Multi-temporal Remote Sensing Image Change Detection Method Based on Spatial Constraint Chi-Square Transform
A technology of spatial constraint and change detection, applied in image analysis, image enhancement, image data processing and other directions, can solve the problems of complex background information and noise interference, and achieve the effect of good versatility, reliable results and robustness
Active Publication Date: 2019-01-15
HOHAI UNIV
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[0005] In order to solve the technical problems raised by the above-mentioned background technology, the present invention aims to provide a multi-temporal remote sensing image change detection method based on the spatially constrained chi-square transform, which uses the spatially constrained chi-square transform combined with the adaptive confidence level change detection method to overcome the Complex background information and serious noise interference in multi-temporal and multi-spectral remote sensing images
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[0051] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0052] like figure 1 As shown, the multi-temporal remote sensing image change detection method based on chi-square transformation with spatial constraints, the specific steps are as follows:
[0053] Step 1: Input two high-resolution optical remote sensing images of the same area and different time phases, denoted as X 1 and x 2 .
[0054] Step 2: For X 1 and x 2 Image registration is divided into two steps: rough correction and fine correction:
[0055] For geometric rough correction, use the relevant functions in ENVI4.8 software to realize, the specific operation steps are:
[0056] (1) Display the reference image and the image to be corrected;
[0057] (2) Collect ground control points GCPs, GCPs should be evenly distributed in the entire image, and the number of GCPs should be at least greater than or equal to 9;
[0058] (3) C...
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The invention discloses a multi-temporal remote sensing image change detection method based on chi-square transformation of spatial constraints. First, the difference image of the multi-temporal multi-spectral image is calculated, and on this basis, the CST value of each point on the image is calculated. Get the threshold, get the preliminary change detection result, and then perform mode filtering (embedding spatial information) on the preliminary result, and recalculate the mean value and variance matrix of the non-changing area according to the filtering result. Repeat the above process until there is no change in the detection result. The selection of the confidence level in the detection process is selected through the pseudo training sample set, and the final change detection result is obtained on the basis of the optimal confidence level. The invention solves the problems of complex background information and serious noise interference of multi-temporal multi-spectrum remote sensing images.
Description
technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a multi-temporal remote sensing image change detection method based on spatially constrained chi-square transformation. Background technique [0002] With the continuous accumulation of multi-temporal remote sensing data and the successive establishment of spatial databases, how to extract and detect change information from these remote sensing data has become an important research topic in remote sensing science and geographic information science. According to remote sensing images of different time phases in the same area, dynamic information such as cities and environments can be extracted to provide scientific decision-making basis for resource management and planning, environmental protection and other departments. [0003] Change detection of remote sensing images is to quantitatively analyze and determine the characteristics and process ...
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IPC IPC(8): G06T7/00G06T7/30G06T7/11G06T7/136G06T5/00
CPCG06T7/0002G06T2207/10032G06T2207/20081G06T5/80
Inventor 陈哲石爱业孔伟为
Owner HOHAI UNIV