An Efficient Method of Detection and Recognition in Remote Sensing Image Based on multi-angle Region of Interests

Presently, deep learning technology has been widely used in the field of image recognition. However, it mainly aims at the recognition and detection of ordinary pictures and common scenes. As special images, remote sensing images have different shooting angles and shooting methods compared with ordinary ones, which makes remote sensing images play an irreplaceable role in some areas. In this paper, based on a deep convolution neural network for providing multi-level information of images and combines RPN (Region Proposal Network) for generating multi-angle ROIs (Region of Interest), a new model for object detection and recognition in remote sensing images is proposed. In the experiment, it achieves better results than traditional ways, which demonstrate that the model proposed here would have a huge potential application in remote sensing image recognition.Comment: 4 pages, 3 figure

Land Use Information Quick Mapping Based on UAV Low- Altitude Remote Sensing Technology and Transfer Learning

Obtaining surface spatio-temporal data rapidly, automatically and accurately is an important issue in agriculture informationization and intellectualization. Samples obtained by conventional manual visual interpretation are difficult to adapt the demands of land resources information extraction. Low altitude remote sensing technology as a kind of emerging technology for earth observation in recent years. Based on this, spatio-temporal data mining technology was introduced, and knowledge transfer learning mechanism was used, a novel landuse information classification method based on knowledge transfer learning (KTLC) was proposed. Firstly, new image was segmented by improved mean shift algorithm to obtain image objects. Secondly, the vector boundary of the objects and former historical landuse thematic map were matched and nested, invariant objects were obtained through overlay analysis, and purification of invariant object was finished by spectral and spatial information threshold filtering. The historical features category knowledge of thematic map was transferred to the new image objects. Finally, current images classification mapping was completed based on decision tree, and landuse classification mapping results were completed by the KTLC and eCognition for landuse information mapping classification (EC). The experimental results showed that KTLC could obtain accuracies equivalent to EC, and also outperforms EC in terms of efficiency

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure