Main Article Content
Remote sensing images are acquired using special platforms, sensors and are classified as aerial, multispectral and hyperspectral images. Multispectral and hyperspectral images are represented using large spectral vectors as compared to normal Red, Green, Blue (RGB) images. Hence, remote sensing image retrieval process from large archives is a challenging task. Remote sensing image retrieval mainly consist of feature representation as first step and finding out similar images to a query image as second step. Feature representation plays important part in the performance of remote sensing image retrieval process. Research work focuses on impact of feature representation of remote sensing images on the performance of remote sensing image retrieval. This study shows that more discriminative features of remote sensing images are needed to improve performance of remote sensing image retrieval process.
Moataz Aboras, Hani Amasha, Issa Ibraheem(2015), Early detection of melanoma using multispectral imaging and artificial intelligence techniques American Journal of Biomedical and Life Sciences 2015; 3(2-3): 29-33
Emmanuel Christophe, Corinne Mailhes, and Pierre Duhamel, Hyperspectral Image Compression: Adapting SPIHT and EZW to Anisotropic 3-D Wavelet Coding, IEEE Transactions On Image Processing, VOL. 17, NO. 12, DECEMBER 2008 1057-7149/$25.00 © 2008 IEEE
P. Li, P. Ren, Partial randomness hashing for large-scale remote sensing image retrieval, IEEE Geosci. Remote Sens Lett 14 (3) (2017) 464–468.
T. Reato, B. Demir, L. Bruzzone, An unsupervised multicode hashing method for accurate and scalable remote sensing image retrieval, IEEE Geosci. Remote Sens. Lett. 16 (2) (2019) 276–280.
J. Kong, Q. Sun, M. Mukherjee, J. Lloret, Low-rank hypergraph hashing for largescale remote sensing image retrieval, Remote Sens (Basel) 12 (2020) 1164.
L. Han, P. Li, X. Bai, C. Grecos, X. Zhang, P. Ren, Cohesion intensive deep hashing for remote sensing image retrieval, Remote Sens (Basel) 12 (1) (2020) 101.
S. Roy, E. Sangineto, B. Demir, N. Sebe, Metric-learning-based deep hashing network for content-based retrieval of remote sensing images, IEEE Geosci. Remote Sens Lett. (2020) in press.
R. Fernandez-Beltran, B. Demir, F. Pla, A. Plaza, Unsupervised remote sensing image retrieval using probabilistic latent semantic hashing, IEEE Geosci. Remote Sens Lett. (2020) in press.
P. Li, L. Han, X. Tao, X. Zhang, C. Grecos, A. Plaza, P. Ren, Hashing nets for hashing: a quantized deep learning to hash framework for remote sensing image retrieval, IEEE Transactions on Geoscience and Remote Sensing (2020)
Y. Hongyu, L. Bicheng, C. Wen, Remote sensing imagery retrieval based-on gabor texture feature classification, in: Proceedings of ICSP, 2004.
P. Maheshwary, N. Srivastava, Prototype system for retrieval of remote sensing images based on color moment and gray level co-occurrence matrix, Int. J Comput Sci Issues 3 (2009) 20–23.
Z. Shao, W. Zhou, L. Zhang, J. Hou, Improved color texture descriptors for remote sensing image retrieval, J Appl Remote Sens 8 (1) (2014), 083584.
E. Aptoula, Remote sensing image retrieval with global morphological texture descriptors, IEEE Trans Geosci Remote Sens. 52 (2014) 3023–3034.
S. Bouteldja, A. Kourgli, Multiscale texture features for the retrieval of high resolution satellite images, in: Proceedings of IWSSIP, 2015.
I. Tekeste, B. Demir, Advanced Local Binary Patterns for Remote Sensing Image Retrieval, in: Proceedings of IGARSS, 2018.
K. Sukhia, M. Riaz, A. Ghafoor, S. Ali, Content-based remote sensing image retrieval using multi-scale local ternary pattern, Digit Signal Process 104 (2020), 102765.
D.G. Lowe, Distinctive image features from scale-invariant keypoints, Int J Comput Vis 60 (2) (2004) 91–110.
A. Ma, I. Sethi, Local shape association based retrieval of infrared satellite images, in: Proceedings of ISM, 2005.
G. Scott, M. Klaric, C. Davis, C. Shyu, Entropy-balanced bitmap tree for shapebased object retrieval from large-scale satellite imagery databases, IEEE Trans Geosci Remote Sens 49 (2011) 1603–1616.
X. Wang, Z. Shao, X. Zhou, J. Liu, A novel remote sensing image retrieval method based on visual salient point features, Sensor Review 34 (4) (2014) 349–359.
P. Maragos, Pattern spectrum and multiscale shape representation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (7) (1989) 701–716
H. Jegou, M. Douze, C. Schmid, P. Perez, Aggregating local descriptors into a compact image representation, in: Proceedings of CVPR, 2010.
Y. Yang, S. Newsam, Geographic image retrieval using local invariant features, IEEE Trans Geosci. Remote Sens 51 (2) (2012) 818–832
W. Zhou, Z. Shao, C. Diao, Q. Cheng, High-resolution remote-sensing imagery retrieval using sparse features by auto-encoder, Remote Sens Lett. 6 (2015) 775–783.
J. Yang, J. Liu, Q. Dai, An improved bag-of-words framework for remote sensing image retrieval in large-scale image databases, Int J Digit Earth 8 (4) (2015) 273–292
P. Bosilj, E. Aptoula, S. Lefevre, E. Kijak, Retrieval of remote sensing images with pattern spectra descriptors, ISPRS Int J Geoinf 5 (12) (2016) 228.
