Hyperspectral Target Detection via Co-Teaching Multiple Instance Neural Network with Deterministic Annealing Algorithm

Multiple instance learning (MIL) effectively solves the inaccurate labeled hyperspectral target detection problems, which models the region containing the target as a positive bag, and the area without the target as a negative bag. However, the real labels of instances in the positive bag are unknow...

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Vydané v:IEEE International Geoscience and Remote Sensing Symposium proceedings s. 6568 - 6571
Hlavní autori: Liu, Lirong, Jiao, Changzhe, Li, Jiaming, Chen, Chao
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 16.07.2023
Predmet:
Annealing
Co-teaching
deterministic annealing
hyperspectral target detection
multiple instance learning
Neural networks
Object detection
Robustness
Stability analysis
Training
Training data
ISSN:2153-7003
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Popis
Shrnutí:Multiple instance learning (MIL) effectively solves the inaccurate labeled hyperspectral target detection problems, which models the region containing the target as a positive bag, and the area without the target as a negative bag. However, the real labels of instances in the positive bag are unknown, which increases the difficulty of hyperspectral target detection. In this paper, we propose a hyperspectral target detection method based on Co-teaching multiple instance neural network (Coteaching MINN), in which the positive instances in the positive bags are selected to participate in the training of the network by comparing the loss value of the training data. Furthermore, deterministic annealing (DA) algorithm is proposed to enhance the stability of the network during the training process. The experimental results on simulated data and real hyperspectral data demonstrate the advancement and robustness of the proposed method.
ISSN:2153-7003
DOI:10.1109/IGARSS52108.2023.10282469