Abstract
GaoFen-4 (GF-4) imagery has very potential in terms of emergency response due to its gazing mode. However, only poor geometric accuracy can be obtained using the rational polynomial coefficient (RPC) parameters provided, making ground control points (GCPs) necessary for emergency response. However, selecting GCPs is traditionally time-consuming, labor-intensive, and not fully reliable. This is mainly due to the facts that (1) manual GCP selection is time-consuming and cumbersome because of too many human interventions, especially for the first few GCPs; (2) typically, GF-4 gives planar array imagery acquired at rather large tilt angles, and the distortion introduces problems in image matching; (3) reference data will not always be available, especially under emergency circumstances. This paper provides a novel emergency georeferencing framework for GF-4 Level 1 imagery. The key feature is GCP prediction based on dynamic RPC refinement, which is able to predict even the first GCP and the prediction will be dynamically refined as the selection goes on. This is done by two techniques: (1) GCP prediction using RPC parameters and (2) dynamic RPC refinement using as few as only one GCP. Besides, online map services are also adopted to automatically provide reference data. Experimental results show that (1) GCP predictions improve using dynamic RPC refinement; (2) GCP selection becomes more efficient with GCP prediction; (3) the integration of online map services constitutes a good example for emergency response.
Highlights
GF-4 (GF is a Chinese abbreviation for high-resolution) is China’s first geostationary earth-observation satellite [1,2] enabled with gazing mode, when continuous imagery series of the same area can be captured at intervals of seconds, providing an ideal observation approach for emergency response when facing disasters such as flood
000A0s9has bee1n20d.e3m33o6n0strated16in.6S9e6c2t2ion 3, th1e20p.r3o5p2o0s4ed fram16e.w73o3r5k1is able1to20p.3ro3v3i7d7e GCP1p6r.e6d9i5c9t0ion thr0o0u0g1h0out the12e0n.7ti8r2e3s2electio1n2p.8r0o5c7e8ss, whi1c2h0.w77il7l6g3reatly 1im2.8p3r8o3v8e effici1e2n0c.y7.8W33e6actual1ly2.8d0i7d2n1ot carry out any quantitative evaluations about howRMmSuEc:h3t8i9m4e.4wmas saved either bRyMuSsEin:g86o.n7lmine maps services as reference data or by using the combination of ground control points (GCPs) prediction and dynamic rational polynomial coefficient (RPC) refinement Compared with the original RPC parameters, a new set of RPC parameters given by the proposed framework decreased the RMSE of the georeferencing from 3894.4 to 86.7 m
An emergency georeferencing framework for GF-4 imagery based on RPC refinement and GCPs selection assisted by integrated online map services is proposed in the paper
Summary
GF-4 (GF is a Chinese abbreviation for high-resolution) is China’s first geostationary earth-observation satellite [1,2] enabled with gazing mode, when continuous imagery series of the same area can be captured at intervals of seconds, providing an ideal observation approach for emergency response when facing disasters such as flood. While for manual selection methods, the low efficiency of the manual selection is a major obstacle: typically, to be georeferenced, operators have to search the GCPs blindly within the whole reference image or the Level 1 imagery, which means substantial amounts of manpower and time are required [8] This is mainly because of the lack of GCP prediction, not to mention a prediction that improves as selection goes on, and is true even when using online map services as reference data. A novel emergency georeferencing framework for GF-4 imagery based on RPC refinement and GCP selection assisted by integrated online map services is proposed to solve this. A novel emergency georeferencing framework for GF-4 imagery based on RPC refinement and GCP selection assisted by integrated online map services is proposed to solve this pprroobblleemm. (4) Derive a new set of RPC parameters using the latitude/longitude of all the junction points
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