Practical framework and methodology for high-performance intelligent invariant detection in remote sensing imagery

doi:10.11947/j.AGCS.2024.20230405

Abstract

Abstract:

In addressing the challenges posed by sample category imbalance, limited algorithm applicability, and inadequate knowledge application inherent in traditional change detection techniques, we propose a novel framework for high-reliability, intelligent invariant detection of land classes in remote sensing imagery. This framework employs advanced algorithms to precisely extract stable invariant areas that are typically irrelevant to various tasks, thereby reducing the operational footprint and boosting productivity in practical settings. Commencing with data preprocessing, a sample library tailored to the specifics of invariant detection is developed. Additionally, we introduce a method for invariant detection that utilizes prior information to guide the discrimination between global and local pseudo-changes. This approach leads to the creation of a gridded invariant mask and the introduction of two object-level metrics—compression accuracy and compression range—to assess the framework's performance in terms of accuracy and efficiency. Empirical validation across multiple national regions confirms that this framework not only minimizes the workload associated with manual visual interpretation but also significantly improves the efficiency of data extraction, thus offering a groundbreaking solution for extracting change information from remote sensing data in real-world scenarios.

Key words: change detection, changeless detection, artificial intelligence, remote sensing imagery

CLC Number:

P237

Xiaogang NING, Hanchao ZHANG, Ruiqian ZHANG. Practical framework and methodology for high-performance intelligent invariant detection in remote sensing imagery[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(6): 1098-1112.

Figures/Tables 10

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数据集	分辨率/m	变化类型	数据来源	分布地区
SZTAKI^[44]	1.5	新建城区、建筑作业、大批树木种植、耕地变化等	航空数据+谷歌地球	匈牙利佩斯州绍道
ABCD^[45]	0.4	建筑物是否被冲走	航空数据	日本东北地区
WHU building CDD^[46]	0.075	只关注建筑物变化	航空数据	克赖斯特彻奇
GZCD^[47]	0.55	只标记建筑物变化	谷歌地球	广州
Lebedev-CD^[48]	0.03~1	考虑不同大小对象变化（建筑物、道路、森林、汽车、树木、坦克等）	谷歌地球	—
LEVIR-CD^[49]	0.5	只关注建筑相关变化	谷歌地球	美国得克萨斯州
DSIFN-CD^[50]	2	关注土地覆盖对象变化（道路、建筑物、农田、水体等地物）	谷歌地球	北京、成都、深圳、重庆、武汉、西安
SYSU-CD^[51]	0.5	新建城市建筑、郊区扩张、施工前的基础工作、植被变化、道路扩建、海上建设等	航空数据	香港
LIM-CD^[52]	0.5~2	新增建设用地变化（如住宅建筑，工业、商业建设，公共、交通设施建设），特殊用途建筑（水利、园林、绿化等）	镶嵌影像（15颗卫星）	中国10个地形各异的省区市

行政区名称	真实变化图斑个数	不变区域掩膜外的变化图斑个数	压盖准度/（%）	压盖幅度/（%）
北京市门头沟区	65	61	93.85	93.54
河北省石家庄市深泽县	65	61	93.85	85.41
山西省临汾市侯马市	33	31	93.94	73.82
内蒙古锡林郭勒盟正镶白旗	98	93	94.90	97.75
吉林省白山市浑江区	85	72	84.71	94.55
江苏省扬州市高邮市	185	165	89.19	93.44
浙江省杭州市桐庐县	153	141	92.16	92.31
浙江省宁波市象山县	268	223	83.21	89.25
安徽省合肥市蜀山区	260	238	91.54	77.60
安徽省六安市金安区	266	235	88.35	93.53
福建省泉州市泉港区	50	48	96.00	70.59
河南省新乡市获嘉县	76	72	94.74	83.91
湖南省长沙市雨花区	78	75	96.15	88.90
湖南省株洲市天元区	69	69	100.00	80.24
湖南省湘西土家族苗族自治州花垣县	107	92	85.98	87.34
平均			91.90	86.81