Building detection method based on pixel and region segmentation decision fusion

Abstract

The invention discloses a building detection method based on pixel and region segmentation decision fusion. The building detection method comprises the following steps: respectively constructing a residual structure introduced pixel segmentation model and a feature pyramid network introduced region-based double segmentation model; generating a training sample set and a test sample set from the optical remote sensing data set; preprocessing the images in the training set samples; training a pixel segmentation model by using the mixed supervision loss added with the Dice loss and the cross entropy loss; inputting the test sample set into the trained double-segmentation network, and respectively outputting prediction results of the test sample set; and fusing the prediction result of the double-segmentation network according to the decision scheme, outputting the final detection result of the test sample set, and completing the detection. The method pays attention to the spatial consistency of the large-scale building, reserves the multi-scale features of the small-scale building, guarantees the richness of the features of the building, and improves the building detection accuracy.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a building detection method based on pixel and region segmentation decision fusion, which can be applied to building detection and recognition in optical remote sensing images. Background technique [0002] At present, countries around the world have launched remote sensing satellites with various functions. The spatial resolution of remote sensing images has achieved sub-meter breakthroughs. For example, the images taken by the GeoEye series satellites of the United States, the SPOT-6 / 7 series satellites of France, and the Gaofen series satellites of China contain rich ground features. It can describe the surface conditions in detail. The huge reserve of remote sensing data guarantees and meets the requirements of geographic surveying and mapping, marine and climate meteorology, and urban traffic management. Buildings, as one of the most imp...

AI Technical Summary

Problems solved by technology

It solves the shortcomings of a single region-based segmentation model that focuses on small buildings and a pixel-based segmentation model that focuses on large buildings, and is more suitable for building detection scenarios with complex background texture information and large differences in building scales

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