Attention mechanism-based image captioning methods have achieved good results in the remote sensing field, but are driven by tagged sentences, which is called passive attention. However, different observers may give different levels of attention to the same image. The attention of observers during testing, then, may not be consistent with the attention during training. As a direct and natural human-machine interaction, speech is much faster than typing sentences. Sound can represent the attention of different observers. This is called active attention. Active attention can be more targeted to describe the image; for example, in disaster assessments, the situation can be obtained quickly and the corresponding disaster areas can be located related to the specific disaster. A novel sound active attention framework is proposed for more specific caption generation according to the interest of the observer. First, sound is modeled by mel-frequency cepstral coefficients (MFCCs) and the image is encoded by convolutional neural networks (CNNs). Then, to handle the continuity characteristic of sound, a sound module and an attention module are designed based on the gated recurrent units (GRUs). Finally, the sound-guided image feature processed by the attention module is imported into the output module to generate descriptive sentence. Experiments based on both fake and real sound data sets show that the proposed method can generate sentences that can capture the focus of human. © 1980-2012 IEEE.