An unsupervised augmentation framework for deep learning based geospatial object detection: a summary of results
An unsupervised augmentation framework for deep learning based geospatial object detection: a summary of results
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基于深度学习的地理空间对象检测的无监督增强框架：结果摘要
DOI：
10.1145/3274895.3274901
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Knight, Joseph
中科院分区：
文献类型：
--
作者：
Xie, Yiqun;Bhojwani, Rahul;Shekhar, Shashi;Knight, Joseph
给定空间域中的遥感数据集，我们的目标是利用深度学习框架检测具有最小边界矩形（即角度感知）的地理空间对象。地理空间对象（例如建筑物、车辆、农场）为各种社会应用提供有意义的信息，包括城市规划、人口普查、可持续发展、安全监控、农业管理等。这些对象的检测具有挑战性，因为由于地形、规划等原因，它们的方向往往严重混合且不与图像帧的正交方向平行。此外，大多数类型对象的带有角度信息的训练数据非常有限。在相关工作中，最先进的深度学习框架使用正交边界矩形（即边与输入图像的边平行）来检测对象，因此它们无法识别对象的方向并在对象上生成松散的矩形边界。我们提出了一种无监督增强（UA）框架来检测具有一般最小边界矩形（即带角度）的地理空间对象。 UA框架包含两种方案，即基于ROtation-Vector（ROV）的方案和基于上下文的方案。该方案完全避免了对以下方面的需要：（1）带有注释角度的附加地面实况数据； （2）现有网络架构的重构； (3)再培训。实验结果表明，UA 框架可以很好地近似物体的角度，并在物体上生成更紧密的边界框。
Given remote sensing datasets in a spatial domain, we aim to detect geospatial objects with minimum bounding rectangles (i.e., angle-aware) leveraging deep learning frameworks. Geospatial objects (e.g., buildings, vehicles, farms) provide meaningful information for a variety of societal applications, including urban planning, census, sustainable development, security surveillance, agricultural management, etc. The detection of these objects are challenging because their directions are often heavily mixed and not parallel to the orthogonal directions of an image frame due to topography, planning, etc. In addition, there is very limited training data with angle information for most types of objects. In related work, state-of-the-art deep learning frameworks detect objects using orthogonal bounding rectangles (i.e., sides are parallel to the sides of an input image), so they cannot identify the directions of objects and generate loose rectangular bounds on objects. We propose an Unsupervised Augmentation (UA) framework to detect geospatial objects with general minimum bounding rectangles (i.e., with angles). The UA framework contains two schemes, namely a ROtation-Vector (ROV) based scheme and a context-based scheme. The schemes completely avoid the need for: (1) additional ground-truth data with annotated angles; (2) restructuring of existing network architectures; and (3) re-training. Experimental results show that the UA framework can well approximate the angles of objects and generate much tighter bounding boxes on objects.
DOI：
10.1109/tpami.2016.2577031
发表时间：
2017-06-01
影响因子：
23.6
作者：
Ren, Shaoqing;He, Kaiming;Sun, Jian
通讯作者：
Sun, Jian