An Adaptive DeepLabv3+ for Semantic Segmentation of Aerial Images Using Improved Golden Eagle Optimization Algorithm

Abstract

Semantic segmentation is a significant task in the field of Remote Sensing and Computer Vision. DeepLabV3+ is a convolutional neural network architecture that excels in the task of semantic segmentation, which involves assigning a class label to each pixel in an input image. This Paper proposes an Adaptive Deeplabv3+ model for semantic segmentation of Aerial Images, which combines Deeplabv3+ with the Improved Golden Eagle Optimization Algorithm (IGEO), to solve imprecise target segmentation and poor border segmentation accuracy. To enhance the quality of segmentation, Adaptive DeepLabV3+ employs atrous spatial pyramid pooling (ASPP) with multiple dilation rates in the encoder and allows the model to capture multi-scale context information efficiently, enabling it to distinguish between objects with varying scales. The proposed model effectively segmented the aerial images by optimizing the hyper-parameters such as hidden neuron count and learning rate. The suggested model achieved 98.46% accuracy, 96.32% correlation, 96.48% precision and 98.36% dice coefficient within the computation time of 136.8912 and 147.2684 seconds for dataset 1 and 2 respectively. Therefore the evolutional outcomes of the proposed model show significantly improved than the state-of-the-art techniques.

Keywords

• Semantic segmentation
• adaptive DeepLabv3+
• improved golden eagle optimization
• hidden neuron count
• learning rate