论文标题
寻找变化？掷骰子并要求注意
Looking for change? Roll the Dice and demand Attention
论文作者
论文摘要
更改检测，即从一组双阶的共注册图像中的某些兴趣类别的更改的每个像素识别，是遥感领域的基本任务。由于输入图像中不同时间出现的无关变化形式，它仍然具有挑战性。在这里，我们提出了一个可靠的深度学习框架，用于在非常高分辨率的空中图像中进行语义变化检测的任务。我们的框架包括一个新的损失功能，新的注意力模块，新功能提取构建块以及针对语义变化检测任务量身定制的新骨干架构。具体而言，我们定义了一种新形式的集合相似性，这是基于对骰子系数变体的迭代评估。我们使用此相似性度量来定义新的损失函数以及新的空间和通道卷积注意层（分形）。专门为视觉任务设计的新注意力层是有效的，因此适合在所有深度卷积网络中使用。基于这些，我们介绍了两个新的有效的独立特征提取卷积单元。我们验证CIFAR10参考数据上这些特征提取构建块的性能，并将结果与标准的Resnet模块进行比较。此外，我们引入了一种新的编码器/解码器方案，该方案是一种网络宏观论，该方案是针对变更检测的任务量身定制的。我们的网络远离用于识别变化的特征层减法概念。我们通过在两个建筑物更改检测数据集上表现出出色的性能和实现最先进的得分（F1和IOU）的出色表现（F1和交叉点）来验证我们的方法，即Levircd（F1：0.918，IOU：0.848）和WHU（F1：0.938，IOU：0.882）数据集合。
Change detection, i.e. identification per pixel of changes for some classes of interest from a set of bi-temporal co-registered images, is a fundamental task in the field of remote sensing. It remains challenging due to unrelated forms of change that appear at different times in input images. Here, we propose a reliable deep learning framework for the task of semantic change detection in very high-resolution aerial images. Our framework consists of a new loss function, new attention modules, new feature extraction building blocks, and a new backbone architecture that is tailored for the task of semantic change detection. Specifically, we define a new form of set similarity, that is based on an iterative evaluation of a variant of the Dice coefficient. We use this similarity metric to define a new loss function as well as a new spatial and channel convolution Attention layer (the FracTAL). The new attention layer, designed specifically for vision tasks, is memory efficient, thus suitable for use in all levels of deep convolutional networks. Based on these, we introduce two new efficient self-contained feature extraction convolution units. We validate the performance of these feature extraction building blocks on the CIFAR10 reference data and compare the results with standard ResNet modules. Further, we introduce a new encoder/decoder scheme, a network macro-topology, that is tailored for the task of change detection. Our network moves away from any notion of subtraction of feature layers for identifying change. We validate our approach by showing excellent performance and achieving state of the art score (F1 and Intersection over Union-hereafter IoU) on two building change detection datasets, namely, the LEVIRCD (F1: 0.918, IoU: 0.848) and the WHU (F1: 0.938, IoU: 0.882) datasets.
