7.1.7.3 Remote Sensing Object Detection Applications

Chapter Contents (Back)
Object Detction. Remote Sensing.
See also ATR Applications, Automatic Target Recognition.

DOTA: A Large-Scale Benchmark and Challenges for Object Detection in Aerial Images,
Online2021
WWW Link. Dataset, Aerial Objects. 2106
2806 aerial images obtained from different sensors and platforms, including 15 classification categories (vehicle, track, storange tanks, sports fields, etc.) BibRef

TGRS-HRRSD-Dataset: High Resolution Remote Sensing Detection (HRRSD),
Online2017
WWW Link. Dataset, Aerial Objects. 2106
21,761 images. in 13 categories.
See also Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection. BibRef

Benteftifa, M.H.[M. Hafed], Kurz, L.[Ludwik],
Two-Dimensional Object Detection in Correlated Noise,
PR(24), No. 8, 1991, pp. 755-773.
Elsevier DOI BibRef 9100

Diab, S.L., Karim, M.A., Iftekharuddin, K.M.,
Multiobject Detection of Targets with Fine Details, Scale and Translation Variations,
OptEng(37), No. 3, March 1998, pp. 876-883. 9804
BibRef

Strickland, R.N., Zoucha, D.M.,
Object Detection Using Subband Decomposition,
OptEng(37), No. 1, January 1998, pp. 320-330. 9802
BibRef

Sahin, A., Miller, E.L.,
Object detection using high resolution near-field array processing,
GeoRS(39), No. 1, January 2001, pp. 136-141.
IEEE Top Reference. 0104
BibRef

Miller, E.L., Karl, W.C.,
Detection and localization of obscured objects from near-field sensor array data: physical models and statistical processing,
ICIP98(III: 370-374).
IEEE DOI 9810
BibRef

Kerekes, J.P., Baum, J.E.,
Spectral imaging system analytical model for subpixel object detection,
GeoRS(40), No. 5, May 2002, pp. 1088-1101.
IEEE Top Reference. 0206
BibRef

Kerekes, J.P., Baum, J.E.,
Full-Spectrum Spectral Imaging System Analytical Model,
GeoRS(43), No. 3, March 2005, pp. 571-580.
IEEE Abstract. 0501
BibRef

Stefanou, M.S., Kerekes, J.P.,
A Method for Assessing Spectral Image Utility,
GeoRS(47), No. 6, June 2009, pp. 1698-1706.
IEEE DOI 0905
BibRef

Stefanou, M.S., Kerekes, J.P.,
Image-Derived Prediction of Spectral Image Utility for Target Detection Applications,
GeoRS(48), No. 4, April 2010, pp. 1827-1833.
IEEE DOI 1003
BibRef

Kerekes, J.P.[John P.],
Hyperspectral remote sensing subpixel object detection performance,
AIPR11(1-4).
IEEE DOI 1204
BibRef

Cheng, G.[Gong], Han, J.W.[Jun-Wei], Guo, L.[Lei], Qian, X.L.[Xiao-Liang], Zhou, P.C.[Pei-Cheng], Yao, X.W.[Xi-Wen], Hu, X.T.[Xin-Tao],
Object detection in remote sensing imagery using a discriminatively trained mixture model,
PandRS(85), No. 1, 2013, pp. 32-43.
Elsevier DOI 1310
Object detection BibRef

Cheng, G.[Gong], Han, J.W.[Jun-Wei], Zhou, P.C.[Pei-Cheng], Guo, L.[Lei],
Multi-class geospatial object detection and geographic image classification based on collection of part detectors,
PandRS(98), No. 1, 2014, pp. 119-132.
Elsevier DOI 1411
Geospatial object detection. Find specific objects or spatial pattern. For high resolution remote sensing applications. BibRef

Cheng, G.[Gong], Han, J.W.[Jun-Wei], Guo, L.[Lei], Liu, T.M.[Tian-Ming],
Learning coarse-to-fine sparselets for efficient object detection and scene classification,
CVPR15(1173-1181)
IEEE DOI 1510
BibRef

Han, J.W.[Jun-Wei], Zhou, P.C.[Pei-Cheng], Zhang, D.W.[Ding-Wen], Cheng, G.[Gong], Guo, L.[Lei], Liu, Z.B.[Zhen-Bao], Bu, S.[Shuhui], Wu, J.[Jun],
Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding,
PandRS(89), No. 1, 2014, pp. 37-48.
Elsevier DOI 1403
Geospatial target detection BibRef

Qiu, H.Q.[He-Qian], Li, H.L.[Hong-Liang], Wu, Q.B.[Qing-Bo], Meng, F.[Fanman], Ngan, K.N.[King Ngi], Shi, H.C.[Heng-Can],
A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images,
RS(11), No. 13, 2019, pp. xx-yy.
DOI Link 1907
BibRef

