3D geo-information is of great significance for understanding the living environment; however, 3D perception from remote sensing data, especially on a large scale, is restricted. To tackle this problem, we propose a method for monocular height estimation from optical imagery, which is currently one of the richest sources of remote sensing data. As an ill-posed problem, monocular height estimation requires well-designed networks for enhanced representations to improve performance. Moreover, the distribution of height values is long-tailed with the low-height pixels, e.g., the background, as the head, and thus trained networks are usually biased and tend to underestimate building heights. To solve the problems, instead of formalizing the problem as a regression task, we propose HTC-DC Net following the classification-regression paradigm, with the head-tail cut (HTC) and the distribution-based constraints (DCs) as the main contributions. HTC-DC Net is composed of the backbone network as the feature extractor, the HTC-AdaBins module, and the hybrid regression process. The HTC-AdaBins module serves as the classification phase to determine bins adaptive to each input image. It is equipped with a vision transformer encoder to incorporate local context with holistic information and involves an HTC to address the long-tailed problem in monocular height estimation for balancing the performances of foreground and background pixels. The hybrid regression process does the regression via the smoothing of bins from the classification phase, which is trained via DCs. The proposed network is tested on three datasets of different resolutions, namely ISPRS Vaihingen (0.09 m), DFC19 (1.3 m) and GBH (3 m). Experimental results show the superiority of the proposed network over existing methods by large margins. Extensive ablation studies demonstrate the effectiveness of each design component.

翻译：三维地理信息对于理解生存环境具有重要意义；然而，从遥感数据（尤其是大尺度数据）进行三维感知仍受到限制。为解决此问题，我们提出了一种基于光学影像的单目高度估计方法，而光学影像是目前最丰富的遥感数据来源之一。由于单目高度估计属于病态问题，需要设计具有增强表征能力的网络以提升性能。此外，高度值分布呈长尾特性，低高度像素（如背景）占据头部，导致训练后的网络通常存在偏差，倾向于低估建筑物高度。为解决上述问题，我们摒弃了将问题形式化为回归任务的传统做法，转而遵循分类-回归范式提出HTC-DC Net，其核心贡献包括头尾截断（HTC）与基于分布的约束（DCs）。HTC-DC Net由作为特征提取器的骨干网络、HTC-AdaBins模块以及混合回归过程组成。HTC-AdaBins模块承担分类阶段任务，用于确定适应每张输入图像的自适应分箱区间。该模块配备了视觉Transformer编码器以融合局部上下文与全局信息，并通过引入HTC来解决单目高度估计中的长尾问题，从而平衡前景与背景像素的性能。混合回归过程通过对分类阶段产生的分箱区间进行平滑处理实现回归，并借助DCs进行训练。所提网络在三个不同分辨率的公开数据集（ISPRS Vaihingen（0.09米）、DFC19（1.3米）、GBH（3米））上进行了测试。实验结果表明，该网络在性能上显著优于现有方法。大量消融研究证实了各设计组件的有效性。