Abstract
Abstract: Semantic segmentation of remote sensing images is crucial for interpreting these large, rich in information scenes and our study introduces a new method for segmenting remote sensing imagery (RSI) when faced with limited training data and imbalanced classes. Our approach utilizes a unique potential function that merges information from both super pixel segmentation and edge detection. This combination allows the model to effectively analyze features at various scales and reduce the influence of potential errors in super pixel segmentation. Furthermore, the inclusion of edge details extracted via the Sketch token algorithm refines object boundaries, yielding more accurate segmentation results. This work offers a promising solution for achieving reliable interpretation of RSIs in scenarios with limited training data.
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