Abstract
In the era of big data, where massive amounts of remotely sensed imagery can be obtained from various satellites accompanied by the rapid change in the surface of the Earth, new techniques for large-scale change detection are necessary to facilitate timely and effective human understanding of natural and human-made phenomena. In this research, we propose a chip-based change detection method that is enabled by using deep neural networks to extract visual features. These features are transformed into deep orthogonal visual features that are then clustered based on land cover characteristics. The resulting chip cluster memberships allow arbitrary level-of-detail change analysis that can also support irregular geospatial extent based agglomerations. The proposed methods naturally support cross-resolution temporal scenes without requiring normalization of the pixel resolution across scenes and without requiring pixel-level coregistration processes. This is achieved with configurable spatial locality comparisons between years, where the aperture of a unit of measure can be a single chip, a small neighborhood of chips, or a large irregular geospatial region. The performance of our proposed method has been validated using various quantitative and statistical metrics in addition to presenting the visual geo-maps and the percentage of the change. The results show that our proposed method efficiently detected the change from a large scale area.
Highlights
The remote sensing field has seen rapid development in recent years, which has resulted in a deluge of overhead high-resolution imagery from airborne and spaceborne remote sensing platforms
We propose a new approach for change detection, which is a chip-based approach facilitating multitype CD
The cluster analysis could serve as the exploratory step to the change detection process since it is important to know the division of the study area based on the type of the cluster to be able to compare and conclude the multi-type change
Summary
The remote sensing field has seen rapid development in recent years, which has resulted in a deluge of overhead high-resolution imagery from airborne and spaceborne remote sensing platforms This huge data provides plentiful new sources, it far exceeds the capacity of humans to visually analyze and demands highly automatic methods. This data volume has seen continual enhancements in temporal and spatial resolution of remote sensing information [1,2,3,4,5,6] These circumstances necessitate the development of new human–machine teaming approaches for visual imagery analysis workflows, techniques that leverage state-of-the-art computer vision and machine learning. It is a key technology that has been investigated in many fields such as disaster management, deforestation, and urbanization [7,12]
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