scholarly journals Adaptive Distance-Weighted Voronoi Tessellation for Remote Sensing Image Segmentation

2020 ◽  
Vol 12 (24) ◽  
pp. 4115
Author(s):  
Xiaoli Li ◽  
Jinsong Chen ◽  
Longlong Zhao ◽  
Shanxin Guo ◽  
Luyi Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Noise Immunity ◽  
Voronoi Tessellation ◽  
Spectral Characteristics ◽  
Remote Sensing Image ◽  
Effective Characteristics ◽  
Spatial Distance ◽  
Remote Sensing Images ◽  
Distance Weighted

The spatial fragmentation of high-resolution remote sensing images makes the segmentation algorithm put forward a strong demand for noise immunity. However, the stronger the noise immunity, the more serious the loss of detailed information, which easily leads to the neglect of effective characteristics. In view of the difficulty of balancing the noise immunity and effective characteristic retention, an adaptive distance-weighted Voronoi tessellation technology is proposed for remote sensing image segmentation. The distance between pixels and seed points in Voronoi tessellation is established by the adaptive weighting of spatial distance and spectral distance. The weight coefficient used to control the influence intensity of spatial distance is defined by a monotone decreasing function. Following the fuzzy clustering framework, a fuzzy segmentation model with Kullback–Leibler (KL) entropy regularization is established by using multivariate Gaussian distribution to describe the spectral characteristics and Markov Random Field (MRF) to consider the neighborhood effect of sub-regions. Finally, a series of parameter optimization schemes are designed according to parameter characteristics to obtain the optimal segmentation results. The proposed algorithm is validated on many multispectral remote sensing images with five comparing algorithms by qualitative and quantitative analysis. A large number of experiments show that the proposed algorithm can overcome the complex noise as well as better ensure effective characteristics.

scholarly journals Semi-Supervised Remote Sensing Image Semantic Segmentation via Consistency Regularization and Average Update of Pseudo-Label

2020 ◽  
Vol 12 (21) ◽  
pp. 3603 ◽  
Author(s):  
Jiaxin Wang ◽  
Chris H. Q. Ding ◽  
Sibao Chen ◽  
Chenggang He ◽  
Bin Luo
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Supervised Learning ◽  
Semantic Segmentation ◽  
Remote Sensing Image ◽  
Unlabeled Data ◽  
Training Method ◽  
Remote Sensing Images ◽  
Supervised Training ◽  
Great Progress

Image segmentation has made great progress in recent years, but the annotation required for image segmentation is usually expensive, especially for remote sensing images. To solve this problem, we explore semi-supervised learning methods and appropriately utilize a large amount of unlabeled data to improve the performance of remote sensing image segmentation. This paper proposes a method for remote sensing image segmentation based on semi-supervised learning. We first design a Consistency Regularization (CR) training method for semi-supervised training, then employ the new learned model for Average Update of Pseudo-label (AUP), and finally combine pseudo labels and strong labels to train semantic segmentation network. We demonstrate the effectiveness of the proposed method on three remote sensing datasets, achieving better performance without more labeled data. Extensive experiments show that our semi-supervised method can learn the latent information from the unlabeled data to improve the segmentation performance.

scholarly journals REGION-BASED FUZZY CLUSTERING IMAGE SEGMENTATION ALGORITHM WITH KULLBACK-LEIBLER DISTANCE

2020 ◽  
Vol V-4-2020 ◽  
pp. 27-31
Author(s):  
X. L. Li ◽  
J. S. Chen
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Fuzzy Clustering ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Spatial Constraints ◽  
Image Domain ◽  
Markov Random ◽  
Optimal Segmentation ◽  
Image Segmentation Algorithm

Abstract. To effectively describe the uncertainty of remote sensing image segmentation, a novel region-based algorithm using fuzzy clustering and Kullback-Leibler (KL) distance is proposed. By regular tessellation, the image domain is completely divided into several sub-blocks to overcome the complex noise existed in high-resolution remote sensing images. Taking the blocks as the basic processing units, KL divergence is used to model the distance between blocks and clusters, which enables the model to describe the uncertainty of the non-similarity relationship. Besides, based on the theory of Markov Random Field (MRF), the regionalized KL entropy regularization term is established and added to the objective function to further consider the spatial constraints. Finally, the optimal segmentation results are obtained by estimating the parameters. The experiments carried out on different kinds of remote sensing images by comparing algorithms fully demonstrate the performance of the proposed algorithm.

scholarly journals Variational-Scale Segmentation for Multispectral Remote-Sensing Images Using Spectral Indices

