Analysis of Flow Regime in the Turbidity Maximum Zone of Yangtze Estuary Based on Texture Features of Tiangong-2 Remote Sensing Images

2018 ◽  
pp. 312-322 ◽  
Author(s):  
Lizhi Teng ◽  
Heqin Cheng ◽  
Yuanying Qiao
Keyword(s):  
Remote Sensing ◽  
Flow Regime ◽  
Texture Features ◽  
Yangtze Estuary ◽  
Turbidity Maximum ◽  
Remote Sensing Images ◽  
Turbidity Maximum Zone

scholarly journals Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

2019 ◽  
Vol 11 (2) ◽  
pp. 108 ◽  
Author(s):  
Lu Xu ◽  
Dongping Ming ◽  
Wen Zhou ◽  
Hanqing Bao ◽  
Yangyang Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Color Space ◽  
Texture Features ◽  
Rate Of Change ◽  
Parameters Estimation ◽  
Remote Sensing Images ◽  
Image Region ◽  
Local Variance

Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction.

scholarly journals Remote-Sensing Image Tile-Level Annotation Based on Discriminative Features and Expressive Visual Word Descriptors

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Jiaming Xue ◽  
Shun Xiong ◽  
Chaoguang Men ◽  
Zhiming Liu ◽  
Yongmei Liu
Keyword(s):  
Remote Sensing ◽  
Expert Knowledge ◽  
Feature Fusion ◽  
Image Interpretation ◽  
Texture Features ◽  
Remote Sensing Image ◽  
Remote Sensing Images ◽  
Visual Words ◽  
Wide Range ◽  
Semantic Concepts

Remote-sensing images play a crucial role in a wide range of applications and have been receiving significant attention. In recent years, great efforts have been made in developing various methods for intelligent interpretation of remote-sensing images. Generally speaking, machine learning-based methods of remote-sensing image interpretation require a large number of labeled samples and there are still not enough annotated datasets in the field of remote sensing. However, manual annotation of remote-sensing images is usually labor-intensive and requires expert knowledge and the accuracy of annotation results is relatively low. The goal of this paper is to propose a novel tile-level annotation method of remote-sensing images to obtain remote-sensing datasets which are well-labeled and contain accurate semantic concepts. Firstly, we use a set of images with defined semantic concepts to represent the training set and divide them into several nonoverlapping regions. Secondly, the color features, texture features, and spatial features of each region are extracted, and discriminative features are obtained by the weight optimization feature fusion method. Then, the features are quantized into visual words by applying a density-based clustering center selection method and an isolated feature point elimination method. And the remote-sensing images can be represented by a series of visual words. Finally, the LDA model is used to calculate the probabilities of semantic categories for each region. The experiments are conducted on remote-sensing images which demonstrate that our proposed method can achieve good performance on remote-sensing image tile-level annotation. The implications of our research can obtain annotated datasets with accurate semantic concepts for intelligent interpretation of remote-sensing images.

Influences of River Discharge Variation and Tidal Asymmetry on the Spatial Evolution of the Turbidity Maximum Zone in Yangtze Estuary

2020 ◽  
Author(s):  
Ping Dong ◽  
Huabin Shi
Keyword(s):  
River Discharge ◽  
Yangtze Estuary ◽  
Turbidity Maximum ◽  
Tidal Range ◽  
Tidal Asymmetry ◽  
Interannual Variations ◽  
The South ◽  
Turbidity Maximum Zone ◽  
Averaged Model ◽  
Seasonal And Interannual Variations

<p>The Yangtze estuary is characterized by its extremely high suspended sediment concentration (SSC) and the extensive turbidity maximum zone (TMZ). The estuary is physically forced by an upstream river discharge seasonally varying in a wide range of 6000 – 92000 m3/s and semidiurnal-diurnal mixed tides with the tidal range up to 5 m. The influences of the seasonal and interannual variations in the upstream river discharge and the tidal asymmetry on the location of the Yangtze TMZ are numerically investigated with a two-dimensional depth-averaged model. Sensitivities of SSC and hence the location of TMZ to the bottom shear stress, bed erodibility, and the sediment settling velocity are studied. The spatial and temporal evolutions of the TMZ position in the cases of various upstream river discharges with different monthly, seasonal and interannual variations are simulated and discussed. The effects of the M2/M4-induce tidal asymmetry on the TMZ position and those of the interactions between the eight main astronomical tides (M2, S2, N2, K2, K1, O1, P1, and Q1) are compared. It is shown that the M2/M4-induce tidal asymmetry plays a critical role in the formulation of TMZ in the downstream of the South Branch of Yangtze estuary, while the interactions between the eight main astronomical tides have more significant effects on the TMZ location in other areas of Yangtze estuary such as the South and the North Passages.</p>

The Earthquake Damage Remote Sensing Automatic Recgnition Based on Texture of Roof Tiles

2014 ◽  
Vol 651-653 ◽  
pp. 1315-1319 ◽  
Author(s):  
Dong Ping Li ◽  
Jun Gong ◽  
Jing Yi Li ◽  
Shan Shan Guo
Keyword(s):  
Remote Sensing ◽  
Evaluation Method ◽  
Rapid Assessment ◽  
Texture Features ◽  
Technology Support ◽  
Remote Sensing Images ◽  
Gis Technology ◽  
Disaster Assessment ◽  
High Resolution Images ◽  
Earthquake Damages

