Abstract
As demonstrated in prior studies, InSAR holds great potential for land cover classification, especially considering its wide coverage and transparency to climatic conditions. In addition to features such as backscattering coefficient and phase coherence, the temporal migration in InSAR signatures provides information that is capable of discriminating types of land cover in target area. The exploitation of InSAR signatures was expected to provide merits to trace land cover change in extensive areas; however, the extraction of suitable features from InSAR signatures was a challenging task. Combining time series amplitudes and phase coherences through linear and nonlinear compressions, we showed that the InSAR signatures could be extracted and transformed into reliable classification features for interpreting land cover types. The prototype was tested in mountainous areas that were covered with a dense vegetation canopy. It was demonstrated that InSAR time series signature analyses reliably identified land cover types and also recognized tracing of temporal land cover change. Based on the robustness of the developed scheme against the temporal noise components and the availability of advanced spatial and temporal resolution SAR data, classification of finer land cover types and identification of stable scatterers for InSAR time series techniques can be expected. The advanced spatial and temporal resolution of future SAR assets combining the scheme in this study can be applicable for various important applications including global land cover changes monitoring.
Highlights
Extracting land cover information through optical imagery analysis has been one of the primary remote sensing applications
The initial classification results derived from conventional maximum likelihood (ML) classifiers and interferometric SAR (InSAR) features set by principal component analysis (PCA) analysis are shown in Figure 2, in which classification maps of HH1–HH3 packets and a hybrid by PCA analysis are shown in Figure 2, in which classification maps of HH1–HH3 packets and a packet are illustrated
The initial classification results derived from conventional ML classifiers and InSAR features set lines (1100 m, 1700 m and 2000 m) presented in Figure 3 fit well with our land cover classification types, by PCA analysis are shown in Figure 2, in which classification maps of HH1–HH3 packets and a especially transition between bare field and conifer, and conifer and open/mixed forest
Summary
Extracting land cover information through optical imagery analysis has been one of the primary remote sensing applications. Given recent improvements in electro-optical sensor technology with machine vision algorithms, contemporary in-orbital images with high temporal resolution have become valuable sources for effective extractions of land cover information through data mining techniques. Three feature spaces were established for the exploitation of InSAR signatures: (1) phase coherence; (2) backscattering coefficients; (3) the difference in backscattering coefficients This strategy was implemented based on phase coherence changes over different types of land cover. Characteristics of SAR backscattering coefficients are useful in compensating phase coherence for land cover classification. These characteristics include weak signatures in water, weak to medium signatures according to the density of vegetation, and strong signatures in artificial structures
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