Semantic Segmentation in Remote Sensing Images
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Abstract
Semantic segmentation of remote sensing images plays a crucial role in understanding and analyzing geospatial data. This research explores the application of state-of-the-art deep learning techniques for semantic segmentation in remote sensing imagery. By accurately classifying each pixel in the image, this approach enables the identification and delineation of various land cover and land use categories.
This paper reviews the evolution of semantic segmentation methods and discusses their adaptation to the unique challenges posed by remote sensing data. We address issues such as varying spatial resolutions, diverse sensor types, and limited training data availability. Additionally, we investigate the integration of multispectral and hyperspectral information to improve the accuracy of semantic segmentation.
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