Laser & Optoelectronics Progress, Volume. 60, Issue 14, 1410016(2023)
Semantic Segmentation of Multispectral Remote Sensing Images Based on Band-Location Adaptive Selection
[1] Wang Q, He X, Li X L. Locality and structure regularized low rank representation for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 911-923(2019).
[2] Tobias O J, Seara R. Image segmentation by histogram thresholding using fuzzy sets[J]. IEEE Transactions on Image Processing, 11, 1457-1465(2002).
[3] Dhanachandra N, Manglem K, Chanu Y J. Image segmentation using K-means clustering algorithm and subtractive clustering algorithm[J]. Procedia Computer Science, 54, 764-771(2015).
[4] Liu J X, Ban W, Chen Y et al. Multi-dimensional CNN fused algorithm for hyperspectral remote sensing image classification[J]. Chinese Journal of Lasers, 48, 1610003(2021).
[5] Zhang S K, Wu Q X, Lin Z Y. Detection and segmentation of structured light stripe in weld image[J]. Acta Optica Sinica, 41, 0515002(2021).
[6] Yuan Y, Chen M H, Ke S T et al. Fundus image classification research based on ensemble convolutional neural network and vision transformer[J]. Chinese Journal of Lasers, 49, 2007205(2022).
[7] Luo Y J, Zhang J, Chen L et al. Lightweight target detection algorithm based on adaptive spatial feature fusion[J]. Laser & Optoelectronics Progress, 59, 0415004(2022).
[8] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C], 3431-3440(2015).
[9] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).
[10] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).
[11] Lin G S, Milan A, Shen C H et al. RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C], 5168-5177(2017).
[12] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C], 1520-1528(2015).
[13] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[C], 2999-3007(2017).
[14] Sudre C H, Li W Q, Vercauteren T et al. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations[M]. Cardoso M J, Arbel T, Carneiro G, et al. Deep learning in medical image analysis and multimodal learning for clinical decision support. Lecture notes in computer science, 10553, 240-248(2017).
[15] Hao S J, Zhou Y, Guo Y R. A brief survey on semantic segmentation with deep learning[J]. Neurocomputing, 406, 302-321(2020).
[16] Li R, Zheng S Y, Zhang C et al. Multiattention network for semantic segmentation of fine-resolution remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5607713(2022).
[17] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).
[18] Yu B, Yang L, Chen F. Semantic segmentation for high spatial resolution remote sensing images based on convolution neural network and pyramid pooling module[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 11, 3252-3261(2018).
[19] Ding L, Zhang J, Bruzzone L. Semantic segmentation of large-size VHR remote sensing images using a two-stage multiscale training architecture[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 5367-5376(2020).
[20] Mou L C, Hua Y S, Zhu X X. Relation matters: relational context-aware fully convolutional network for semantic segmentation of high-resolution aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 58, 7557-7569(2020).
[21] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).
[22] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).
[23] Li G, Li L L, Zhu H et al. Adaptive multiscale deep fusion residual network for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 8506-8521(2019).
[24] Mi L, Chen Z Z. Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 140-152(2020).
[25] Roy S K, Krishna G, Dubey S R et al. HybridSN: exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 17, 277-281(2020).
[26] Chen Y S, Jiang H L, Li C Y et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 6232-6251(2016).
[27] He M Y, Li B, Chen H H. Multi-scale 3D deep convolutional neural network for hyperspectral image classification[C], 3904-3908(2017).
[28] Liu R, Mi L, Chen Z Z. AFNet: adaptive fusion network for remote sensing image semantic segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 7871-7886(2021).
[29] Peng C L, Zhang K N, Ma Y et al. Cross fusion net: a fast semantic segmentation network for small-scale semantic information capturing in aerial scenes[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 5601313(2022).
[30] Guo M H, Xu T X, Liu J J et al. Attention mechanisms in computer vision: a survey[J]. Computational Visual Media, 8, 331-368(2022).
[31] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).
[32] Wang Q L, Wu B G, Zhu P F et al. ECA-net: efficient channel attention for deep convolutional neural networks[C], 11531-11539(2020).
[33] Qin Z Q, Zhang P Y, Wu F et al. FcaNet: frequency channel attention networks[C], 763-772(2021).
[34] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).
[35] Li H F, Qiu K J, Chen L et al. SCAttNet: semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 18, 905-909(2021).
[36] Zhao Q, Liu J H, Li Y W et al. Semantic segmentation with attention mechanism for remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-13(2021).
[37] Li R, Zheng S Y, Duan C X et al. Multistage attention ResU-Net for semantic segmentation of fine-resolution remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 19, 1-5(2022).
[38] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
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Zhengyin Liang, Xili Wang. Semantic Segmentation of Multispectral Remote Sensing Images Based on Band-Location Adaptive Selection[J]. Laser & Optoelectronics Progress, 2023, 60(14): 1410016
Category: Image Processing
Received: Aug. 5, 2022
Accepted: Sep. 26, 2022
Published Online: Aug. 10, 2023
The Author Email: Wang Xili (wangxili@snnu.edu.cn)