To avoid using a large 4D-Hough counting space (HCS) and complex invariant features of generalized Hough transform (GHT) or its extensions when detecting objects in remote sensing image (RSI), a tensored GHT (TGHT) is proposed to extract object contour by simple gradient angle feature in a 2D-HCS using a single training sample. Considering that tensor can record the structure relationship of object contour, tensor representation R-table is constructed to record the contour information of template. For slice centered at each position of RSI, the tensor-space-based voting mechanism is presented to use the tensor that records the contour information of slice to gather votes at the same entry of 2D-HCS. Furthermore, a multiorder binary-tree-based searching method is presented to accelerate voting by searching the index numbers of elements in tensors. In addition, by solving the tensor-space-based optimization problem that is used to determine the candidates objects, the cause of false alarms (FAs) caused by interferences with complex contour and FAs caused by interferences that are partial-similar to objects is revealed, and the matching rate and matching sparsity-based strategies are then proposed to remove these FAs. Using public RSI datasets with different scenes, experimental results demonstrate that TGHT reduces nearly 99% storage requirement compared with GHT for RSI with size exceeding 1000 × 1000 under small time consumption, and outperforms the well-known contour extraction methods and state-of-the-art deep-learning-based methods in terms of precision and recall.

中文翻译：

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用于遥感图像中目标检测的张量广义霍夫变换


为了避免在检测遥感图像（RSI）中的物体时使用大的4D-Hough计数空间（HCS）和广义霍夫变换（GHT）或其扩展的复杂不变特征，提出了一种张量GHT（TGHT）来提取物体轮廓通过使用单个训练样本的 2D-HCS 中的简单梯度角度特征。考虑到张量可以记录物体轮廓的结构关系，构造张量表示R表来记录模板的轮廓信息。对于以RSI每个位置为中心的切片，提出基于张量空间的投票机制，利用记录切片轮廓信息的张量在2D-HCS的同一条目处进行投票。此外，提出了一种基于多阶二叉树的搜索方法，通过搜索张量中元素的索引号来加速投票。此外，通过求解用于确定候选对象的基于张量空间的优化问题，揭示了复杂轮廓干扰引起的误报（FA）和与对象部分相似的干扰引起的误报（FA）的原因，然后提出基于匹配率和匹配稀疏性的策略来删除这些 FA。使用不同场景的公共RSI数据集，实验结果表明，对于大小超过1000×1000的RSI，TGHT在较小的时间消耗下比GHT减少了近99%的存储需求，并且优于众所周知的轮廓提取方法和现状-在精确度和召回率方面基于深度学习的艺术方法。