MtSCCD：面向深度学习的土地利用场景分类与变化检测数据集
MtSCCD： Land-use scene classification and change-detection dataset for deep learning
- 2024年28卷第2期 页码：321-333
纸质出版日期： 2024-02-07
DOI： 10.11834/jrs.20243210
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摘要
利用遥感影像识别土地利用类型及监测其变化情况在城市规划和土地利用优化等领域发挥着重要作用。当前，相关数据集存在样本量少、类别划分不合理、数据不开源等局限，难以满足样本驱动的深度学习遥感信息提取范式的需求。本文构建了一个面向深度学习的大规模场景分类与变化检测数据集MtSCCD（Multi-temporal Scene Classification and Change Detection）。该数据集包括MtSCCD_LUSC（MtSCCD Land Use Scene Classification）和MtSCCD_LUCD（MtSCCD Land Use Change Detection）两个子数据集，分别用于土地利用场景分类与变化检测任务。该数据集具有以下特点：（1）MtSCCD是目前规模最大的公开的土地利用类型识别与检测数据集，包含10种土地利用类型共65548幅图像，并且样本覆盖中国5个城市的中心区域；（2）由于MtSCCD数据集根据城市划分训练集、验证集以及测试集，对于新增的城市土地利用数据，可以根据需求划分为训练集与验证集或测试集，因此可扩展性较高；（3）MtSCCD数据集中测试集与训练集的样本来自不同的城市，因此符合实际业务需求，且能够验证模型的泛化性能。基于MtSCCD_LUSC和MtSCCD_LUCD两个子数据集，本文评估了多个深度学习网络的分类与变化检测效果，为后续的相关研究提供了参考。
Abstract
Land-Use Scene Classification and change Detection (LUSCD) aim to recognize land-use types and monitor their changes by using Remote-Sensing (RS) images
which play an important role in urban planning and land-use optimization. In the era of RS big data
conventional hand-crafted feature-based methods are infeasible for LUSCD because the extracted features are not sufficiently discriminative for RS images with high complexity. As a novel data-driven paradigm for information extraction from RS images
deep learning provides a new solution for LUSCD. However
the existing publicly available datasets have limited samples and is thus unable to train a successful deep-learning model. Therefore
it has great significance in constructing an open and large-scale LUSCD benchmark.
To advance the progress of LUSCD using deep-learning methods
this paper releases a large-scale scene classification and change-detection dataset termed Multi-temporal Scene Classification and Change Detection (MtSCCD). The RGB images in MtSCCD are cropped from large-size high-resolution RS images captured from the central areas of five China cities
namely
Hangzhou
Shanghai
Wuhan
Nanjing
and Hefei. The size of the cropped images is 300×300 pixels with the spatial resolution of around 1 m. MtSCCD has 10 land use classes
which are residential land
public service and commercial land
educational land
industrial land
transportation land
agricultural land
water body
green space
woodland
and woodland. Based on the cropped land-use images in MtSCCD
this paper constructs two sub-datasets termed MtSCCD_LUSC (MtSCCD Land Use Scene Classification) and MtSCCD_LUCD (MtSCCD Land Use Change Detection) for land-use scene classification (LUSC) and land-use change detection (LUCD)
respectively. MtSCCD dataset has the following characteristics. (1) It is currently the largest publicly available LUSCD dataset
and both of the two sub-datasets (i.e.
MtSCCD_LUSC and MtSCCD_LUCD) have 65548 images in total. (2) The images in MtSCCD are split into training set
validation set
and testing set according to the five cities. For example
images from three of the five cities are randomly split into training and validation set
whereas the rest remain to be the testing set. Therefore
MtSCCD has high extensibility
i.e.
it can be easily extended to be a larger dataset. (3) For a deep-learning model
the training set and testing set are categorized from different cities
so it is beneficial to demonstrate the model’s generalization ability. (4) MtSCCD has high intra-class diversity
making it a challenging dataset.
Based on MtSCCD_LUSC and MtSCCD_LUCD
this paper evaluates several deep-learning feature-based methods for LUSC and LUCD. Specifically
AlexNet
VGG networks (i.e.
VGG16 and VGG19)
GoogLeNet
and ResNet networks (i.e.
ResNet18
ResNet50
and ResNet101) are selected to extract deep-learning features that are then fed into SVM for LUSC. We also evaluate DenseNet
EfficientNet
SENet
ViT
and SwinT for LUSC. Two kinds of LUCD approaches including conventional classification-based methods and current similarity-based methods have been evaluated. Experimental results show that the highest overall accuracy of MtSCCD_LUSC dataset is around 76%
indicating much room for improvement. Regarding LUCD
similarity-based methods particularly similarity learning-based ones outperform classification-based methods by a significant margin
providing a promising research direction for LUCD.
This paper presents the currently largest scene classification and change-detection dataset MtSCCD based on high-resolution RS images of the central area of five China cities. MtSCCD contains two subsets MtSCCD_LUSC and MtSCCD_LUCD. Both had 10 land-use types and 65548 images in total. Based on the two sub-datasets
this paper evaluates the performance of several deep networks for scene classification and change detection
expecting to provide baseline results for related researchers. We hope that the MtSCCD dataset can promote this progress in land-use type recognition and monitoring.
关键词
土地利用场景分类变化检测数据集信息提取特征提取深度学习卷积神经网络
Keywords
land usescene classificationchange detectiondatasetinformation extractionfeature extractiondeep learningconvolutional neural network
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相关作者
刘渊 中国科学院西安光学精密机械研究所 光谱成像技术实验室;中国科学院大学
郑向涛 福州大学 物理与信息工程学院
卢孝强 福州大学 物理与信息工程学院
王威 长沙理工大学 计算机与通信工程学院
李希杰 长沙理工大学 计算机与通信工程学院
王新 长沙理工大学 计算机与通信工程学院
汤玉奇 中南大学 有色金属成矿预测与地质环境监测教育部重点实验室;中南大学 地球科学与信息物理学院
林泽锋 中南大学 地球科学与信息物理学院
相关机构
中国科学院西安光学精密机械研究所 光谱成像技术实验室
中国科学院大学
福州大学 物理与信息工程学院
长沙理工大学 计算机与通信工程学院
中南大学 有色金属成矿预测与地质环境监测教育部重点实验室
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