Deep hierarchical representation and segmentation of high resolution remote sensing images

Wang, Jun; Qin, Qiming; Li, Zhoujing; Ye, Xin; Wang, Jianhua; Yang, Xiucheng; Qin, Xuebin

doi:10.1109/igarss.2015.7326782

Cited by 15 publications

(13 citation statements)

References 3 publications

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“…Conventionally, a nonlinear function is provided after the convolutional operation, which is usually called activation function. There are a lot of alternatives for activation function, such as sigmoid function 1 1þe Àx and tanh function e x Àe Àx e x þe Àx . The most popular activation function nowadays is called rectified linear unit (ReLU).…”

Section: Transferred Deep Cnns For Remote Scene Classificationmentioning

confidence: 99%

“…Remote sensing image processing achieves great advances in recent years, from low-level tasks, such as segmentation, to high-level ones, such as classification. [1][2][3][4][5][6][7] However, the task becomes incrementally more difficult as the level of abstraction increases, going from pixels, to objects, and then scenes. Classifying remote scenes according to a set of semantic categories is a very challenging problem, because of high intra-class variability and low interclass distance.…”

Section: Introductionmentioning

confidence: 99%

“…ImageNet 1 is a large-scale dataset, which offers a very comprehensive database of more than 1.2 million categorized natural images of 1000+ classes [16]. Deep CNN models trained upon this dataset serve as the backbone for many segmentation, detection, and classification tasks on other datasets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Utilization of Deep Convolutional Neural Networks for Remote Sensing Scenes Classification

Luo¹,

Huang²,

Wang³

et al. 2019

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure

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Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, for the task of remote scene classification, there are no sufficient images to train a very deep CNN from scratch. Instead, transferring successful pre-trained deep CNNs to remote sensing tasks provides an effective solution. Firstly, from the viewpoint of generalization power, we try to find whether deep CNNs need to be deep when applied for remote scene classification. Then, the pre-trained deep CNNs with fixed parameters are transferred for remote scene classification, which solve the problem of timeconsuming and parameters over-fitting at the same time. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that transferred deep CNNs can achieve state-of-the-art results in unsupervised setting. This chapter also provides baseline for applying deep CNNs to other remote sensing tasks.

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Section: Transferred Deep Cnns For Remote Scene Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utilization of Deep Convolutional Neural Networks for Remote Sensing Scenes Classification

Luo¹,

Huang²,

Wang³

et al. 2019

Advanced Remote Sensing Technology for Synthetic Aperture Radar Applications, Tsunami Disasters, and Infrastructure

View full text Add to dashboard Cite

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“…Characterized by their rich and detailed spatial information, remote sensing scene images allow effective discrimination of objects by capturing subtle discrepancies from the contiguous shape of signatures associated with their pixels [5]. To extract features of different objects, a variety of handcrafted and learning-based classification algorithms have been successfully designed in recent [6] [7] [8] [9]. Of the various classification algorithms, deep convolutional neural networks (CNNs) have been used extensively and typically yield high classification performances.…”

Section: Introductionmentioning

confidence: 99%

MGFN: A Multi-Granularity Fusion Convolutional Neural Network for Remote Sensing Scene Classification

2021

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Convolutional neural networks (CNNs) have been successfully used in remote sensing scene classification and identification due to their ability to capture deep spatial feature representations. However, the performance of deep models inevitably encounters a bottleneck when multimodality-dominated scene classification rather than single-modality-dominated scene classification is performed, due to the high similarity among different categories. In this study, we propose a novel multi-granularity fusion convolutional neural network (MGFN) to automatically capture the latent ontological features of remote sensing images. We firstly design a multigranularity module that can progressively crop input images to learn multigrained features, which can describe images to different degrees. Based on a comparison of different granularities, we then design a maxout-based module to learn the corresponding Gaussian covariance matrices of different granularities, which can extract second-order features to express the latent ontological essence of inputs and select the most distinguished inputs. We thirdly provide an adaptive fusion module to fuse all features via normalization to combine features of different degrees using the adaptive fused module. Finally, an SVM classifier is used to classify the fused matrix of every input image. Extensive experimentation and evaluations, particularly for multimodality-dominated scenes, demonstrate that the proposed network can achieve promising results for public remote sensing datasets.

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“…Remote sensing image processing achieves great advances in recent years, from low-level tasks, such as segmentation, to high-level ones, such as classification [1][2][3][4][5][6][7]. However, the task becomes incrementally more difficult as the level of abstraction increases, going from pixels, to objects, and then scenes.…”

Section: Introductionmentioning

confidence: 99%

Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

et al. 2017

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Abstract:Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, in the field of remote sensing, there are not sufficient images to train a useful deep CNN. Instead, we tend to transfer successful pre-trained deep CNNs to remote sensing tasks. In the transferring process, generalization power of features in pre-trained deep CNNs plays the key role. In this paper, we propose two promising architectures to extract general features from pre-trained deep CNNs for remote scene classification. These two architectures suggest two directions for improvement. First, before the pre-trained deep CNNs, we design a linear PCA network (LPCANet) to synthesize spatial information of remote sensing images in each spectral channel. This design shortens the spatial "distance" of target and source datasets for pre-trained deep CNNs. Second, we introduce quaternion algebra to LPCANet, which further shortens the spectral "distance" between remote sensing images and images used to pre-train deep CNNs. With five well-known pre-trained deep CNNs, experimental results on three independent remote sensing datasets demonstrate that our proposed framework obtains state-of-the-art results without fine-tuning and feature fusing. This paper also provides baseline for transferring fresh pre-trained deep CNNs to other remote sensing tasks.

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Deep hierarchical representation and segmentation of high resolution remote sensing images

Cited by 15 publications

References 3 publications

Utilization of Deep Convolutional Neural Networks for Remote Sensing Scenes Classification

Utilization of Deep Convolutional Neural Networks for Remote Sensing Scenes Classification

MGFN: A Multi-Granularity Fusion Convolutional Neural Network for Remote Sensing Scene Classification

Transferring Pre-Trained Deep CNNs for Remote Scene Classification with General Features Learned from Linear PCA Network

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