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Multi-class geospatial object detection and geographic image classification based on collection of part detectors
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“or to train independent detectors separately for each orientation [167, 168].”
From Object Detection in 20 Years: A Survey (2023) · cites this paper as [168]
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Cited by
Sorted by citations
Object Detection in 20 Years: A Survey
Zou, Z., Chen, K., Shi, Z., Guo, Y., & Ye, J. (2023) · Proceedings of the IEEE · doi:10.1109/jproc.2023.3238524 · Cited by 3,689
“The most straightforward solution to this problem is to perform data augmentation so that an object in any orientation can be well covered by the augmented data distribution [166], or to train independent detectors separately for each orientation [167, 168].”
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Cheng, G., Han, J., & Lu, X. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2675998 · Cited by 2,926
Self-citationAID: A benchmark data set for performance evaluation of aerial scene classification
Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., & Lu, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2685945 · Cited by 2,372
“Due to the drastically increasing number of aerial images and the highly complex geometrical structures and spatial patterns, to effectively understand the semantic content of them is particularly important, driven by many real-world applications in remote sensing community.”
BackgroundObject detection in optical remote sensing images: A survey and a new benchmark
Li, K., Wan, G., Cheng, G., Meng, L., & Han, J. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.11.023 · Cited by 2,317
“This dataset has been widely used in the earth observation community (Cheng et al., 2014; Cheng et al., 2018b; Farooq et al., 2017; Guo et al., 2018; Han et al., 2017a; Li et al., 2018; Yang et al., 2018b; Yang et al., 2017; Zhong et al., 2018).”
BackgroundMethodSelf-citationCheng, G., Zhou, P., & Han, J. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2601622 · Cited by 1,771
“Specifically, our RICNN with fine-tuning obtained 48.06%, 41.82%, 47.89%, 18.05%, 12.92%, and 10.86% performance gains, in terms of mean AP over all ten object categories, compared with the BoW-based method [12], the SSCBoW-based method [13], the FDDL-based method [16], the COPD-based method [22], the transferredCNN-m…”
BackgroundMethodInfluentialSelf-citationA survey on object detection in optical remote sensing images
Cheng, G., & Han, J. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.03.014 · Cited by 1,542
Self-citationA Survey of Deep Learning-Based Object Detection
Jiao, L., Zhang, F., Liu, F., Yang, S., Li, L., Feng, Z., & Qu, R. (2019) · IEEE Access · doi:10.1109/access.2019.2939201 · Cited by 1,454
Cheng, G., Yang, C., Yao, X., Guo, L., & Han, J. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2783902 · Cited by 1,229
Self-citationHu, F., Xia, G.-S., Hu, J., & Zhang, L. (2015) · Remote Sensing · doi:10.3390/rs71114680 · Cited by 1,222
“[38] developed a rotation-invariant framework based on a collection of part detectors (COPD) that can capture discriminative visual parts of images.”
BackgroundAccurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks
Long, Y., Gong, Y., Xiao, Z., & Liu, Q. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2645610 · Cited by 925
Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges
Ding, J., Xue, N., Xia, G.-S., Bai, X., Yang, W., Yang, M. Y., Belongie, S., Luo, J., Datcu, M., Pelillo, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117983 · Cited by 678
“some public datasets [17], [42], [43], [44], [45] have multiple categories, they have only limited number of samples, which are hardly efficient for training robust deep models.”
BackgroundInfluentialScene Classification With Recurrent Attention of VHR Remote Sensing Images
Wang, Q., Liu, S., Chanussot, J., & Li, X. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2864987 · Cited by 614
“Scale-invariant feature transform (SIFT) [10], [28] and histogram of oriented gradients [29], [30] are local features that usually need mid-level descriptor to generate the entire representation [10], [31].”
BackgroundSun, X., Wang, P., Yan, Z., Xu, F., Wang, R., Diao, W., Chen, J., Li, J., Feng, Y., Xu, T., et al. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.12.004 · Cited by 608
Multi-scale object detection in remote sensing imagery with convolutional neural networks
Deng, Z., Sun, H., Zhou, S., Zhao, J., Lei, L., & Zou, H. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.003 · Cited by 532
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
Zhang, G., Lu, S., & Zhang, W. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2930982 · Cited by 502
“Most works [20]–[27] in earlier days use hand-crafted features, producing very limited detection performance.”