Sheng, Y.L.[Yun-Long], Sahli, S.[Samir], Ouyang, Y.[Yueh], Lavigne, D.[Daniel],
Object detection: From optical correlator to intelligent recognition surveillance system,
SPIE(Newsroom), October 21, 2013.
DOI Link 1310
Survey, Object Detection. Component- and feature-based approaches enable object detection in aerial imagery. BibRef

Li, G.S.[Gang-Sheng], Zeng, L.P.[Li-Ping], Zhang, L.[Ling], Wu, Q.M.J.[Q.M. Jonathan],
State Identification of Duffing Oscillator Based on Extreme Learning Machine,
SPLetters(25), No. 1, January 2018, pp. 25-29.
IEEE DOI 1801
Signals with very low signal-to-noise ratio. chaos, feature extraction, learning (artificial intelligence), object detection, oscillators, signal classification, state identification BibRef

Pang, J.M.[Jiang-Miao], Li, C.[Cong], Shi, J.P.[Jian-Ping], Xu, Z.H.[Zhi-Hai], Feng, H.J.[Hua-Jun],
RR^2-CNN: Fast Tiny Object Detection in Large-Scale Remote Sensing Images,
GeoRS(57), No. 8, August 2019, pp. 5512-5524.
IEEE DOI 1908
Remote-sensing Region-based CNN. convolutional neural nets, feature extraction, geophysical image processing, image classification, remote sensing region-based convolutional neural network (Rē-CNN) BibRef

Zhang, Y.L.[Yuan-Lin], Yuan, Y.[Yuan], Feng, Y.C.[Ya-Chuang], Lu, X.Q.[Xiao-Qiang],
Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection,
GeoRS(57), No. 8, August 2019, pp. 5535-5548.
IEEE DOI 1908
convolutional neural nets, feature extraction, learning (artificial intelligence), object detection, rotation and scaling robust enhancement (RSRE)
See also TGRS-HRRSD-Dataset: High Resolution Remote Sensing Detection (HRRSD). BibRef

Li, L.[Lin], Zhang, S.B.[Sheng-Bing], Wu, J.[Juan],
Efficient Object Detection Framework and Hardware Architecture for Remote Sensing Images,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Zhang, G.J.[Gong-Jie], Lu, S.J.[Shi-Jian], Zhang, W.[Wei],
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery,
GeoRS(57), No. 12, December 2019, pp. 10015-10024.
IEEE DOI 1912
Remote sensing, Object detection, Optical sensors, Optical imaging, Feature extraction, Detectors, Visualization, optical remote sensing images BibRef

Geng, X.R.[Xiu-Rui], Ji, L.Y.[Lu-Yan], Yang, W.T.[Wei-Tun],
The Analytical Solution of the Clever Eye (CE) Method,
GeoRS(59), No. 1, January 2021, pp. 478-487.
IEEE DOI 2012
Object detection, Optimization, Mathematical model, Detectors, Covariance matrices, Minimization, Correlation, Clever eye (CE), target detection BibRef

Sun, X.[Xian], Liu, Y.F.[Ying-Fei], Yan, Z.Y.[Zhi-Yuan], Wang, P.J.[Pei-Jin], Diao, W.H.[Wen-Hui], Fu, K.[Kun],
SRAF-Net: Shape Robust Anchor-Free Network for Garbage Dumps in Remote Sensing Imagery,
GeoRS(59), No. 7, July 2021, pp. 6154-6168.
IEEE DOI 2106
Convolution, Shape, Feature extraction, Remote sensing, Object detection, Detectors, Sun, Anchor free, remote sensing imagery BibRef

Wang, Y.[Yu], Jia, Y.[Yannan], Gu, L.[Lize],
EFM-Net: Feature Extraction and Filtration with Mask Improvement Network for Object Detection in Remote Sensing Images,
RS(13), No. 20, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Chen, Y.[Ying], Liu, Q.[Qi], Wang, T.[Teng], Wang, B.[Bin], Meng, X.L.[Xiao-Liang],
Rotation-Invariant and Relation-Aware Cross-Domain Adaptation Object Detection Network for Optical Remote Sensing Images,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Han, W.[Wei], Fan, R.[Runyu], Wang, L.[Lizhe], Feng, R.[Ruyi], Li, F.[Fengpeng], Deng, Z.[Ze], Chen, X.[Xiaodao],
Improving Training Instance Quality in Aerial Image Object Detection With a Sampling-Balance-Based Multistage Network,
GeoRS(59), No. 12, December 2021, pp. 10575-10589.
IEEE DOI 2112
Training, Detectors, Object detection, Remote sensing, Proposals, Geology, Feature extraction, High-quality example mining, remote sensing BibRef

