A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

Gu, Yating; Wang, Yantian; Li, Yansheng

doi:10.3390/app9102110

Cited by 124 publications

(60 citation statements)

References 97 publications

(121 reference statements)

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“…Audebert et al, 2016Audebert et al, , 2017Audebert et al, , 2018Längkvist et al, 2016;Li et al, 2015;Li and Shao, 2014;Volpi and Tuia, 2017;Liu et al, 2018Liu et al, , 2017aPan et al, 2018a,b;Marmanis et al, 2016Marmanis et al, , 2018Wen et al, 2017;Zhao et al, 2017b). A comprehensive review of deep learning applications in the field of remote sensing can be found in ; Ma et al (2019); Gu et al (2019).…”

Section: Related Workmentioning

confidence: 99%

ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

Diakogiannis

Waldner

Caccetta

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

1,114

428

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Scene understanding of high resolution aerial images is of great importance for the task of automated monitoring in various remote sensing applications. Due to the large within-class and small between-class variance in pixel values of objects of interest, this remains a challenging task. In recent years, deep convolutional neural networks have started being used in remote sensing applications and demonstrate state of the art performance for pixel level classification of objects. Here we propose a reliable framework for performant results for the task of semantic segmentation of monotemporal very high resolution aerial images. Our framework consists of a novel deep learning architecture, ResUNet-a, and a novel loss function based on the Dice loss. ResUNet-a uses a UNet encoder/decoder backbone, in combination with residual connections, atrous convolutions, pyramid scene parsing pooling and multi-tasking inference. ResUNet-a infers sequentially the boundary of the objects, the distance transform of the segmentation mask, the segmentation mask and a colored reconstruction of the input. Each of the tasks is conditioned on the inference of the previous ones, thus establishing a conditioned relationship between the various tasks, as this is described through the architecture's computation graph. We analyse the performance of several flavours of the Generalized Dice loss for semantic segmentation, and we introduce a novel variant loss function for semantic segmentation of objects that has excellent convergence properties and behaves well even under the presence of highly imbalanced classes. The performance of our modeling framework is evaluated on the ISPRS 2D Potsdam dataset. Results show state-of-the-art performance with an average F1 score of 92.9% over all classes for our best model.

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Section: Related Workmentioning

confidence: 99%

ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

Diakogiannis

Waldner

Caccetta

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

1,114

428

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“…In the past several years, due to a large amount of training images and high-performance GPUs, deep learning techniques-in particular, supervised approaches such as deep convolutional neural networks (DCNNs)-have achieved relentless success in various high-level computer vision tasks, such as image classification, object detection, semantic segmentation, etc. [2][3][4]. The key advantage of these deep learning techniques is to learn high-level feature representations in an end-to-end fashion, which are more discriminative than traditional ones.…”

Section: Introductionmentioning

confidence: 99%

Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift

Zhang

et al. 2020

Symmetry

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Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

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“…The deep development of remote sensing technology in recent years has induced urgent demands for processing, analyzing and understanding the high-resolution remote sensing images. The most fundamental and key task for remote sensing image analysis (RSIA) is to recognize, detect, classify and retrieve the images belonging to multiple remote sensing categories like agricultural, airplane, forest and so on [1][2][3][4][5]. Among all these tasks, remote sensing image retrieval (RSIR) [2,[6][7][8] is the most challengeable in analyzing remote sensing data effectively.…”

Section: Introductionmentioning

confidence: 99%

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

Gou

Xue

et al. 2020

Sensors

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A rich line of works focus on designing elegant loss functions under the deep metric learning (DML) paradigm to learn a discriminative embedding space for remote sensing image retrieval (RSIR). Essentially, such embedding space could efficiently distinguish deep feature descriptors. So far, most existing losses used in RSIR are based on triplets, which have disadvantages of local optimization, slow convergence and insufficient use of similarity structure in a mini-batch. In this paper, we present a novel DML method named as global optimal structured loss to deal with the limitation of triplet loss. To be specific, we use a softmax function rather than a hinge function in our novel loss to realize global optimization. In addition, we present a novel optimal structured loss, which globally learn an efficient deep embedding space with mined informative sample pairs to force the positive pairs within a limitation and push the negative ones far away from a given boundary. We have conducted extensive experiments on four public remote sensing datasets and the results show that the proposed global optimal structured loss with pairs mining scheme achieves the state-of-the-art performance compared with the baselines.

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A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

Cited by 124 publications

References 97 publications

ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

ResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data

Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift

Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

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