To meet the technical demands of rapid assessment on small and medium earthquake damages, this paper presents the comprehensive disaster evaluation method of on-spot human-computer interaction survey and remote sensing image analysis based on the GIS technology support in the small and medium earthquakes. By making full use of the advantages of existing data, emphasizing on the automatical identification of the unique texture features of small earthquakes with a combination analysis on high resolution images gained from unmanned aerial vehicles (uav) and the seismic damages, the new method results in the rank distribution of earthquakes by gaining the experienced parameter of local small-medium earthquakes based on the analysis of regional characteristics of texture features of remote sensing images. It is concluded that the evaluation method is more accurate and efficient for small and medium earthquake rapid disaster assessment.

scholarly journals Turbidity maximum zone index: A novel model for remote extraction of turbidity maximum zone in different estuaries

2021 ◽  
Author(s):  
Chongyang Wang ◽  
Li Wang ◽  
Danni Wang ◽  
Dan Li ◽  
Chenghu Zhou ◽  
...  
Keyword(s):  
Remote Sensing ◽  
River Estuary ◽  
Turbidity Maximum ◽  
Low Flow ◽  
Spatial And Temporal Variation ◽  
Hydrological Process ◽  
Turbidity Maximum Zone ◽  
The World ◽  
Fixed Threshold ◽  
Novel Model

Abstract. Recognizing and extracting estuarine turbidity maximum zone (TMZ) efficiently is important for kinds of terrestrial hydrological process. Although many relevant studies of TMZ have been carried out around the world, the method of extracting and criteria of describing TMZ vary greatly from different regions and different times. In order to improve the applicability of the fixed threshold in previous studies and develop a novel model extracting TMZ accurately in multi estuaries and different seasons by remote sensing imagery, this study estimated the total suspended solids (TSS) concentrations and chlorophyll a (Chla) concentrations in Pearl River Estuary (PRE), Hanjiang River Estuary (HRE) and Moyangjiang River Estuary (MRE) of Guangdong province, China. The spatial distribution characteristics of both TSS concentrations and Chla concentrations were analyzed subsequently. It was found that there was an almost opposite relationship between TSS concentration and Chla concentration in the three estuaries, especially in PRE. The regions of high (low) TSS concentrations are exactly corresponding to the relative low (high) Chla concentrations. Based on the special feature, an index named turbidity maximum zone index (TMZI), defining as the ratio of the difference and sum of logarithmic transformation of TSS concentrations and Chla concentrations, was firstly proposed. By calculating the values of TMZI in PRE on 20 November 2004 (low-flow season), it was found that the criterion (TMZI > 0.2) could be used to distinguish TMZs of PRE effectively. Compared with the true (false) color imagery and the rudimentary visual interpretation results, the TMZs extraction results by TMZI were mostly consistent with the actual distribution. Moreover, the same criterion was further applied in PRE on 18 October 2015. The high accuracy and good consistency across seasons were also found. The west shoal of PRE was the main distribution areas of TMZs. In addition, the good performance in extracting TMZs by this newly proposed index were also found in different estuaries and different times (HRE, 13 August 2008, high-flow season; MRE, on 6 December 2013, low-flow season). Compared to the previous fixed threshold (TSS or turbidity) methods, extracting TMZ by TMZI has a higher accuracy and better applicability. Evidently, this unified TMZI is a potentially optimized method to monitor and extract TMZs of other estuaries in the world by different satellite remote sensing imageries, which can be used to improve the understanding of the spatial and temporal variation of TMZs and estuarial processes on regional and global scales, and the management and sustainable development of regional society and nature environment.

The Extraction of Water Based on Texture Features Form Remote Sensing Information

2012 ◽  
Vol 546-547 ◽  
pp. 1444-1447
Author(s):  
Kun Pang ◽  
Wen Bin Xie ◽  
Zhi Gang Ao ◽  
Guo Qin Qiu ◽  
Song Huang
Keyword(s):  
Remote Sensing ◽  
Wavelet Transform ◽  
Texture Feature ◽  
Texture Features ◽  
Water Bodies ◽  
Remote Sensing Images ◽  
Water Based

Take the SPOT remote sensing images for a district in June 2009 as study object, using the texture feature methods based on the wavelet transform to the extraction for image water information, the results show that: The introduction of the wavelet transform and the edge of the restrictions to the the split of waters , can effectively extract and classify different colors, types of water bodies .

scholarly journals CHANGE DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES USING LEVENE-TEST AND FUZZY EVALUATION

2018 ◽  
Vol XLII-3 ◽  
pp. 1695-1701
Author(s):  
G. H. Wang ◽  
H. B. Wang ◽  
W. F. Fan ◽  
Y. Liu ◽  
H. J. Liu
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Evaluation Method ◽  
Fault Tolerant ◽  
Texture Features ◽  
Fuzzy Evaluation ◽  
Remote Sensing Images ◽  
Texture Information ◽  
Levene Test

High-resolution remote sensing images possess complex spatial structure and rich texture information, according to these, this paper presents a new method of change detection based on Levene-Test and Fuzzy Evaluation. It first got map-spots by segmenting two overlapping images which had been pretreated, extracted features such as spectrum and texture. Then, changed information of all map-spots which had been treated by the Levene-Test were counted to obtain the candidate changed regions, hue information (H component) was extracted through the IHS Transform and conducted change vector analysis combined with the texture information. Eventually, the threshold was confirmed by an iteration method, the subject degrees of candidate changed regions were calculated, and final change regions were determined. In this paper experimental results on multi-temporal ZY-3 high-resolution images of some area in Jiangsu Province show that: Through extracting map-spots of larger difference as the candidate changed regions, Levene-Test decreases the computing load, improves the precision of change detection, and shows better fault-tolerant capacity for those unchanged regions which are of relatively large differences. The combination of Hue-texture features and fuzzy evaluation method can effectively decrease omissions and deficiencies, improve the precision of change detection.

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