Query: Remote Sensing Image  segmentation

title: A Multi Level Feature Fusion Network for Remote Sensing Image Segmentation
abstract: 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.

title: REGION ADAPTIVE ADJUSTMENT STRATEGY BASED ON INFORMATION ENTROPY FOR REMOTE SENSING IMAGE SEGMENTATION
abstract: For the difficulty of boundary fitting in region based algorithms, a region adaptive adjustment strategy based on information entropy is proposed for remote sensing image segmentation. Considering the characteristics of imperfect blocks that cover two homogeneous regions, a selection factor constructed by the spectral coefficient of variation and the information entropy of prior probability representing neighborhood constraint is designed to find the imperfect blocks. Then, the selected imperfect block is split into four equal parts, and new blocks enjoy the same membership as the original block. The model parameters are updated based on the current tessellation. If the fuzzy clustering objective function decrease, the split operation is certainly accepted, otherwise, it will be accepted with a certain probability to avoid local optimum. Finally, the experiments on simulated and multi spectral remote sensing images show that the proposed strategy can accurately locate the imperfect blocks and effectively fit the boundary of homogeneous regions.

title: Remote sensing image segmentation based on the fuzzy deep convolutional neural network
abstract: Remote sensing image segmentation has large uncertainty related to the heterogeneity of similar objects and complex spectrum in satellite images, causing the traditional segmentation methods to be greatly limited. Existing semantic segmentation methods represented by deep learning have made breakthrough progress. However, traditional deep learning methods, such as deep convolution neural network, are a completely deterministic model, which cannot describe the uncertainty of remote sensing image well. To solve this problem, a new deep neural network combined with fuzzy logic units is proposed in this paper, called RSFCNN (Remote Sensing image segmentation with Fuzzy Convolutional Neural Network) The network integrates convolution units and fuzzy logic units. Convolution units are used to extract discriminant features with different proportions, thus providing comprehensive information for pixel level remote sensing image segmentation. Fuzzy logic units are used to deal with various uncertainties and provide more reliable segmentation results, and each unit handles the feature maps at a particular image scale by Gaussian blur function. Experiments were carried out on two data sets, and the results showed that RSFCNN has higher segmentation accuracy and better performance than state of the art algorithms.

title: Large Remote Sensing Image Segmentation with Stitching Strategy Based on Dominant Color
abstract: Large remote sensing image segmentation is a crucial issue in object based image analysis. It is common sense that a segmentation framework consists of three components: (1) dividing large remote sensing image into blocks for overcoming the constraint of computer memory; (2) executing segmentation algorithm for each block individually; (3) stitching segmentation results of all blocks into a complete result for eliminating artificial borders created by dividing blocks. However, there is a lack of mature technologies to eliminate artificial borders produced by dividing blocks. In this paper, we proposed a new stitching strategy based on the dominant color similarity measure and modified the traditional method of dominant color similarity measure to make it more suitable for measuring the similarity of two segmented regions. A multi scale segmentation algorithm is adopted for segmenting each block. External memory is used to store intermediate segmentation results and exchange data with internal memory. We tested the algorithm with three different images and validated that the algorithm can implement the segmentation for large remote sensing images in a common computer. Experiments demonstrate that the stitching strategy based on the similarity measure of dominant color can effectively eliminate artificial borders.

title: High spatial resolution remote sensing image segmentation based on the multiclassification model and the binary classification model
abstract: Semantic segmentation technology is an important step in the interpretation of remote sensing images. High spatial resolution remote sensing images have clear features. Traditional image segmentation methods cannot fully represent the information in high spatial resolution images and tend to yield unsatisfactory segmentation accuracy. With the rapid development of deep learning, many researchers have tried to use deep learning algorithms for remote sensing image segmentation. This paper uses U Net for multiclassification and binary classification of Gaofen 2 high spatial resolution remote sensing image data. Six types of features, which were build up, farmland, water, meadow, forest and others, were labeled in the image. A "neighborhood voting" method was used to determine the category of uncertain pixels based on spatial heterogeneity and homogeneity. Through U Net neural network multiclassification, the overall accuracy of the training data is 93.83% the overall accuracy of the test data is 82.27% and the test accuracy of the binary classification algorithm is 79.75% The results show that the two models yield high accuracy and credibility in remote sensing image segmentation.