R. Imbriaco, C. Sebastian, E. Bondarev, P. deWith, Aggregated deep local features for remote sensing image retrieval, Remote Sens (Basel) 11 (5) (2019) 493.
Yansheng Li , Yongjun Zhang, Chao Tao and Hu Zhu ,Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion, Remote Sens., 8, 709; doi:10.3390/rs8090709(2016)
W. Xiong, Y. Lv, Y. Cui, X. Zhang, X. Gu, A discriminative feature learning approach for remote sensing image retrieval, Remote Sens (Basel) 11 (3) (2019) 281.
U. Chaudhuri, B. Banerjee, A. Bhattacharya, Siamese graph convolutional network for content based remote sensing image retrieval, Computer Vis. Image Underst 184 (2019) 22–30.
R. Cao, Q. Zhang, J. Zhu, Q. Li, Q. Li, B. Liu, G. Qiu, Enhancing remote sensing image retrieval using a triplet deep metric learning network, Int J Remote Sens (2019).
Chung, Woo-Jeoung Nam and Seong-Whan Lee , Rotation Invariant Aerial Image Retrieval with Group Convolutional Metric Learning Hyunseung 978-1-7281-8808-9/20/$31.00 ©2020 IEEE
Y. Liu, Z. Han, C. Chen, L. Ding, Y. Liu, Eagle-eyed multitask CNNs for aerial image retrieval and scene classification, IEEE Trans Geosci Remote Sens (2020).
Min-Sub Yun, Woo-Jeoung Nam, and Seong-Whan Lee, Coarse-to-Fine Deep Metric Learning for Remote Sensing Image Retrieval, Remote Sens. , 12, 219; doi:10.3390/rs12020219(2020)
Yun Cao, Yuebin Wang , Junhuan Peng, Liqiang Zhang , Linlin Xu, Kai Yan , and Lihua Li, DML-GANR: Deep Metric Learning With Generative Adversarial Network Regularization for High Spatial Resolution Remote Sensing Image Retrieval, IEEE Transactions on geoscience and remote sensing, VOL. 58, NO. 12, DECEMBER 2020
Li Liu , Yuebin Wang ,Junhuan Peng, and Antonio Plaza , DFLLR: Deep Feature Learning With Latent Relationship Embedding for Remote Sensing Image Retrieval, IEEE transactions on geoscience and remote sensing, VOL. 60, 2022
Xinyue Li, Song Wei,Jian Wang , Yanling Du, and Mengying Ge , Adaptive Multi-Proxy for Remote Sensing Image Retrieval, Remote Sens.,14,5615(2022).
Shawn D. Newsam and Chandrika Kamath , Retrieval Using Texture Features in High Resolution Multi-spectral Satellite Imagery , UCRL-CONF-201981(2004)
[Chandani Joshi , Saurabh Mukherjee, Empirical Analysis of SIFT, Gabor and Fused Feature Classification Using SVM for Multispectral Satellite Image Retrieval, Fourth International Conference on Image Information Processing (ICIIP) (2017)
Jinmika Wijitdechakul et.al., UAV-based Multispectral Aerial Image Retrieval using Spectral Feature and Semantic Computing , International Electronics Symposium on Knowledge Creation and Intelligent Computing (IES-KCIC)(2017)
Devulapalli Sudheer, Rajakumar Krishnan, Multiscale Texture Analysis and Color Coherence Vector Based Feature Descriptor for Multispectral Image Retrieval,, Advances in Science, Technology and Engineering Systems Journal Vol. 4, No. 6, 270-279 (2019)
Xuelei Chen, Cunyue Lu, an end-to-end adversarial hashing method for unsupervised multispectral remote sensing image retrieval, ICIP 2020
B. Sathiyaprasad, B. Satheesh Kumar, Multi Spectral Image Retrieval in Remote Sensing Big Data using Fast Recurrent Convolutional Neural Network , 2022 International Conference for Advancement in Technology (ICONAT) Goa, India. Jan 21-22, 2022
Gran˜a n, Miguel A. Veganzones , An end member-based distance for content based hyperspectral image retrieval Manuel , Pattern Recognition 45 ,3472–3489(2012)
Miguel A. Veganzones, Mihai Datcu and Manuel Gra˜na1,Dictionary based hyperspectral imageretrieval, In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods, pages426-432(2012)
Miguel Angel Veganzones, and Manuel Graña, , A Spectral/Spatial CBIR System for Hyperspectral Images, IEEE Journal of selected topics in applied earth observations and remote sensing, VOL. 5, NO. 2, APRIL 2012
Fatih Ömrüuzun , Begüm Demir, Lorenzo Bruzzone, Yasemin Yard?mc? Çetin1, Content based hyperspectral image retrieval using bag of endmembers image descriptors (2015)
Lu Chen, Jing Zhang, Xi Liang, Jiafeng Li, Li Zhuo , Deep Spectral-Spatial Feature Extraction Based on DCGAN for Hyperspectral Image Retrieval , 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing
Jing Zhang, Lu Chen, Li Zhuo, Xi Liang and Jiafeng Li, An Efficient Hyperspectral Image Retrieval Method: Deep Spectral-Spatial Feature Extraction with DCGAN and Dimensionality Reduction Using t-SNE-Based NM Hashing, Remote Sens. 2018, 10, 271; doi:10.3390/rs10020271(2018)
Olfa Ben-Ahmed , Thierry Urruty , Noël Richard and Christine Fernandez-Maloigne , Toward Content-Based Hyperspectral Remote Sensing Image Retrieval (CB-HRSIR): A Preliminary Study Based on Spectral Sensitivity Functions , Remote Sens. 2019, 11, 600; doi:10.3390/rs11050600(2019)