BackgroundInfluentialDetecting tiny objects in aerial images: A normalized Wasserstein distance and a new benchmark
Xu, C., Wang, J., Yang, W., Yu, H., Yu, L., & Xia, G.-S. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.06.002 · Cited by 536
“…al., 2020b), DOTA (Xia et al., 2018), xView (Lam et al., 2018), VisDrone (Zhu et al., 2018), HRSC2016 (Liu et al., 2016b), VEDAI (Razakarivony and Jurie, 2016), NWPU VHR-10 (Cheng et al., 2014), UAVDT (Yu et al., 2019), and FAIR1M (Sun et al., 2021b) are all public aerial object detection datasets.”
Deep Feature Fusion for VHR Remote Sensing Scene Classification
Chaib, S., Liu, H., Gu, Y., & Yao, H. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2700322 · Cited by 462
“based on part detection [9], and land-use classification with compressive sensing multifeature fusion reported in [45] and”
MethodTiny Object Detection in Aerial Images
Wang, J., Yang, W. T., Guo, H., Zhang, R., & Xia, G.-S. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9413340 · Cited by 495
“In the past years, numerous object detection datasets in aerial images, such as NWPU VHR-10 [16], HRSC2016 [17], VEDAI [18], xView [19], DOTA [1], VisDrone [20], UAVDT [21], and DIOR [3] have been proposed to make advancement in object detection research for Earth Vision.”
BackgroundHyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects
Ahmad, M. I., Shabbir, S., Roy, S. K., Hong, D., Wu, X., Yao, J. T., Khan, A., Mazzara, M., Distefano, S., & Chanussot, J. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3133021 · Cited by 478
“Usually they are used to construct bag-of-visualwords (BoVW) models [36], [37], [41], [71], [76]–[81] and HOG feature-based models [35], [82].”
MethodLiu, Z., Wang, H., Weng, L., & Yang, Y. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2565705 · Cited by 442
Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images
Li, K., Cheng, G., Bu, S., & You, X. (2017) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2017.2778300 · Cited by 443
Self-citationChen, K., Liu, C., Chen, H., Zhang, H., Li, W., Zou, Z., & Shi, Z. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3356074 · Cited by 466
Hoeser, T., & Kuenzer, C. (2020) · Remote Sensing · doi:10.3390/rs12101667 · Cited by 404
“NWPU VHR-10 [182] OD multiple classes airborne platforms optical very high DarkNet + YOLO (modified: VaryBlock) [183], ResNet-101 + FPN (modified: Densely connected top-down path) + fully convolutional detector head [184] COWC [185] OD vehicle detection airborne platforms optical very high VGG16 + SSD + correlation al…”
BackgroundZhang, Y., Yuan, Y., Feng, Y., & Lu, X. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2900302 · Cited by 369
“HOG is one of the best features in extracting the edge or local shape information and has been successfully applied to both single-category detection [25], [26] and multicategory detection [9].”
BackgroundMethodInfluentialSemantic Annotation of High-Resolution Satellite Images via Weakly Supervised Learning
Yao, X., Han, J., Cheng, G., Qian, X., & Guo, L. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2523563 · Cited by 346
“For land-use classification experiments, we follow the setting in [2], [3], and [7] to randomly select 80% of the land-use images from each class for training and the remaining 20% of the images for testing.”
MethodSelf-citationNested Network With Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images
Li, C., Cong, R., Hou, J., Zhang, S., Qian, Y., & Kwong, S. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2925070 · Cited by 342
“have many different orientations, scales, and types since the RSIs are taken overhead [13], [14].”
BackgroundRemote Sensing Image Scene Classification Using Bag of Convolutional Features
Cheng, G., Li, Z., Yao, X., Guo, L., & Wei, Z. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2731997 · Cited by 335
Self-citationHou, X., Ao, W., Song, Q., Lai, J., Wang, H., & Xu, F. (2020) · Science China Information Sciences · doi:10.1007/s11432-019-2772-5 · Cited by 328
Cheng, G., Han, J., Guo, L., Liu, Z., Bu, S., & Ren, J. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2393857 · Cited by 306
Self-citationTang, T., Zhou, S., Deng, Z., Zou, H., & Lei, L. (2017) · Sensors · doi:10.3390/s17020336 · Cited by 282
“Seventeen UAV images have a spatial resolution of approximately 2 cm, and 85 CIR images were acquired from an Northwestern Polytechnical University (NWPU) VHR-10 data set [31,32], which are downloaded from the Vaihingen data set [33] with a spatial resolution of approximately 0.”