title: Remote sensing image segmentation advances: A meta analysis
abstract: The advances in remote sensing sensors during the last two decades have led to the production of very high spatial resolution multispectral images. In order to adapt to this rapid development and handle these data, object based analysis has emerged. A critical part of such an analysis is image segmentation. The selection of optimal segmentation parameters' values generates a qualitative segmentation output and has a direct impact on feature extraction and subsequent overall classification accuracy. Even though several image segmentation methods have been developed and suggested in the literature, each of them has advantages and disadvantages. This article presents the conceptual characteristics of image segmentation methods with a special focus on semantic segmentation. In addition, a meta analysis was conducted through a comprehensive review of recent image segmentation case studies. It includes statistics and quantitative data regarding the applied segmentation algorithm, the software utilized and the data source among others. Since there is no miraculous segmentation algorithm, the statistical results depict only the recent trend. Finally, a few interesting subjects are addressed, including identification of current problems, image segmentation on non traditional data and hot topics for future research.

title: High Resolution Remote Sensing Image Segmentation Framework Based on Attention Mechanism and Adaptive Weighting
abstract: Semantic segmentation has been widely used in the basic task of extracting information from images. Despite this progress, there are still two challenges: (1) it is difficult for a single size receptive field to acquire sufficiently strong representational features, and (2) the traditional encoder decoder structure directly integrates the shallow features with the deep features. However, due to the small number of network layers that shallow features pass through, the feature representation ability is weak, and noise information will be introduced to affect the segmentation performance. In this paper, an Adaptive Multi Scale Module (AMSM) and Adaptive Fuse Module (AFM) are proposed to solve these two problems. AMSM adopts the idea of channel and spatial attention and adaptively fuses three channel branches by setting branching structures with different void rates, and flexibly generates weights according to the content of the image. AFM uses deep feature maps to filter shallow feature maps and obtains the weight of deep and shallow feature maps to filter noise information in shallow feature maps effectively. Based on these two symmetrical modules, we have carried out extensive experiments. On the ISPRS Vaihingen dataset, the F1 score and Overall Accuracy (OA) reached 86.79% and 88.35% respectively.

title: Deep Learning optimization in remote sensing image segmentation using dilated convolutions and ShuffleNet
abstract: Semantic segmentation of land use land cover data using deep learning networks have gained significant importance in the remote sensing domain. However, deep learning architectures are computation intensive. In this research, we propose an Atrous Shuffle UNet network, which is designed to be lightweight. The network comprises of modified ShuffleNet units which are arranged in a similar network structure as the UNet. Atrous convolution in the proposed network increases the receptive field of the network enabling faster convergence. We compare the proposed network to state of the art deep learning architectures such as UNet, UNet with ResNet modules and a UNet with standard ShuffleNet modules. The proposed changes in the ShuffleNet units enable the network to outperform these architectures and do so with significantly less parameters.

title: Double Variance Measures: A Potential Approach to Parameter Optimization of Remote Sensing Image Segmentation
abstract: The unsupervised segmentation evaluation (USE) method has been commonly used for remote sensing segmentation parameter (SP) determinations to produce good segmentation results, due to its objectiveness and high efficiency. Existing studies have used different criteria to measure homogeneity and heterogeneity and have used certain combination strategies to form overall evaluations. However, different criteria have unique statistical characteristics. The differentiated statistical characteristics maintained in homogeneity and heterogeneity calculations may result in inherent instability in the USE results, leading to unsuitable SP selections. Moreover, few studies have focused on the simultaneous determination of a single optimal SP and multiple optimal SPs. In this article, double variance (DV) measures were proposed for recognizing more suitable SPs. Then, two combination strategies, F measure and local peak (LP) were applied to test the potential of using DV measures to determine a single SP and multiple SPs, respectively. The multiresolution segmentation algorithm and Gaofen 1 data were used to test the proposed method. The comparative results indicated that the DV is a more promising internal homogeneity and external heterogeneity metric for segmentation evaluation and optimal SP determination compared to conventional methods. The F measure based DV method could produce better overall goodness of segmentation for differently sized natural geo objects, compared with the competing methods. The LP based DV method could obtain multiple optimal scales that produced better segments for the identification of small, natural geo objects to large, semantic geo objects, compared to the competitive methods.

title: CCT: Conditional Co Training for Truly Unsupervised Remote Sensing Image Segmentation in Coastal Areas