Change detection on surface of earth plays an important role in global-scale pattern of climate and biogeochemistry of the world, which helps to comprehend the connections and associations between human and nature. Remote Sensing and Geographic Information Systems can possibly provide accurate data in regards to land use and land cover changes. However, pixel-based change detection methods are limited in suppressing outliers and noise; they often fail to process remote sensing images with high spatial-/spectral-resolution. To conquer these drawbacks, a superpixel-level change detection and analysis method is proposed in this paper. Superpixels are the atomic regions gathering pixels with similar property, which will be more efficient and robust than pixels. Deep neural network is a powerful feature learning and classification tool, it can represent superpixel abstractly and classify them robustly. The learning progress of deep architectures includes unsupervised sample selection and supervised feature learning, unsupervised progress aims at selecting training samples for deep neural network, supervised progress aims at learning the representation of superpixels and fine-tuning the whole network to finish classification. Experimental results on multi-temporal images have demonstrated that the proposed approach can handle the task of change detection and analysis effectively and accurately.

地球表面的变化检测在全球气候和生物地球化学的全球格局中起着重要作用，这有助于理解人与自然之间的联系和联系。遥感和地理信息系统可能会提供有关土地使用和土地覆盖变化的准确数据。然而，基于像素的变化检测方法在抑制异常值和噪声方面受到限制。他们通常无法处理具有高空间/光谱分辨率的遥感影像。为了克服这些缺陷，本文提出了一种超像素级变化检测与分析方法。超像素是聚集具有相似属性的像素的原子区域，它将比像素更有效和更坚固。深度神经网络是功能强大的功能学习和分类工具，它可以抽象地表示超像素并对其进行稳健的分类。深度架构的学习进度包括无监督样本选择和监督特征学习，无监督进度旨在选择用于深度神经网络的训练样本，有监督进度旨在学习超像素的表示并微调整个网络以完成分类。多时相图像的实验结果表明，该方法能够有效，准确地处理变化检测和分析任务。监督进度旨在学习超像素的表示形式，并对整个网络进行微调以完成分类。多时相图像的实验结果表明，该方法能够有效，准确地处理变化检测和分析任务。监督进度旨在学习超像素的表示形式，并对整个网络进行微调以完成分类。多时相图像的实验结果表明，该方法能够有效，准确地处理变化检测和分析任务。