MethodSmall Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN
Ren, Y., Zhu, C., & Xiao, S. (2018) · Applied Sciences · doi:10.3390/app8050813 · Cited by 281
“Currently only the NWPU VHR-10 dataset [28], which contains totally 800 very-high-resolution (VHR) optical remote sensing images that were cropped from Google Earth and Vaihingen dataset and then manually annotated by experts, is available to the public.”
BackgroundFu, K., Chang, Z., Zhang, Y., Xu, G., Zhang, K., & Sun, X. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.01.025 · Cited by 280
ABNet: Adaptive Balanced Network for Multiscale Object Detection in Remote Sensing Imagery
Liu, Y., Li, Q., Yuan, Y., Du, Q., & Wang, Q. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3133956 · Cited by 263
“2) NWPU VHR-10 [7]: This dataset, which includes ten categories, is proposed by Cheng et al.”
BackgroundMethodInfluentialA Survey of Object Detection for UAVs Based on Deep Learning
Tang, G., Ni, J., Zhao, Y., Gu, Y., & Cao, W. (2023) · Remote Sensing · doi:10.3390/rs16010149 · Cited by 270
“…Shao et al. [87] UAV-head [87] 2021 Pedestrian FR-Transformer [88] UWHD [88] 2022 Agriculture Category imbalance Deng et al. [14] NWPU VHR-10 [89] 2018 Multicategory MLD [90] Leaf [90] 2022 Agriculture Object rotation RoI Transformer [91] DOTA [64] 2019 Multicategory TS 4 Net [92] UAV-ROD…”
BackgroundOn Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
Long, Y., Xia, G.-S., Li, S., Yang, W., Yang, M. Y., Zhu, X. X., Zhang, L., & Li, D. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3070368 · Cited by 250
“, NWPU-VHR10 [83], RSOD [89], HRRSD [103], and DIOR [104], or enclosed polygons, e.”
BackgroundRemote sensing image super-resolution and object detection: Benchmark and state of the art
Wang, Y., Bashir, S. M. A., Khan, M., Ullah, Q., Wang, R., Song, Y., Guo, Z., & Niu, Y. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116793 · Cited by 262
“Some leading datasets include NWPU-RESISC45, NWPU VHR-10 (Cheng et al., 2016), DIOR, DOTA.”
MethodCDLNet: Collaborative Dictionary Learning Network for Remote Sensing Image Scene Classification
Zhao, Y., Liu, J., & Wu, Z. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3336471 · Cited by 244
“A. RSISC 1) Handcrafted Features Methods: The early scene classification methods mainly rely on handcrafted features methods to extract spectral, color, and texture from images as features for classification [21], [22], [23].”
MethodZou, Z., & Shi, Z. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2773199 · Cited by 240
“NWPU-VHR-10 has helped drive recent advances [1], [4], [5], [10] in target detection of remote sensing images.”
BackgroundMethodMa, L., Fu, T., Blaschke, T., Li, M., Dirk Tiede, A., Zhou, Z., Ma, X., & Chen, D. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6020051 · Cited by 247
“Support vector machine, which is a non-parametric supervised statistical learning classifier, has become increasingly popular in remote sensing classification [4,45,46].”
BackgroundA survey of remote sensing image classification based on CNNs
Song, J., Gao, S., Zhu, Y., & Ma, C. (2019) · Big Earth Data · doi:10.1080/20964471.2019.1657720 · Cited by 248
A Unified Metric Learning-Based Framework for Co-Saliency Detection
Han, J., Cheng, G., Li, Z., & Zhang, D. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2706264 · Cited by 231
Self-citationWu, X., Hong, D., Tian, J., Chanussot, J., Li, W., & Tao, R. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2897139 · Cited by 224
“, TAS aerial car detection data set1 and NWPU VHR-10 data set2 [24], [25], have contributed to spurring interest and progress in this area of remote sensing object detection.”
BackgroundMethodEffective Feature Fusion Network in BIFPN for Small Object Detection
Chen, J., Mai, H., Luo, L., Chen, X., & Wu, K. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506347 · Cited by 215
“In the experiment, this paper uses the NWPU VHR-10 dataset [15].”
