The main reason is that the processing of remote sensing images is very complicated. However, none of them support labeling multispectral remote sensing images. Advanced commercial annotation tools also integrate project management, task collaboration, and deep learning functions. These tools are fully functional and support the use of boxes, lines, dots, polygons, and bitmap brushes to label images and videos. A brief comparison between these annotation tools is shown in Table 1. In the field of computer vision, representative tools and platforms include Labelme, LabelImg, Computer Vision Annotation Tool (CVAT), RectLabel and Labelbox. At present, more and more researchers and institutions begin to pay attention to how to design and implement high-efficiency annotation methods and tools for images, video, text, and speech. ![]() Due to the dependence of deep learning technology on massive samples, making samples is always an important task that consumes a lot of manpower and time and relies on expert knowledge. The quality and quantity of samples directly affect the accuracy and generalization ability of the model. Samples are the foundation of deep learning. In addition, deep learning is also used for remote sensing image fusion and image registration. regarded road extraction from remote sensing images as a semantic segmentation task and used boosting segmentation based on D-LinkNet to enhance the robustness of the model. built an end-to-end aircraft detection framework using VGG16 and transfer learning. used AlexNet for complex wetland classification, and the results show that the CNN is better than the random forest. Compared with traditional image processing methods, deep learning has achieved state-of-the-art success. At the same time, more and more researchers use technologies similar to convolutional neural networks (CNNs) to process and analyze remote sensing images. With the development of artificial intelligence, deep learning has achieved great success in image classification, object detection, and semantic segmentation tasks in the field of computer vision. The experimental results show that LabelRS can produce deep learning samples with remote sensing images automatically and efficiently. ![]() To evaluate the reliability of LabelRS, we used its three sub-tools to make semantic segmentation, object detection and image classification samples, respectively. In addition, the attached geographic information helps to achieve seamless conversion between natural samples, and geographic samples. We have designed and built in multiple band stretching, image resampling, and gray level transformation algorithms for LabelRS to deal with the high spectral remote sensing images. LabelRS solves the problem of size diversity of the targets in remote sensing images through sliding windows. In this work, we proposed a feature merging strategy that enables LabelRS to automatically adapt to both sparsely distributed and densely distributed scenarios. Therefore, we developed an add-in (LabelRS) based on ArcGIS to help researchers make their own deep learning samples in a simple way. However, the lack of tools for making deep learning samples with remote sensing images is a problem, so researchers have to rely on a small amount of existing public data sets that may influence the learning effect. ![]() Deep learning technology has achieved great success in the field of remote sensing processing.
0 Comments
Leave a Reply. |