Retrieval of remotely sensed imagery using spectral information content

Bretschneider, T.; Cavet, René; Kao, Odej

doi:10.1109/igarss.2002.1026510

Cited by 46 publications

(29 citation statements)

References 10 publications

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“…16 blocks by combining row wise as well as columns wise as shown in fig.4(iii) and equation (1). In matlab matrix form get converted into cell.…”

Section: Feature Extractionmentioning

confidence: 99%

“…The performance of method is measured in terms of average precision (P) and average recall (R), collectively f-measure. P and R are defining as follow sensed imagery using spectral information content" [1], proposed the CBIR using simple unsupervised query-byexample approach that can have exploitation of spatial features i.e. not only characterized by shape but by texture also (special distribution of spectral information) which will help to increase retrieval accuracy but just one feature exploitation 2.2 T.S.…”

Section: To Improve Performance In Terms Of Precision Recall and F-mmentioning

confidence: 99%

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A Novel Image Retrieval Technique based on Gabor Function, Local Tetra Pattern and ASMC

Ashok¹,

Manthalkar²

2016

IJAIS

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“…16 blocks by combining row wise as well as columns wise as shown in fig.4(iii) and equation (1). In matlab matrix form get converted into cell.…”

Section: Feature Extractionmentioning

confidence: 99%

Section: To Improve Performance In Terms Of Precision Recall and F-mmentioning

confidence: 99%

A Novel Image Retrieval Technique based on Gabor Function, Local Tetra Pattern and ASMC

Ashok¹,

Manthalkar²

2016

IJAIS

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“…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%

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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“…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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Retrieval of remotely sensed imagery using spectral information content

Cited by 46 publications

References 10 publications

A Novel Image Retrieval Technique based on Gabor Function, Local Tetra Pattern and ASMC

A Novel Image Retrieval Technique based on Gabor Function, Local Tetra Pattern and ASMC

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

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

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