MethodGong, H., Mu, T., Li, Q., Dai, H., Li, C., He, Z., Wang, W., Han, F., Tuniyazi, A., Li, H., et al. (2022) · Remote Sensing · doi:10.3390/rs14122861 · Cited by 221
“Our proposed SPH-YOLOv5 achieves 0.716 mean Average Precision (mAP) on the DOTA dataset with complex objects and 0.98 on the NWPU-VHR10 dataset with relatively simple objects, the best accuracy among the results of the existing models.”
MethodInfluentialA Feature Aggregation Convolutional Neural Network for Remote Sensing Scene Classification
Lu, X., Sun, H., & Zheng, X. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2917161 · Cited by 213
“The development of the RSSC technology can contribute to target detection [3], change detection [4], [5], and so on [6]–[9].”
BackgroundDeep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey
Li, Z., Wang, Y., Zhang, N., Zhang, Y., Zhao, Z., Xu, D., Ben, G., & Gao, Y. (2022) · Remote Sensing · doi:10.3390/rs14102385 · Cited by 230
Aziz, L., Salam, M. S. B. H., Sheikh, U. U., & Ayub, S. (2020) · IEEE Access · doi:10.1109/access.2020.3021508 · Cited by 224
“Some of the remote-sensing object detection benchmark datasets are VHR-10 [183], HRRSD [172], DOTA [184], DLR 3K Munich [185] and VEDAI [186].”
BackgroundMultiscale Visual Attention Networks for Object Detection in VHR Remote Sensing Images
Wang, C., Bai, X. P., Wang, S., Zhou, J. W., & Ren, P. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2872355 · Cited by 198
“In this section, we evaluate the performance of our model on the DOTA [1] and NWPU VHR-10 [9] data sets.”
BackgroundMethodDeng, Z., Sun, H., Zhou, S., Zhao, J., & Zou, H. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2017.2694890 · Cited by 195
“Our AVPN is inspired by an RPN [21], fully convolutional network [43], [44] and its improved algorithm [45]–[48].”
MethodSun, X., Wang, P., Wang, C., Liu, Y., & Fu, K. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.12.015 · Cited by 191
Vision-Language Models in Remote Sensing: Current progress and future trends
Xiang, L., Wen, C., Hu, Y., Yuan, Z., & Zhu, X. X. (2024) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2024.3383473 · Cited by 252
Vehicle Detection From UAV Imagery With Deep Learning: A Review
Bouguettaya, A., Zarzour, H., Kechida, A., & Taberkit, A. M. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3080276 · Cited by 201
Lv, P., Wu, W., Zhong, Y., Du, F., & Zhang, L. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3157671 · Cited by 201
Few-Shot Object Detection on Remote Sensing Images
Xiang, L., Deng, J., & Fang, Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3051383 · Cited by 186
“NWPU VHR-10 is a very high resolution (VHR) remote sensing image data set released by [68].”
BackgroundWang, Q., Huang, W. F., Xiong, Z., & Li, X. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3042276 · Cited by 192
“These methods rely on a series of manually designed feature descriptors including global feature descriptors (such as color histograms and texture descriptors [22], [23]), and local features descriptors (such as Histogram of Oriented Gradients (HOG) [24], [25] and Scale-Invariant Feature Transform (SIFT) [26], [27]).”
MethodRemote Sensing Object Detection Meets Deep Learning: A metareview of challenges and advances
Zhang, X., Zhang, T., Wang, G., Zhu, P., Tang, X., Jia, X., & Jiao, L. (2023) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2023.3312347 · Cited by 219
“In this section, we mainly introduce 10 widely used datasets with specific characteristics: 1) NWPU VHR-10 [18] : This dataset is a multiclass geo-spatial object detection dataset.”
BackgroundMethodInfluentialHoeser, T., Bachofer, F., & Kuenzer, C. (2020) · Remote Sensing · doi:10.3390/rs12183053 · Cited by 188
“Important datasets for method development in image segmentation are the well-known ISPRS Potsdam and Vaihingen datasets; and for object detection the NWPU VHR-10 and DOTA dataset.”
BackgroundMethodInfluentialCheng, G., Zhou, P., & Han, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.315 · Cited by 175
“The DPM [30] and its variants [20, 22, 23, 34-39] have been the leading methods for object detection tasks for years owing to the carefully crafted features like HOG [32].”
