Negative Bootstrapping for Weakly Supervised Target Detection in Remote Sensing Images

When training a classifier in a traditional weakly supervised learning scheme, negative samples are obtained by randomly sampling. However, it may bring deterioration or fluctuation for the performance of the classifier during the iterative training process. Considering a classifier is inclined to misclassify negative examples which resemble positive ones, comprising these misclassified and informative negatives should be important for enhancing the effectiveness and robustness of the classifier. In this paper, we propose to integrate Negative Bootstrapping scheme into weakly supervised learning framework to achieve effective target detection in remote sensing images. Compared with traditional weakly supervised target detection schemes, this method mainly has three advantages. Firstly, our model training framework converges more stable and faster by selecting the most discriminative training samples. Secondly, on each iteration, we utilize the negative samples which are most easily misclassified to refine target detector, obtaining better performance. Thirdly, we employ a pre-trained convolutional neural network (CNN) model named Caffe to extract high-level features from RSIs, which carry more semantic meanings and hence yield effective image representation. Comprehensive evaluations on a high resolution airplane dataset and comparisons with state-of-the-art weakly supervised target detection approaches demonstrate the effectiveness and robustness of the proposed method.