A remote sensing image classification method based on sparse representation

Wu, Shulei; Chen, Huandong; Bai, Yong; Zhu, Guangtian

doi:10.1007/s11042-016-3320-7

Cited by 10 publications

(8 citation statements)

References 37 publications

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“…In recent years, it has been widely used in remote sensing image recognition and classification. Literature [7] studied the application of SVM in building classification, and the classification accuracy reached 90%. Canny edge detection operator was used in literature [8] to remove short lines and curves, and Hough transform was used to detect long lines.…”

Section: A Remotely Sensed Imagery Classification Methods Based On Mamentioning

confidence: 99%

“…As a consequence, it is a challenging task to carry out the multitarget classification effectively and further interpret satellite data correctly [1]- [6]. The classic methods currently applied to natural image classification include K-nearest neighbor classification (K-NN), random forest (RF), support vector machine (SVM), sparse representation classifier (SRC) [7]- [10], etc. K-NN method uses the neighborhood information of the test sample as a reference to determine its category.…”

Section: Introductionmentioning

confidence: 99%

“…It has very high classification accuracy, but the high demand of computer storage and long operation time for generating over-complete dictionary are its main defects [11]. In addition to the above classification methods [12]- [15], the early classification methods applied to remote sensing images work is around the target based on the spectroscopic analysis of objects, i.e., a normalized index or a representative target characteristic curve is generated for classification and discrimination. The normalized difference index method refers to the index generated by the operation between special bands that can represent the obvious characteristics of the target.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Perspective Invariant Dissociaton Hash Method for Remotely Sensed Imagery Plusieurs Target Classification

Zhang

Xiao

2021

IEEE Access

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Plusieurs target classification based on remotely sensed imagery is a hot and difficult-ridden topic in recent years. Specifically, the time and space complexity of the classical pattern recognition model in the classification task based on remote sensing image is usually high, and the remotely sensed imagery is usually not normally captured. For the former, we introduce the low delay and low storage hash method into the plusieurs target classification of remotely sensed imagery. Aiming at the latter, in order to improve the effectiveness of the proposed model in monitoring perspective transformation data, a dissociation perspective invariant model is constructed. By fusing these two solutions, a perspective invariant dissociaton hash model for remotely sensed imagery plusieurs target classification is obtained. By adding perspective invariant constraint to the supervised dissociaton hash method, our method forces the same type of target to share the same binary code, increases the similarity of the hash code of the same type of target, and thus improves the performance. In order to verify the validity and universality of our method, two different data sets were used for experiments, in which different hash methods and different classification methods are compared. The experimental results show that, compared with the comparison methods, the proposed method improves the accuracy of plusieurs target classes with a small number of samples, thus achieving a higher overall classification accuracy. In addition, the advantages of hash method in low storage make the speed of proposed method improved compared with the classical pattern recognition method.

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Section: A Remotely Sensed Imagery Classification Methods Based On Mamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Perspective Invariant Dissociaton Hash Method for Remotely Sensed Imagery Plusieurs Target Classification

Zhang

Xiao

2021

IEEE Access

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“…Many researchers from the field of remote sensing are attracted by the superior performance of SRC and CRC. Wu et al [21] introduced an improved sparse representation-based classification method, which represented the test samples with a feature dictionary. Then, a novel sparse representation classification method [22] was proposed by Tang et al, which added the Local Binary Pattern(LBP) feature into the SRC model to extract the local texture of the remote-sensing image.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

et al. 2018

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In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.

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“…The paper by C. Wang et al [1] presents a completely rely on spatial geometry calculations to achieve the user's line of sight placement calculation method. The paper by S. Wu et al [9] introduces an improved classification method based on sparse representation by representing the test samples through a dictionary. The paper by Z. Xu et al [10] introduced a semantic-based model named video structural description (VSD) for representing and organizing the content in videos.…”

mentioning

confidence: 99%

Guest Editorial: Multimedia Data Sensing and Analyzing of Surveillance Systems

Luo

Liu

et al. 2016

Multimed Tools Appl

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Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. With the pervasive of the definition of the smart city, the surveillance system, huge number of video surveillance devices such as surveillance cameras. Understanding the semantics of surveillance device has been an important component in many video-based applications. Manual annotation and tagging has been considered as a reliable source of video semantics. Unfortunately, manual annotation is time-consuming and expensive when dealing with huge scale of video data. However, the semantic gap between semantics and video visual appearance is still a challenge towards automated ontology-driven video annotation. Thus, automatically understanding raw videos solely based on their visual appearance becomes an important yet challenging problem. Thus, it is important to accurately describe the video content and enable the organizing and searching potential videos.

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A remote sensing image classification method based on sparse representation

Cited by 10 publications

References 37 publications

A Novel Perspective Invariant Dissociaton Hash Method for Remotely Sensed Imagery Plusieurs Target Classification

A Novel Perspective Invariant Dissociaton Hash Method for Remotely Sensed Imagery Plusieurs Target Classification

Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

Guest Editorial: Multimedia Data Sensing and Analyzing of Surveillance Systems

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