In recent years, high-resolution remote sensing image segmentation has become a key task involving technology in many fields, mainly used to accurately extract target information from remote sensing images, and widely used in land detection, coverage classification, etc. These images are characterized by high resolution and large scale, and the overall segmentation requires high performance of hardware devices, the downsampling method usually used is easy to affect the segmentation quality, cropping and slicing the image will lead to the lack of edge information, and the problems of category homogenization, changes in complex scenes, and noise and occlusion make the segmentation challenging. Therefore, in this paper, we propose An iterative attention context fusion network (IACFNet) based on the attention mechanism as well as information fusion at different scales, which iteratively calculates the attention module weights connecting low and high features and refines the problem of spatial information loss, utilizing multiscale segmentation for information complementation, and utilizing an improved boundary loss function to precisely define boundary instances. Our proposed method obtains a performance improvement of about 2.8% and 1.5% in the mean intersection over union (mIoU) metric compared to the current state-of-the-art methods, respectively.
KEYWORDS: Information technology, Algorithms, Principal component analysis, Linear filtering, Matrices, Error analysis, Data processing, Roads, Machine learning, Visualization
In recent years, Low-rank matrix recovery from corrupted noise matrix has attracted interests as a very effective method
in high-dimensional data. And its fast algorithm has become a research focus. This paper we first review the basic theory
and typical accelerated algorithms. All these methods are proposed to mitigating the computational burden, such as the
iteration count before convergence, especially the frequent large-scale Singular Value Decomposition (SVD). For better
convergence, we employ the Augmented Lagrange Multipliers to solve the optimization problem. Recent the endeavors
have focused on smaller-scale SVD, especially the method based on submatrix. Finally, we present numerical
experiments on large-scale date.
Classical compression methods of remote sensing (RS) panchromatic images are much the same as the traditional compression ones, in which distributions of different surface features are not taken into account. Instead, RS panchromatic images are divided into blocks in our method and those blocks can be classified into several categories by analyzing their intensity distributions. Afterwards, each category is compressed separately. According to Shannon’s theorem 3, a source with given distribution and distortion has a unique theoretical minimum bitrate. Hence, under a given compression quality, the theoretical minimum bitrate of each category can be calculated using rate-distortion theory. Meanwhile, each category may have its own distortion due to the user’s different quality requirements. Our method performs well in reducing the redundancy of surface features which users do not care about so that more “valid data” would be obtained from the compressed images. Furthermore, it also provides flexibility between fixed compression ratio and quality-based compression.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.