Sensor-Independent Hyperspectral Target Detection With Semisupervised Domain Adaptive Few-Shot Learning

Abstract

Deep learning-based hyperspectral target detection (HTD) is potentially hindered by the limited training samples and sensor-dependent transferability. To address this issue, we propose a novel semisupervised domain adaptive few-shot learning (SDAFL) model to adaptively transfer similarity/dissimilarity measurement from source domain with sufficient labeled samples to target domain in an adversarial manner, where source data and target data can be collected by different sensors, i.e., sensor-independent. In order to alleviate negative transfer, residual channel attention (RCA) and weighted domain adaptation (WDA) are used to automatically select representative features and assign easy-transferred samples with higher priority. In addition, we adopt modulated deformable convolution (MDConv) to make the receptive field fit image spatial structure and also introduce a discriminatively boosted loss (DBL) function based on the prior known target signature to further enhance feature distinction, where intraclass similarity is improved, while interclass similarity is suppressed. After extracting discriminative features through the SDAFL model, guided filter and t-distribution kernel are jointly used for spatial–spectral target detection (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> TD). It should be noted that only the spectral signature of the desired object is needed in the target domain. Experimental results and analysis on three real hyperspectral images (HSIs) verify the efficiency and superiority of our proposed sensor-independent hyperspectral target detection (SIHTD) method compared with other algorithms.