MethodSelf-citationLi, W., Fu, H., Yu, L., Gong, P., Feng, D., Li, C., & Clinton, N. (2016) · International Journal of Remote Sensing · doi:10.1080/01431161.2016.1246775 · Cited by 175
“…Machine (SVM), Arti fi cial Neural Network (ANN), and Random Forest (RF) have been playing an important role in remote-sensing image classi fi cation (Cheng et al. 2015), object detection (Cheng et al. 2014; Cheng and Han 2016; Han et al. 2015), and land-cover mapping (Yu et al. 2014a) for decades.”
BackgroundQi, X., Zhu, P., Wang, Y., Zhang, L., Peng, J., Wu, M., Chen, J., Zhao, X., Zang, N., & Mathiopoulos, P. T. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.09.020 · Cited by 197
Chalavadi, V., Prudviraj, J., Datla, R., Babu, S., & Mohan, C. K. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108548 · Cited by 180
Chintakindi, B. M., Hashmi, M. F., Bokde, N. D., & Geem, Z. W. (2020) · Applied Sciences · doi:10.3390/app10093280 · Cited by 188
“A double multi-scale FPN which consists of a multi-scale RPN and multi-scale object detection network on very high resolution (VHR) remote sensing imagery, especially for small and dense objects [163].”
MethodHan, X., Zhong, Y., & Zhang, L. (2017) · Remote Sensing · doi:10.3390/rs9070666 · Cited by 176
“In order to quantitatively evaluate the performance of the proposed algorithm, the widely utilized evaluation indicators of average precision (AP), Accuracy, Kappa, true negative rate (TNR), negative predictive value (NPV), and precision recall curves (PRCs) are adopted for the object detection framework [12,15,16,28,…”
BackgroundMethodLi, Y., Zhang, Y., Huang, X., & Ma, J. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2839705 · Cited by 169
“[23] in the remote sensing community have proposed a large number of remote sensing image scene data sets, which have effectively promoted the development of remote sensing image scene understanding [24]–[27].”
Yao, X., Feng, X., Han, J., Cheng, G., & Guo, L. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2991407 · Cited by 170
Self-citationGuo, W., Yang, W. T., Zhang, H., & Hua, G. (2018) · Remote Sensing · doi:10.3390/rs10010131 · Cited by 174
“Each part detector is a linear SVM classifier corresponding to a particular viewpoint of a particular object class, hence, the collection of them provides an approximate solution for rotation-invariant object detection [58]; (4) a transferred CNN model fine-tuned from AlexNet, which is used as a universal CNN feature…”
MethodAutomatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks
Gallego, A., Pertusa, A., & Gil, P. (2018) · Remote Sensing · doi:10.3390/rs10040511 · Cited by 182
“To the best of our knowledge, the only public dataset labeled for classification of scenes with ships and with a sufficient number of images is MWPU VHR-10 [49], which is a general purpose collection containing 800 samples classified into 10 classes, and only 302 ships distributed throughout 57 images.”
BackgroundInfluentialRSI-CB: A Large-Scale Remote Sensing Image Classification Benchmark Using Crowdsourced Data
Li, H., Dou, X., Tao, C., Wu, Z., Chen, J., Peng, J., Deng, M., & Zhao, L. D. (2020) · Sensors · doi:10.3390/s20061594 · Cited by 278
Ship detection and classification from optical remote sensing images: A survey
Bo, L. I., Xie, X., Wei, X., & Tang, W. (2020) · Chinese Journal of Aeronautics · doi:10.1016/j.cja.2020.09.022 · Cited by 165
Hu, F., Xia, G.-S., Wang, Z., Huang, X., Zhang, L., & Sun, H. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2444405 · Cited by 165
“In spite of a little better performance than ours, the computation cost of method in [55] is limited by the number of part detectors.”
BackgroundTransformer with Transfer CNN for Remote-Sensing-Image Object Detection
Li, Q., Chen, Y., & Zeng, Y. (2022) · Remote Sensing · doi:10.3390/rs14040984 · Cited by 176
“The proposed TRD, T-TRD and T-TRD-DA are evaluated on the NWPU VHR-10 [6] and DIOR [2] data sets, which are both widely-used public data sets for multi-class object detection in RSIs.”
MethodUsing convolutional features and a sparse autoencoder for land-use scene classification
Othman, E. M. A., Bazi, Y., Alajlan, N., Alhichri, H. S., & Melgani, F. (2016) · International Journal of Remote Sensing · doi:10.1080/01431161.2016.1171928 · Cited by 158
“Cheng et al. (2014) introduced a rotation invariant approach for multi-class geospatial object detection and geographic image classi fi cation based on the collection of part detectors.”
