Resolution Remote Sensing Image Segmentation Research Articles

Extraction of urban green space with high resolution remote sensing image segmentation

Extraction of urban green space with high resolution remote sensing image segmentation

High spatial resolution remote sensing image segmentation based on the multiclassification model and the binary classification model

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.

A high resolution remote sensing image segmentation method based on superpixel and graph theory

A high resolution remote sensing image segmentation method based on superpixel and graph theory

Fully Convolutional Neural Network with Augmented Atrous Spatial Pyramid Pool and Fully Connected Fusion Path for High Resolution Remote Sensing Image Segmentation

Recent developments in Convolutional Neural Networks (CNNs) have allowed for the achievement of solid advances in semantic segmentation of high-resolution remote sensing (HRRS) images. Nevertheless, the problems of poor classification of small objects and unclear boundaries caused by the characteristics of the HRRS image data have not been fully considered by previous works. To tackle these challenging problems, we propose an improved semantic segmentation neural network, which adopts dilated convolution, a fully connected (FC) fusion path and pre-trained encoder for the semantic segmentation task of HRRS imagery. The network is built with the computationally-efficient DeepLabv3 architecture, with added Augmented Atrous Spatial Pyramid Pool and FC Fusion Path layers. Dilated convolution enlarges the receptive field of feature points without decreasing the feature map resolution. The improved neural network architecture enhances HRRS image segmentation, reaching the classification accuracy of 91%, and the precision of recognition of small objects is improved. The applicability of the improved model to the remote sensing image segmentation task is verified.

Unsupervised evaluation-based region merging for high resolution remote sensing image segmentation

Image segmentation has a remarkable influence on the classification accuracy of object-based image analysis. Accordingly, how to raise the performance of remote sensing image segmentation is a key issue. However, this is challenging, primarily because it is difficult to avoid over-segmentation errors (OSE) and under-segmentation errors (USE). To solve this problem, this article presents a new segmentation technique by fusing a region merging method with an unsupervised segmentation evaluation technique called under- and over-segmentation aware (UOA), which is improved by using edge information. Edge information is also used to construct the merging criterion of the proposed approach. To validate the new segmentation scheme, five scenes of high resolution images acquired by Gaofen-2 and Ziyuan-3 multispectral sensors are chosen for the experiment. Quantitative evaluation metrics are employed in the experiment. Results indicate that the proposed algorithm obtains the lowest total error (TE) values for all test images (0.3791, 0.1434, 0.7601, 0.7569, 0.3169 for the first, second, third, fourth, fifth image, respectively; these values are averagely 0.1139 lower than the counterparts of the other methods), as compared to six state-of-the-art region merging-based segmentation approaches, including hybrid region merging, hierarchical segmentation, scale-variable region merging, size-constrained region merging with edge penalty, region merging guided by priority, and region merging combined with the original UOA. Moreover, the performance of the proposed method is better for artificial-object-dominant scenes than the ones mainly covering natural geo-objects.

Scale-variable region-merging for high resolution remote sensing image segmentation

In high resolution remote sensing imagery (HRI), the sizes of different geo-objects often vary greatly, posing serious difficulties to their successful segmentation. Although existent segmentation approaches have provided some solutions to this problem, the complexity of HRI may still lead to great challenges for previous methods. In order to further enhance the quality of HRI segmentation, this paper proposes a new segmentation algorithm based on scale-variable region merging. Scale-variable means that the scale parameters (SP) adopted for segmentation are adaptively estimated, so that geo-objects of various sizes can be better segmented out. To implement the proposed technique, 3 steps are designed. The first step produces a coarse-segmentation result with slight degree of under segmentation error. This is achieved by segmenting a half size image with the global optimal SP. Such a SP is determined by using the image of original size. In the second step, structural and spatial contextual information is extracted from the coarse-segmentation, enabling the estimation of variable SPs. In the last step, a region merging process is initiated, and the SPs used to terminate this process are estimated based on the information obtained in the second step. The proposed method was tested by using 3 scenes of HRI with different landscape patterns. Experimental results indicated that our approach produced good segmentation accuracy, outperforming some competitive methods in comparison.

