In the field of remote sensing image interpretation, utilizing Convolutional Neural Networks (CNNs) for building extraction is a highly significant task. End-to-end building extraction methods typically consist of two key components: the encoder and the decoder. However, during building extraction facilitated by the encoder, down-sampling operations often lead to a loss of boundary features in the segmented objects. Many of these lost features correspond to the boundaries of buildings, and the reduction of smaller-scale boundaries features diminishes the network's attention to building boundary, resulting in blurred architectural delineation. In this paper, we propose the Reshape Feature Distribution (RFD-Net) network to alleviate the problem of boundary blurriness. We embed a reshaping feature distribution module within the network, which manipulates the data distribution of feature values by compressing the maximum values and elevating the minimum values. This module can effectively increase the magnitude of feature values at positions corresponding to building boundaries in the feature maps, subsequently enhancing the network's attention to building boundaries and alleviating the problem of boundary blurriness. We conducted experiments on the WHU dataset, demonstrating the effectiveness of our proposed approach.
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