Xu, Y., Du, B., & Zhang, L. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.2999962 · Cited by 157
“The early research for the remote sensing scene classification task mainly focuses on handcrafted features, such as shape, texture, color, spatial and spectral features [11]–[18].”
BackgroundRemote Sensing Scene Classification via Multi-Branch Local Attention Network
Chen, S.-B., Wei, Q.-S., Wang, W.-Z., Tang, J., Luo, B., & Wang, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3127851 · Cited by 154
Attention and Feature Fusion SSD for Remote Sensing Object Detection
Lu, X., Ji, J., Xing, Z., & Miao, Q. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3052575 · Cited by 160
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark
Yin, Q., Hu, Q., Liu, H., Zhang, F., Wang, Y., Lin, Z., An, W., & Guo, Y. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3130436 · Cited by 167
“It is clear that the PASCAL VOC dataset [68] and the NWPU VHR-10 [30] dataset are dominated by middle-size instances.”
Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images
Liu, Y., Li, H., Hu, C., Luo, S., Luo, Y., & Chen, C. W. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3336563 · Cited by 163
“We analyze the peculiarities of these scenes and divide the challenges into five distinct aspects, i.e., scale variation, arbitrary orientation, clustered distribution, low contrast, and cluttered background, as illustrated in Fig.”
BackgroundMethodLi, Y., Zhang, Y., Huang, X., & Yuille, A. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.09.014 · Cited by 144
“Multi-class geospatial object detection from remote sensing images (Cheng et al., 2014) consists of localizing objects of interest (e.”
Dong, R., Xu, D., Zhao, J. K., Jiao, L., & An, J. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2921396 · Cited by 144
“model [19].”
Feature Split–Merge–Enhancement Network for Remote Sensing Object Detection
Ma, W., Li, N., Zhu, H., Jiao, L., Tang, X., Guo, Y., & Hou, B. (2022) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3140856 · Cited by 146
“In this article, we use two datasets to validate our proposed method: one is NWPU VHR-10 [63] and the other is IPIU-M.”
MethodZhong, Y., Han, X., & Zhang, L. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.02.014 · Cited by 143
“…thereby providing us with new opportunities for advancing the automatic interpretation of remote sensing images (Cheriyadat et al., 2014; Cheng et al., 2013, 2014, 2016a, 2016b; Eikvil et al., 2009; Lei et al., 2012; Lin et al., 2016; Liu and Shi, 2014; Liu, 2014, Liu et al., 2011; Siva…”
BackgroundObject Detection and Instance Segmentation in Remote Sensing Imagery Based on Precise Mask R-CNN
Su, H., Wei, S., Yan, M., Wang, C., Shi, J. T., & Zhang, X. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8898573 · Cited by 138
“In this paper, we introduce precise Mask R-CNN for object detection and instance segmentation on VHR remote sensing image datasets (NWPU VHR-10 dataset [1][12]).”
MethodUnsupervised Multilayer Feature Learning for Satellite Image Scene Classification
Li, Y., Tao, C., Tan, Y., Shang, K., & Tian, J. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2503142 · Cited by 137
“In addition, the part object detectors in [11] and [12] depend on the pretrained classifiers.”
BackgroundMethodWu, X., Hong, D., Chanussot, J., Xu, Y., Tao, R., & Wang, Y. (2019) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2019.2919755 · Cited by 137
“To effectively evaluate the performances of the proposed method (FRIFB), we make a comparison with some state-of-the-art algorithms: bag-of-words (BOW)-SVM [13], rotation-aware features [14], exemplar-SVMs [15], the collection of part detectors (COPD) [12], rotation dense feature pyramid networks [10], rotation-invari…”
MethodLi, Y., Huang, Q., Pei, X., Jiao, L., & Shang, R. (2020) · Remote Sensing · doi:10.3390/rs12030389 · Cited by 137
“The NWPUVHR-10 dataset contains two sub-datasets: a positive dataset of 650 images and a negative dataset of 150 images.”
BackgroundInfluentialZhao, L., Tang, P. T. P., & Huo, L. (2016) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.10.035004 · Cited by 135
Tang, X., Zhang, H., Mou, L., Liu, F., Zhang, X., Zhu, X. X., & Jiao, L. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3106381 · Cited by 139
“(VHR)-10 dataset [59], can be chosen to generate the pretrained parameters.”
