The accuracy of remote sensing image segmentation and classification is known to dramatically decrease when the source target images are from different sources; while deep learning-based models have boosted performance, they only effective trained with a large number labeled that similar images. In this article, we propose generative adversarial network (GAN) based domain adaptation for land cover using new enormously GANs, fully aligned in space, feature output space domains two stages via...

In this paper, we introduce a new building dataset and propose novel domain generalization method to facilitate the development of extraction from high-resolution remote sensing images. The problem with current datasets involves that they lack diversity train practical learning model good ability, quality labels is unsatisfactory. To address these issues, built diverse, large-scale, high-quality named WHU-Mix dataset, which more practice-oriented. consists training/validation set containing...

(2020). Evaluating generative adversarial networks based image-level domain transfer for multi-source remote sensing image segmentation and object detection. International Journal of Remote Sensing: Vol. 41, No. 19, pp. 7343-7367.

Spatiotemporal fusion of remote sensing data is essential for generating high spatial and temporal resolution by taking advantage imageries. At present, the Spatial Temporal Adaptive Reflectance Fusion Model (STARFM) one most widely used spatiotemporal technologies data. However, quality acquired STARFM depends on information from homogeneous land cover patches at MODIS (Moderate Resolution Imaging Spectroradiometer) imagery, estimation accuracy degrades in highly fragmentated heterogeneous...

Although the emergence of deep learning has improved performance automatic building extraction, there is still a long way to go before it can completely replace labour-intensive manual delineation contours. To narrow gap between extraction results methods and manual-level annotations, this paper introduces an interactive semantic segmentation framework that uses clicks as information guide process towards annotation level. In our framework, we first use network for coarse from high...

Extracting building contours from remote sensing imagery is a significant challenge due to buildings' complex and diverse shapes, occlusions, noise. Existing methods often struggle with irregular contours, rounded corners, redundancy points, necessitating extensive post-processing produce regular polygonal contours. To address these challenges, we introduce novel, streamlined pipeline that generates without post-processing. Our approach begins the segmentation of generic geometric primitives...

Abstract. Content-based remote sensing image retrieval refers to searching interested images from a dataset that are similar query via extracting features (contents) and comparing their similarity. In this work, we come up with lightweight network structure, which call the joint spatial radiometric transformer, is composed of three modules: parameter generation (PGN), conversion conversion. The PGN module learns specific transformation parameters input guide subsequent processes. With these...

In this paper, we introduce a new building dataset and propose novel domain generalization method to facilitate the development of extraction from high-resolution remote sensing images. The problem with current datasets involves that they lack diversity, quality labels is unsatisfactory, are hardly used train model good ability, so as properly evaluate real performance in practical scenes. To address these issues, built diverse, large-scale, high-quality named WHU-Mix dataset, which more...

Deep learning based methods have significantly boosted the study of automatic building extraction from remote sensing images. However, delineating vectorized and regular contours like a human does remains very challenging, due to difficulty methodology, diversity structures, imperfect imaging conditions. In this paper, we propose first end-to-end learnable contour framework, named BuildMapper, which can directly efficiently delineate polygons just as does. BuildMapper consists two main...