Review on High Spatial Resolution Remote Sensing Image Segmentation Evaluation

Review on High Spatial Resolution Remote Sensing Image Segmentation Evaluation

High resolution remote sensing image segmentation using super-pixel MRF for agricultural area

High resolution remote sensing image segmentation using super-pixel MRF for agricultural area

Optimal Segmentation of High-Resolution Remote Sensing Image by Combining Superpixels With the Minimum Spanning Tree

Image segmentation is the foundation of object-based image analysis, and many researchers have sought optimal segmentation results. The initial image oversegmentation and the optimal segmentation scale are two vital factors in high spatial resolution remote sensing image segmentation. With respect to these two issues, a novel image segmentation method combining superpixels with a minimum spanning tree is proposed in this paper. First, the image is oversegmented using a simple linear iterative clustering algorithm to obtain superpixels. Then, the superpixels are clustered by regionalization with a dynamically constrained agglomerative clustering and partitioning (REDCAP) algorithm using the initial number of segments, and the local variance (LV) and the rate of LV change (ROC-LV) indicator diagrams corresponding to the number of segments are obtained. The suitable number of image segments is determined according to the LV and ROC-LV indicator diagrams corresponding to the number of segments. Finally, the superpixels are reclustered using the REDCAP algorithm based on the suitable number of image segments to obtain the image segmentation result. Through two sets of experiments, the proposed method is compared with two other segmentation algorithms. The experimental results show that the proposed method outperforms the others and obtains good image segmentation results.

INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION

Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

INTERVAL TYPE-2 FUZZY BASED NEURAL NETWORK FOR HIGH RESOLUTION REMOTE SENSING IMAGE SEGMENTATION

Abstract. Recently, high resolution remote sensing image segmentation is a hot issue in image procesing procedures. However, it is a difficult task. The difficulties derive from the uncertainties of pixel segmentation and decision-making model. To this end, we take spatial relationship into consideration when constructing the interval type-2 fuzzy neural networks for high resolution remote sensing image segmentation. First, the proposed algorithm constructs a Gaussian model as a type-1 fuzzy model to describe the uncertainty contained in the image. Second, interval type-2 fuzzy model is obtained by blurring the mean and variance in type-1 model. The proposed interval type-2 model can strengthen the expression of uncertainty and simultaneously decrease the uncertainty in the decision model. Then the fuzzy membership function itself and its upper and lower fuzzy membership functions of the training samples are used as the input of neuron network which acts as the decision model in proposed algorithm. Finally, the relationship of neighbour pixels is taken into consideration and the fuzzy membership functions of the detected pixel and its neighbourhood are used to decide the class of each pixel to get the final segmentation result. The proposed algorithm, FCM and HMRF-FCM algorithm and an interval type-2 fuzzy neuron networks without spatial relationships are performed on synthetic and real high resolution remote sensing images. The qualitative and quantitative analyses demonstrate the efficient of the proposed algorithm, especially for homogeneous regions which contains a great difference in its gray level (for example forest).

High resolution remote sensing image segmentation based on graph theory and fractal net evolution approach

Abstract. Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.

Accuracy evaluating method for object-based segmentation of high resolution remote sensing image

Based on the principles of supervised evaluation method, the paper puts forward three evaluation indices of the high resolution remote sensing image segmentation: precision, recall and relative similarity, and brings forward the method of precision evaluation of remote sensing image segmentation. With respect to the reference object matching problem of the supervised evaluation method, the paper proposes the matching method of bidirectional local optimal object. Validation of the proposed evaluation indices is carried out using GF-1 high resolution remote sensing image in Huainan city, Anhui province, China. The results show that the proposed evaluation indices can reflect very well the quality of the segmentation results and are consistent with the real distribution of ground landcover segmentation, and also provide the basic reference to parameter setting for the image segmentation algorithm and optimal scale selection for the multi-scale segmentation.

Refining High Spatial Resolution Remote Sensing Image Segmentation for Man-made Objects through aCollinear and Ipsilateral Neighborhood Model

AbstractMan-made objects, such as buildings and roads, which areimportant targets for information extraction from high spatial resolution (HSR) remote sensing images, often feature straight boundaries. This study employs this knowledge on HSR image segmentation by embedding a straight-line constraint in regionbased image segmentation. A new concept called collinear and ipsilateral neighborhood is proposed and applied to hardboundary constraint-based image segmentation for accuracy improvement. In the experimental areas, the method accuracy measured by recall ratio r increases from 0.036 to 0.048 (on the average) after the refinement, with significantly smaller decreases in precision p that are all less than 0.006. In sum, the proposed technique effectively reduces over-segmentation errors and maintains the same level of under-segmentation error ratio, particularly in man-made areas. It facilitates subsequent objectbased image analyses, including feature extraction, object recognition, and classification.