BackgroundLin, H., Shi, Z., & Zou, Z. (2017) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2017.2727515 · Cited by 133
“scenes also received a lot of attention recently in [7], where the”
BackgroundLiu, N., Wan, L., Zhang, Y., Zhou, T., Huo, H., & Fang, T. (2018) · IEEE Access · doi:10.1109/access.2018.2798799 · Cited by 138
“The classification pipeline of mid-level-based image representation generally consists of three stages [14]: (1) extracting low-level features, such as dense SIFT, HOG, or other types of local descriptors; (2) aggregating these low-level features into a global image representation by feature encoding; and (3) classify…”
BackgroundRS-MetaNet: Deep Metametric Learning for Few-Shot Remote Sensing Scene Classification
Li, H., Cui, Z., Zhu, Z., Chen, L., Zhu, J., Huang, H., & Tao, C. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3027387 · Cited by 130
“The handcrafted features used for remote sensing scene classification can be grouped into three categories: spectral features, texture features, and structural features [6].”
MethodQian, X., Lin, S., Cheng, G., Yao, X., Ren, H., & Wang, W. (2020) · Remote Sensing · doi:10.3390/rs12010143 · Cited by 129
“To validate the effectiveness of our method, various experiments on the NWPU VHR-10 [42,45,46] and DIOR benchmark dataset were conducted with a total of 3775 instances and 192,472 instances available, respectively.”
MethodSelf-citationCoF-Net: A Progressive Coarse-to-Fine Framework for Object Detection in Remote-Sensing Imagery
Zhang, C., Lam, K., & Wang, Q. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2022.3233881 · Cited by 129
“[18], 75% of the images are randomly selected as the training set, and the rest are used for testing.”
BackgroundMethodInfluentialWang, X., Wang, S., Ning, C., & Zhou, H. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2020.3044655 · Cited by 123
“tic labels based on the global characteristics of scenes as well as the local features of objects [2], [18].”
BackgroundGeneralized few-shot object detection in remote sensing images
Zhang, T., Zhang, X., Zhu, P., Jia, X., Tang, X., & Jiao, L. (2022) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.004 · Cited by 123
Global to Local: A Scale-Aware Network for Remote Sensing Object Detection
Gao, T., Niu, Q., Zhang, J., Chen, T., Mei, S., & Jubair, A. (2023) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2023.3294241 · Cited by 127
“It is proposed by Cheng et al. [56] of Northwestern Poly-technic University, Xi’an, China.”
BackgroundTayara, H., & Chong, K. T. (2018) · Sensors · doi:10.3390/s18103341 · Cited by 117
“The results of our proposed model using Resnet 101 backbone, BoW, SSCBoW, COPD, a transferred CNN model, RICNN, SSD, faster R-CNN, rotation-insensitive CNN, multiscale CNN, and deformable CNN have been plotted.”
MethodAircraft Target Detection in Remote Sensing Images Based on Improved YOLOv5
Luo, S. N., Yu, J., Xi, Y., & Liao, X. (2022) · IEEE Access · doi:10.1109/access.2022.3140876 · Cited by 119
“The remote sensing images studied in this paper are from Google Earth, with 78 images in total [27]–[29].”
MethodDeformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery
Xu, Z., Xu, X., Wang, L., Yang, R. Q., & Pu, F. (2017) · Remote Sensing · doi:10.3390/rs9121312 · Cited by 118
“In previous papers [5,16], COPD, FDDL, SSCBoW, BoW, and AlexNet are displayed for comparison.”
MethodA Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images
Di, Y., Jiang, Z., & Zhang, H. (2021) · Remote Sensing · doi:10.3390/rs13040747 · Cited by 116
“Considering the accuracy of categories and the precise annotations, we first choose original remote sensing images containing ship instances from popular public remote sensing datasets including DOTA, HRSC2016 and NWPU VHR-10.”
InfluentialCheng, G., Sun, X., Li, K., Guo, L., & Han, J. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3081421 · Cited by 122
Self-citation
Metadata
- Authors
- Cheng, G., Han, J., Zhou, P., & Guo, L.
- Affiliations
- Northwestern Polytechnical University (CN)
- Corresponding author
- Han, Junwei
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2014-12
- Volume
- 98
- Pages
- 119-132
- ISSN
- 0924-2716 (ISSN-L)
- 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
- 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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