Local shape association based retrieval of infrared satellite images

Ma, Aiyesha; Sethi, Ishwar K.

doi:10.1109/ism.2005.75

Cited by 20 publications

(13 citation statements)

References 7 publications

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“…4. Compute the optimal parameter W according to (8), and compute the binary code for the image database and query image by B = f (X) = sgn W T X and b q = f x q = sgn W T x q . 5.…”

Section: Repeatmentioning

confidence: 99%

“…To characterize remote sensing images, many low-level features have been presented and evaluated in the remote sensing image retrieval task. More specifically, the proposed low-level features included spectral features [4][5][6], shape features [7][8][9], texture features [10][11][12], local invariant features [13], and so forth. Although low-level features have been employed with a certain degree of success, they have a very limited capability in representing the high-level concepts that are presented by remote sensing images (i.e., the semantic content).…”

Section: Introductionmentioning

confidence: 99%

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Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

Tao

et al. 2017

IJGI

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Driven by the urgent demand of remote sensing big data management and knowledge discovery, large-scale remote sensing image retrieval (LSRSIR) has attracted more and more attention. As is well known, hashing learning has played an important role in coping with big data mining problems. In the literature, several hashing learning methods have been proposed to address LSRSIR. Until now, existing LSRSIR methods take only one type of feature descriptor as the input of hashing learning methods and ignore the complementary effects of multiple features, which may represent remote sensing images from different aspects. Different from the existing LSRSIR methods, this paper proposes a flexible multiple-feature hashing learning framework for LSRSIR, which takes multiple complementary features as the input and learns the hybrid feature mapping function, which projects multiple features of the remote sensing image to the low-dimensional binary (i.e., compact) feature representation. Furthermore, the compact feature representations can be directly utilized in LSRSIR with the aid of the hamming distance metric. In order to show the superiority of the proposed multiple feature hashing learning method, we compare the proposed approach with the existing methods on two publicly available large-scale remote sensing image datasets. Extensive experiments demonstrate that the proposed approach can significantly outperform the state-of-the-art approaches.

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“…4. Compute the optimal parameter W according to (8), and compute the binary code for the image database and query image by B = f (X) = sgn W T X and b q = f x q = sgn W T x q . 5.…”

Section: Repeatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

Tao

et al. 2017

IJGI

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show abstract

“…Classical image features, such as spectral features [4,5], texture features [6][7][8], shape features [9,10] and morphological features [7] are the most common features used for remote sensing image representation. Great success with regard to high-resolution remote sensing image retrieval have been achieved using these global features.…”

Section: Background and Related Studiesmentioning

confidence: 99%

High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective

Xiao

Yang

et al. 2017

Remote Sensing

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Because of recent advances in Convolutional Neural Networks (CNNs), traditional CNNs have been employed to extract thousands of codes as feature representations for image retrieval. In this paper, we propose that more powerful features for high-resolution remote sensing image representations can be learned using only several tens of codes; this approach can improve the retrieval accuracy and decrease the time and storage requirements. To accomplish this goal, we first investigate the learning of a series of features with different dimensions using a few tens to thousands of codes via our improved CNN frameworks. Then, a Principal Component Analysis (PCA) is introduced to compress the high-dimensional remote sensing image feature codes learned by traditional CNNs. Comprehensive comparisons are conducted to evaluate the retrieval performance based on feature codes of different dimensions learned by the improved CNNs as well as the PCA compression. To further demonstrate the powerful ability of the low-dimensional feature representation learned by the improved CNN frameworks, a Feature Weighted Map (FWM), which can perform feature visualization and provides a better understanding of the nature of Deep Convolutional Neural Networks (DCNNs) frameworks, is explored. All the CNN models are trained from scratch using a large-scale and high-resolution remote sensing image archive, which will be published and made available to the public. The experimental results show that our method outperforms state-of-the-art CNN frameworks in terms of accuracy and storage.

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“…For charactering high-resolution remote sensing images, low-level features such as spectral features [9,10], shape features [11,12], morphological features [5], texture features [13], and local invariant features [2] have been adopted and evaluated in the CB-HRRS-IR task. Although low-level features have been employed with a certain degree of success, they have a very limited capability in representing the high-level concepts presented by remote sensing images (i.e., the semantic content).…”

Section: Introductionmentioning

confidence: 99%

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

Zhang

Tao

et al. 2016

Remote Sensing

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Abstract:With the urgent demand for automatic management of large numbers of high-resolution remote sensing images, content-based high-resolution remote sensing image retrieval (CB-HRRS-IR) has attracted much research interest. Accordingly, this paper proposes a novel high-resolution remote sensing image retrieval approach via multiple feature representation and collaborative affinity metric fusion (IRMFRCAMF). In IRMFRCAMF, we design four unsupervised convolutional neural networks with different layers to generate four types of unsupervised features from the fine level to the coarse level. In addition to these four types of unsupervised features, we also implement four traditional feature descriptors, including local binary pattern (LBP), gray level co-occurrence (GLCM), maximal response 8 (MR8), and scale-invariant feature transform (SIFT). In order to fully incorporate the complementary information among multiple features of one image and the mutual information across auxiliary images in the image dataset, this paper advocates collaborative affinity metric fusion to measure the similarity between images. The performance evaluation of high-resolution remote sensing image retrieval is implemented on two public datasets, the UC Merced (UCM) dataset and the Wuhan University (WH) dataset. Large numbers of experiments show that our proposed IRMFRCAMF can significantly outperform the state-of-the-art approaches.Keywords: high-resolution remote sensing image management; content-based high-resolution remote sensing image retrieval (CB-HRRS-IR); unsupervised feature learning; collaborative affinity metric fusion

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Local shape association based retrieval of infrared satellite images

Abstract: Abstract

Cited by 20 publications

References 7 publications

Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

High-Resolution Remote Sensing Image Retrieval Based on CNNs from a Dimensional Perspective

Content-Based High-Resolution Remote Sensing Image Retrieval via Unsupervised Feature Learning and Collaborative Affinity Metric Fusion

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