Remote Sensing Image Scene Classification Using Bag of Convolutional Features

Cheng, Gong; Li, Zhenpeng; Yao, Xiwen; Guo, Lei; Zhang, Wei

doi:10.1109/lgrs.2017.2731997

Cited by 304 publications

(153 citation statements)

References 29 publications

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“…Using the complementary information of different features, the spatial feature extraction method may be a better choice for reducing the dimensionality of hyperspectral images before band selection. In the future work, more advanced features, such as deep learning-based features, can be considered to improve the performance further [32,33]. Besides, the spectral and spatial information can be fused to obtain more powerful features [34,35].…”

Section: Resultsmentioning

confidence: 99%

Fast hyperspectral band selection based on spatial feature extraction

Cao

Wang

et al. 2018

J Real-Time Image Proc

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Hyperspectral images usually consist of hundreds of spectral bands, which can be used to precisely characterize different land cover types. However, the high dimensionality also has some disadvantages, such as the Hughes effect and a high storage demand. Band selection is an effective method to address these issues. However, most band selection algorithms are conducted with the high-dimensional band images, which will bring high computation complexity and may deteriorate the selection performance. In this paper, spatial feature extraction is used to reduce the dimensionality of band images and improve the band selection performance. The experiment results obtained on three real hyperspectral datasets confirmed that the spatial feature extraction-based approach exhibits more robust classification accuracy when compared with other methods. Besides, the proposed method can dramatically reduce the dimensionality of each band image, which makes it possible for band selection to be implemented in real time situations.

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Section: Resultsmentioning

confidence: 99%

Fast hyperspectral band selection based on spatial feature extraction

Cao

Wang

et al. 2018

J Real-Time Image Proc

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“…Additionally, we perform RSIR using features extracted from two state-of-the-art RS classification networks. These are Bag-of-Convolutional-Features (BoCF) [61] and Discriminative CNNs (D-CNN) [62]. For BoCF, we directly use the histogrammic image representations extracted from the pretrained VGG16 network.…”

Section: Comparison To Other Rsir Systemsmentioning

confidence: 99%

Aggregated Deep Local Features for Remote Sensing Image Retrieval

et al. 2019

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Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g. 50% faster than the current systems.

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“…Adaptation. The classical pattern recognition and machine learning tasks [21][22][23] mainly adopt a robust classifier learnt by annotated training data and assume the testing data and the training data belong to the same feature space or distribution. However, it is unrealistic in real-world applications because of the high price of manual labeling training samples and environmental restrictions.…”

Section: Transfer Learning and Domainmentioning

confidence: 99%

Transferable Feature Representation for Visible‐to‐Infrared Cross‐Dataset Human Action Recognition

Liu

et al. 2018

Complexity

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Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the idea of transfer learning, an infrared human action recognition framework using auxiliary data from visible light is proposed to solve the problem of limited infrared action data. In the proposed framework, we first construct a novel Cross-Dataset Feature Alignment and Generalization (CDFAG) framework to map the infrared data and visible light data into a common feature space, where Kernel Manifold Alignment (KEMA) and a dual alignedto-generalized encoders (AGE) model are employed to represent the feature. Then, a support vector machine (SVM) is trained, using both the infrared data and visible light data, and can classify the features derived from infrared data. The proposed method is evaluated on InfAR, which is a publicly available infrared human action dataset. To build up auxiliary data, we set up a novel visible light action dataset XD145. Experimental results show that the proposed method can achieve state-of-the-art performance compared with several transfer learning and domain adaptation methods.

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Remote Sensing Image Scene Classification Using Bag of Convolutional Features

Cited by 304 publications

References 29 publications

Fast hyperspectral band selection based on spatial feature extraction

Fast hyperspectral band selection based on spatial feature extraction

Aggregated Deep Local Features for Remote Sensing Image Retrieval

Transferable Feature Representation for Visible‐to‐Infrared Cross‐Dataset Human Action Recognition

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