Color Remote-sensing Image Segmentation Based on Improved Region Filter

High resolution remote-sensing images provide abundant color, shape structure and texture information. However, region-based segmentations do not allow to fully exploit the richness of this kind of images. Despite the enormous progress in the analysis of remote sensing imagery over the past three decades, there is a lack of guidance on how to select an image segmentation algorithm suitable for the image type and size. In accordance with the characteristics of color high-resolution remote sensing images, we put forward an improved region filter remote-sensing color-image segmentation algorithm based on traditional JSEG segmentation. In order to well describe the color homogeneity in region, the local homogeneity matrix can be adopted to correct the local value in traditional JSEG segmentation algorithm so as to accurately reflect the boundary region and enhance the accuracy of image segmentation. Simulation experiment results demonstrate that our proposed algorithm can efficiently overcome the inaccurate segmentation of boundary region. In addition, compared with the traditional filter algorithm, our proposed algorithm can extract weak edge more accurately so as to achieve the better segmentation result in high resolution remote-sensing image segmentation

A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm

Considering the difficulties in image segmentation caused by the complexity of diverse ecological environments and various artificial targets in high resolution remote sensing images, especially in city scene, and in order to overcome the limitations existing in the traditional segmentation algorithm, JSEG (J-Segmentation), for high resolution remote sensing image segmentation and to further improve the segmentation accuracy, WJSEG (Wavelet-JSEG), a novel multi-scale segmentation algorithm based on wavelet transform, is proposed, which is an improved JSEG algorithm. WJSEG is an improved form of JSEG in relation to three aspects, including color quantization, multi-scale segmentation and region merging by introducing the multi-scale analysis tool based on wavelet transform. Experiments have been conducted on high resolution SPOT 5 pan-sharpened multispectral image and IKONOS panchromatic image. These experimental results were compared with those gained by the traditional JSEG algorithm and the famous commercial software named eCognition, which validated the effectiveness and reliability of the proposed WJSEG algorithm.

Multi-scale and Multi-feature Segmentation of High Resolution Remote Sensing Image

With the development of the remote sensing technology, high resolution remote sensing images widely penetrates into the common people's life. Traditional medium or low resolution image processing method based on pixel doesn't meet people's requirement any more. In view of it, this paper puts forward a high resolution remote sensing image segmentation method based on the traditional watershed algorithm with multiple scales and characteristics and introduces two new concepts-wrong waterlogging basin and merging array. It firstly improves the immersion process of traditional algorithm, then optimizes the wrong water logging basin and the merging array of basin, adopts the eight immersion neighbors avoiding the wrong division based on four neighbors immersion, optimizes the multilayer immersion process, segments the high resolution remote sensing image with different scales and different features, and finally combines the similar regions. The simulation experiments show that the improved watershed algorithm not only keeps the accurate segmentation effect of traditional algorithm, but also has much higher efficiency.

Object Detection with an Improved Level Set Method

Object detection is quite an important research in remote sensing image analysis. In this paper, we propose an edge and region based model for high resolution remote sensing image segmentation with level set formulation. Our method firstly made an image enhancement based on ROI (Region of Interest). By introducing the edge speed-up function, we can save time through decreasing the iterations and get a flexible segmentation considering the complexity of high resolution remote sensing image. Our method has been preliminarily applied to QuickBird and aerial images.

An Edge Embedded Marker-Based Watershed Algorithm for High Spatial Resolution Remote Sensing Image Segmentation

This correspondence proposes an edge embedded marker-based watershed algorithm for high spatial resolution remote sensing image segmentation. Two improvement techniques are proposed for the two key steps of maker extraction and pixel labeling, respectively, to make it more effective and efficient for high spatial resolution image segmentation. Moreover, the edge information, detected by the edge detector embedded with confidence, is used to direct the two key steps for detecting objects with weak boundary and improving the positional accuracy of the objects boundary. Experiments on different images show that the proposed method has a good generality in producing good segmentation results. It performs well both in retaining the weak boundary and reducing the undesired over-segmentation.