2022 ◽  
Vol 14 (2) ◽  
pp. 326
Author(s):  
Ke Wang ◽  
Hainan Chen ◽  
Ligang Cheng ◽  
Jian Xiao
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Land Cover ◽  
Spectral Index ◽  
Remote Sensing Image ◽  
Spectral Indices ◽  
Segmentation Method ◽  
Remote Sensing Images ◽  
Watershed Transformation ◽  
Multispectral Remote Sensing

Many studies have focused on performing variational-scale segmentation to represent various geographical objects in high-resolution remote-sensing images. However, it remains a significant challenge to select the most appropriate scales based on the geographical-distribution characteristics of ground objects. In this study, we propose a variational-scale multispectral remote-sensing image segmentation method using spectral indices. Real scenes in remote-sensing images contain different types of land cover with different scales. Therefore, it is difficult to segment images optimally based on the scales of different ground objects. To guarantee image segmentation of ground objects with their own scale information, spectral indices that can be used to enhance some types of land cover, such as green cover and water bodies, were introduced into marker generation for the watershed transformation. First, a vector field model was used to determine the gradient of a multispectral remote-sensing image, and a marker was generated from the gradient. Second, appropriate spectral indices were selected, and the kernel density estimation was used to generate spectral-index marker images based on the analysis of spectral indices. Third, a series of mathematical morphology operations were used to obtain a combined marker image from the gradient and the spectral index markers. Finally, the watershed transformation was used for image segmentation. In a segmentation experiment, an optimal threshold for the spectral-index-marker generation method was identified. Additionally, the influence of the scale parameter was analyzed in a segmentation experiment based on a five-subset dataset. The comparative results for the proposed method, the commonly used watershed segmentation method, and the multiresolution segmentation method demonstrate that the proposed method yielded multispectral remote-sensing images with much better performance than the other methods.

scholarly journals A Multi-Level Feature Fusion Network for Remote Sensing Image Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1267
Author(s):  
Sijun Dong ◽  
Zhengchao Chen
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
High Resolution ◽  
Feature Fusion ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Environmental Disaster ◽  
Forestry Industry ◽  
Multi Level ◽  
Industrial Level

High-resolution remote sensing image segmentation is a mature application in many industrial-level image applications and it also has military and civil applications. The scene analysis needs to be automated as much as possible with high-resolution remote sensing images. This plays a significant role in environmental disaster monitoring, forestry industry, agricultural farming, urban planning, and road analysis. This study proposes a multi-level feature fusion network (MFNet) that can integrate the multi-level features in the backbone to obtain different types of image information. Finally, the experiments in this study demonstrate that the proposed network can achieve good segmentation results in the Vaihingen and Potsdam datasets. By aiming to achieve a large difference in the scale of the target objects in remote sensing images and achieving a poor recognition result for small objects, a multi-level feature fusion solution is proposed in this study. This investigation improves the recognition results of the remote sensing image segmentation to a certain extent.

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Remote Sensing Image Retrieval with Gabor-CA-ResNet and Split-Based Deep Feature Transform Network

2021 ◽  
Vol 13 (5) ◽  
pp. 869
Author(s):  
Zheng Zhuo ◽  
Zhong Zhou
Keyword(s):  
Remote Sensing ◽  
Image Retrieval ◽  
State Of The Art ◽  
Remote Sensing Image ◽  
Storage Space ◽  
Remote Sensing Images ◽  
Retrieval Method ◽  
Organization Management ◽  
Deep Feature ◽  
Feature Transform

In recent years, the amount of remote sensing imagery data has increased exponentially. The ability to quickly and effectively find the required images from massive remote sensing archives is the key to the organization, management, and sharing of remote sensing image information. This paper proposes a high-resolution remote sensing image retrieval method with Gabor-CA-ResNet and a split-based deep feature transform network. The main contributions include two points. (1) For the complex texture, diverse scales, and special viewing angles of remote sensing images, A Gabor-CA-ResNet network taking ResNet as the backbone network is proposed by using Gabor to represent the spatial-frequency structure of images, channel attention (CA) mechanism to obtain stronger representative and discriminative deep features. (2) A split-based deep feature transform network is designed to divide the features extracted by the Gabor-CA-ResNet network into several segments and transform them separately for reducing the dimensionality and the storage space of deep features significantly. The experimental results on UCM, WHU-RS, RSSCN7, and AID datasets show that, compared with the state-of-the-art methods, our method can obtain competitive performance, especially for remote sensing images with rare targets and complex textures.

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