher: Elsevier BV
Published: 2014-12
Volume: 98
Pages: 119-132
ISSN: 0924-2716 (ISSN-L)
DOI: 10.1016/j.isprsjprs.2014.10.002
OpenAlex: W2005368619
Cited by: 1,007
References: 38
Citations by year: 2026: 115 · 2025: 152 · 2024: 141 · 2023: 111 · 2022: 105 · 2021: 105 · 2020: 87 · 2019: 64 · 2018: 41 · 2017: 44 · 2016: 26 · 2015: 16
FWCI: 43.77
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: ISPRS Journal of Photogrammetry and Remote Sensing (publishedVersion)
Funders: Ministry of Education of the People's Republic of China (20136102110037)
National Natural Science Foundation of China (61401357, 61333017, 61473231, 91120005)
China Postdoctoral Science Foundation (2014M552491)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords: geospatial object detection, linear support vector machine, spatial pattern detection, remote sensing images, image representation

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Multi-class geospatial object detection and geographic image classification based on collection of part detectors

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