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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.6
- 수록면
- 270 - 277 (8page)
이용수
초록· 키워드
오류제보하기
The adaptive sparse representation (ASR) can effectively combine the structure information of a sample dictionary and the sparsity of coding coefficients. This algorithm can effectively consider the correlation between training samples and convert between sparse representation-based classifier (SRC) and collaborative representation classification (CRC) under different training samples. Unlike SRC and CRC which use fixed norm constraints, ASR can adaptively adjust the constraints based on the correlation between different training samples, seeking a balance between l1 and l2 norm, greatly strengthening the robustness and adaptability of the classification algorithm. The correlation coefficients (CC) can better identify the pixels with strong correlation. Therefore, this article proposes a hyperspectral image classification method called correlation coefficients and adaptive sparse representation (CCASR), based on ASR and CC. This method is divided into three steps. In the first step, we determine the pixel to be measured and calculate the CC value between the pixel to be tested and various training samples. Then we represent the pixel using ASR and calculate the reconstruction error corresponding to each category. Finally, the target pixels are classified according to the reconstruction error and the CC value. In this article, a new hyperspectral image classification method is proposed by fusing CC and ASR. The method in this paper is verified through two sets of experimental data. In the hyperspectral image (Indian Pines), the overall accuracy of CCASR has reached 0.9596. In the hyperspectral images taken by HIS-300, the classification results show that the classification accuracy of the proposed method achieves 0.9354, which is better than other commonly used methods.
목차
Ⅰ. INTRODUCTION
Ⅱ. ADAPTIVE SPARSE REPRESENTATION CLASSIFICATION ALGORITHM
Ⅲ. CORRELATION COEFFICIENT
Ⅳ. HYPERSPECTRAL IMAGE EXPERIMENT AND ANALYSIS
Ⅴ. CONCLUSION
REFERENCES
참고문헌 (5)
참고문헌 신청
Y. Chen / 2011 / Hyperspectral image classification using dictionary-based sparse represen-tation / IEEE Trans. Geosci. Remote Sens 49:3973~3985
B. Tu / 2018 / Hyperspectral image classification via fusing correlation coefficient and joint sparse representation / IEEE Geosci. Re-mote Sens. Lett 15:340~344
W. Li / 2015 / Collaborative-representa tion-based nearest neighbor classifier for hyperspectral imag-ery / IEEE Geosci. Remote Sens. Lett 12:389~393
J. Wei / 2015 / Collaborative representation classifier based on K nearest neighbors for classification / J. Software Eng. 9:96~104
B. A. Olshausen / 1996 / Emergence of simple-cell receptive field properties by learning a sparse code for natural images / Nature 381:607~609
B. Tu / 2018 / Hyperspectral image classification via fusing correlation coefficient and joint sparse representation / IEEE Geosci. Re-mote Sens. Lett 15:340~344
W. Li / 2015 / Collaborative-representa tion-based nearest neighbor classifier for hyperspectral imag-ery / IEEE Geosci. Remote Sens. Lett 12:389~393
J. Wei / 2015 / Collaborative representation classifier based on K nearest neighbors for classification / J. Software Eng. 9:96~104
B. A. Olshausen / 1996 / Emergence of simple-cell receptive field properties by learning a sparse code for natural images / Nature 381:607~609
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