An Adaptive DeepLabv3+ for Semantic Segmentation of Aerial Images Using Improved Golden Eagle Optimization Algorithm
P. Anilkumar, P. Venugopal, Praveen Kumar Reddy Maddikunta, Thippa Reddy Gadekallu, Amal Al‐Rasheed, Mohamed Abbas, Ben Othman Soufiene
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.