Variational Multiple-Instance Learning With Embedding Correlation Modeling for Hyperspectral Target Detection.

Abstract

The hyperspectral target detection is widely concerned in geoscience and remote sensing due to the abundant spectral information in hyperspectral imagery. However, the detection performance is highly dependent on the high-quality target signature or pixel-level supervised signals, which are extremely challenging and costly. In this article, we propose a variational multiple-instance neural network with embedding correlation modeling (VMIL-ECM) for weakly supervised hyperspectral target detection, which relaxes the rigid target prior (e.g., target signatures and/or pixel-level annotations), and only region-level labels are required. VMIL-ECM explicitly models the location of the targets within the region as a latent variable under the nonindependent and identically distributed (non-i.i.d.) assumption to estimate the underlying ground-truth target locations. The expectation-maximization (EM) algorithm is employed to iteratively optimize the posterior distribution of latent variables and learn discriminative spectral features for the target detection. To fully utilize the contextual information within the hyperspectral region, a permutation-invariant transformer-based structure is devised to explore the embedding correlation among instances. Moreover, a dynamic thresholding strategy is adopted to produce the reliable fine-grained supervised signals. Extensive experiments on three simulated datasets and two real-field datasets are conducted to verify the effectiveness of VMIL-ECM, and the state-of-the-art performance has been achieved over the existing comparison methods. The code for the VMIL-ECM is publicly available at: https://github.com/BoYangXDU/VMIL-ECM.