Machine Learning with Remote Sensing Image Data Sets

Abstract

Computer vision, as a part of machine learning, gains significant attention from researches nowadays. Aerial scene classification is a prominent chapter of computer vision with a vast application: military, surveillance and security, environment monitoring, detection of geospatial objects, etc. There are several publicly available remote sensing image datasets, which enable the deployment of various aerial scene classification algorithms. In our article, we use transfer learning from pre-trained deep Convolutional Neural Networks (CNN) within remote sensing image classification. Neural networks utilized in our research are high-dimensional previously trained CNN on ImageNet dataset. Transfer learning can be performed through feature extraction or fine-tuning. We proposed a two-stream feature extraction method and afterward image classification through a handcrafted classifier. Fine-tuning was performed with adaptive learning rates and a regularization method label smoothing. The proposed transfer learning techniques were validated on two remote sensing image datasets: WHU RS datasets and AID dataset. Our proposed method obtained competitive results compared to state-of-the-art methods. Povzetek: Metoda prenesenega učenja je uporabljena za analizo posnetkov iz zraka na nekaj referenčnih bazah.

GF-UNet:A Cropland Extraction Method Based on Attention Gate and Adaptive Feature Fusion

“…Timely and accurate access to agricultural information is of great importance for ensuring national food security and promoting sustainable development of the national economy [2]. Remote sensing technology, with its wide coverage and timely imaging capabilities, enables rapid updates of agricultural information [3,4].…”