Zhang, L.Y.[Lu-Yang], Wang, H.T.[Hai-Tao], Wang, L.F.[Ling-Feng], Pan, C.[Chunhong], Liu, Q.[Qiang], Wang, X.[Xinyao],
Constraint Loss for Rotated Object Detection in Remote Sensing Images,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112
BibRef

Huang, Z.C.[Zhan-Chao], Li, W.[Wei], Xia, X.G.[Xiang-Gen], Wu, X.[Xin], Cai, Z.Q.[Zhao-Quan], Tao, R.[Ran],
A Novel Nonlocal-Aware Pyramid and Multiscale Multitask Refinement Detector for Object Detection in Remote Sensing Images,
GeoRS(60), 2022, pp. 1-20.
IEEE DOI 2112
Feature extraction, Detectors, Task analysis, Head, Visualization, Remote sensing, Neural networks, Attention, multiscale, multitask, remote sensing (RS) images BibRef

Ye, Y.X.[Yuan-Xin], Ren, X.Y.[Xiao-Yue], Zhu, B.[Bai], Tang, T.F.[Teng-Feng], Tan, X.[Xin], Gui, Y.[Yang], Yao, Q.[Qin],
An Adaptive Attention Fusion Mechanism Convolutional Network for Object Detection in Remote Sensing Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Dong, X.H.[Xiao-Hu], Qin, Y.[Yao], Gao, Y.H.[Ying-Hui], Fu, R.G.[Rui-Gang], Liu, S.L.[Song-Lin], Ye, Y.X.[Yuan-Xin],
Attention-Based Multi-Level Feature Fusion for Object Detection in Remote Sensing Images,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zhang, K.[Kaihua], Shen, H.[Haikuo],
Multi-Stage Feature Enhancement Pyramid Network for Detecting Objects in Optical Remote Sensing Images,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link 2202
BibRef

Luo, J.[Junkun], Hu, Y.M.[Yi-Min], Li, J.D.[Jia-Dong],
Surround-Net: A Multi-Branch Arbitrary-Oriented Detector for Remote Sensing,
RS(14), No. 7, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Liu, Z.M.[Zhi-Ming], Zhang, X.F.[Xue-Fei], Liu, C.Y.[Chong-Yang], Wang, H.[Hao], Sun, C.[Chao], Li, B.[Bin], Huang, P.[Pu], Li, Q.J.[Qing-Jun], Liu, Y.[Yu], Kuang, H.P.[Hai-Peng], Xiu, J.H.[Ji-Hong],
RelationRS: Relationship Representation Network for Object Detection in Aerial Images,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Wu, B.L.[Bing-Long], Shen, Y.[Yuan], Guo, S.X.[Shan-Xin], Chen, J.S.[Jin-Song], Sun, L.[Luyi], Li, H.Z.[Hong-Zhong], Ao, Y.[Yong],
High Quality Object Detection for Multiresolution Remote Sensing Imagery Using Cascaded Multi-Stage Detectors,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Kawauchi, H.[Hiroki], Fuse, T.[Takashi],
SHAP-Based Interpretable Object Detection Method for Satellite Imagery,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205
BibRef

Fang, Q.Y.[Qing-Yun], Wang, Z.K.[Zhao-Kui],
Cross-modality attentive feature fusion for object detection in multispectral remote sensing imagery,
PR(130), 2022, pp. 108786.
Elsevier DOI 2206
Cross-modality, Attention, Feature fusion, Object detection, Multispectral remote sensing imagery BibRef

Shen, Y.Y.[Yan-Yun], Liu, D.[Di], Zhang, F.Z.[Fei-Zhao], Zhang, Q.L.[Qing-Ling],
Fast and accurate multi-class geospatial object detection with large-size remote sensing imagery using CNN and Truncated NMS,
PandRS(191), 2022, pp. 235-249.
Elsevier DOI 2208
Partial objects at edge of tiles. Multi-class geospatial object detection, Convolutional neural network, Truncated NMS, Manhattan-Distance IOU BibRef

Tian, S.[Shu], Cao, L.[Lin], Kang, L.H.[Li-Hong], Xing, X.W.[Xiang-Wei], Tian, J.[Jing], Du, K.N.[Kang-Ning], Sun, K.[Ke], Fan, C.Z.[Chun-Zhuo], Fu, Y.[Yuzhe], Zhang, Y.[Ye],
A Novel Hybrid Attention-Driven Multistream Hierarchical Graph Embedding Network for Remote Sensing Object Detection,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Li, Z.[Zheng], Wang, Y.C.[Yong-Cheng], Zhang, N.[Ning], Zhang, Y.X.[Yu-Xi], Zhao, Z.K.[Zhi-Kang], Xu, D.D.[Dong-Dong], Ben, G.L.[Guang-Li], Gao, Y.X.[Yun-Xiao],
Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey,
RS(14), No. 10, 2022, pp. xx-yy.
DOI Link 2206
Survey, Object Detection. BibRef

Wang, J.Q.[Jia-Qi], Gong, Z.H.[Zhi-Hui], Liu, X.Y.[Xiang-Yun], Guo, H.T.[Hai-Tao], Yu, D.H.[Dong-Hang], Ding, L.[Lei],
Object Detection Based on Adaptive Feature-Aware Method in Optical Remote Sensing Images,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Zheng, K.L.[Kun-Long], Dong, Y.F.[Yi-Fan], Xu, W.[Wei], Su, Y.[Yun], Huang, P.P.[Ping-Ping],
A Method of Fusing Probability-Form Knowledge into Object Detection in Remote Sensing Images,
RS(14), No. 23, 2022, pp. xx-yy.
DOI Link 2212
BibRef


Li, J.J.[Juan-Juan], Hou, Z.Q.[Zhi-Qiang], Sun, Y.[Ying], Guo, H.[Hao], Ma, S.[Sugang],
Object Detection Algorithm Based on Global Information Fusion,
ICIVC22(276-281)
IEEE DOI 2301
Location awareness, Aggregates, Object detection, Feature extraction, Real-time systems, Data mining, FCOS, feature enhancement BibRef

Han, J.M.[Jia-Ming], Ding, J.[Jian], Xue, N.[Nan], Xia, G.S.[Gui-Song],
ReDet: A Rotation-equivariant Detector for Aerial Object Detection,
CVPR21(2785-2794)
IEEE DOI 2111
Codes, Computational modeling, Detectors, Object detection, Predictive models, Feature extraction BibRef

Martinson, E.[Eric], Furlong, B.[Bridget], Gillies, A.[Andy],
Training Rare Object Detection in Satellite Imagery with Synthetic GAN Images,
LLID21(2763-2770)
IEEE DOI 2109
Training, Solid modeling, Analytical models, Satellites, Generative adversarial networks BibRef

Xu, N.[Nuo], Huo, C.L.[Chun-Lei], Guo, J.C.[Jia-Cheng], Liu, Y.[Yiwei], Wang, J.[Jian], Pan, C.H.[Chun-Hong],
Adaptive Remote Sensing Image Attribute Learning for Active Object Detection,
ICPR21(111-118)
IEEE DOI 2105
Image quality, Image processing, Brightness, Imaging, Object detection, Reinforcement learning, Detectors BibRef

Barnes, D.K.[Dustin K.], Davis, S.R.[Sara R.], Hand, E.M.[Emily M.],
SAT-CNN: A Small Neural Network for Object Recognition from Satellite Imagery,
ISVC20(II:39-52).
Springer DOI 2103
BibRef

Ye, X.H.[Xin-Hai], Xiong, F.C.[Feng-Chao], Lu, J.F.[Jian-Feng], Zhao, H.F.[Hai-Feng], Zhou, J.[Jun],
M2-Net: A Multi-scale Multi-level Feature Enhanced Network for Object Detection in Optical Remote Sensing Images,
DICTA20(1-8)
IEEE DOI 2201
Semantics, Object detection, Detectors, Feature extraction, Optical imaging, Task analysis, Remote sensing, multi-scale analysis BibRef

Gao, D.S.[Da-Shan], Vasconcelos, N.M.[Nuno M.],
Integrated Learning of Saliency, Complex Features, and Object Detectors from Cluttered Scenes,
CVPR05(II: 282-287).
IEEE DOI 0507
BibRef
And:
An Experimental Comparison of Three Guiding Principles for the Detection Salient Image Locations: Stability, Complexity, and Discrimination,
AttenPerf05(III: 84-84).
IEEE DOI 0507
BibRef

Yao, J.[Jian], Zhang, Z.F.M.[Zhong-Fei Mark],
Object Detection in Aerial Imagery Based on Enhanced Semi-Supervised Learning,
ICCV05(II: 1012-1017).
IEEE DOI 0510
BibRef
And:
Semi-Supervised Learning Based Object Detection in Aerial Imagery,
CVPR05(I: 1011-1016).
IEEE DOI 0507
Use context for detection. BibRef

Bose, B.[Biswajit], Grimson, W.E.L.,
Improving object classification in far-field video,
CVPR04(II: 181-188).
IEEE DOI 0408
Low resolution. Scene specific context. BibRef

Chapter on 2-D Feature Analysis, Extraction and Representations, Shape, Skeletons, Texture continues in
Camouflaged Object Detection, Camouflage .


Last update:Jan 29, 2023 at 20:54:24