Research on Extraction and Assistant Classification of Remote Sensing for Texture Feature

2014 ◽  
Vol 1073-1076 ◽  
pp. 1881-1885
Author(s):  
Li Hong Li ◽  
Xiu Ming Jia ◽  
Jin Tao Zhang
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristics ◽  
Spectral Feature ◽  
Texture Feature ◽  
Texture Features ◽  
Remote Sensing Image ◽  
Aerial Image ◽  
Likelihood Method ◽  
Texture Characteristics ◽  
The One

The classification precision of remote sensing image has always been one of the problems to each scholar. The traditional classification method is based on the spectral information. With the advancement of technology, the resolution of remote sensing image is gradually improving, and texture features included are getting rich, so adding texture characteristics to spectral characteristics for image classification can remedy the shortage of only relying on spectral characteristics. This paper uses experimental area of aerial image with 0.5m resolution in Datong, calculates fractal dimension using differential box-counting model, extracts the spatial texture features, and classifies precisely combining spectral characteristics in maximum likelihood method. Through comparing different classification results based on different characteristics, it show that classification accuracy based on combination of texture characteristics and spectral characteristics is more accurate (92% overall accuracy and kappa=0.91) than the one based on single spectral feature (88% overall accuracy and kappa=0.85) and texture feature (69% overall accuracy and kappa=0.65), which verifies the effectiveness of this method.

scholarly journals ANALYZING SPECTRAL CHARACTERISTICS OF SHADOW AREA FROM ADS-40 HIGH RADIOMETRIC RESOLUTION AERIAL IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 223-227 ◽  
Author(s):  
Yi-Ta Hsieh ◽  
Shou-Tsung Wu ◽  
Chaur-Tzuhn Chen ◽  
Jan-Chang Chen
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Index ◽  
Spectral Characteristics ◽  
Aerial Images ◽  
Aerial Image ◽  
Optical Remote Sensing ◽  
Shadow Area ◽  
Vegetation Indexes ◽  
Research Findings

The shadows in optical remote sensing images are regarded as image nuisances in numerous applications. The classification and interpretation of shadow area in a remote sensing image are a challenge, because of the reduction or total loss of spectral information in those areas. In recent years, airborne multispectral aerial image devices have been developed 12-bit or higher radiometric resolution data, including Leica ADS-40, Intergraph DMC. The increased radiometric resolution of digital imagery provides more radiometric details of potential use in classification or interpretation of land cover of shadow areas. Therefore, the objectives of this study are to analyze the spectral properties of the land cover in the shadow areas by ADS-40 high radiometric resolution aerial images, and to investigate the spectral and vegetation index differences between the various shadow and non-shadow land covers. According to research findings of spectral analysis of ADS-40 image: (i) The DN values in shadow area are much lower than in nonshadow area; (ii) DN values received from shadowed areas that will also be affected by different land cover, and it shows the possibility of land cover property retrieval as in nonshadow area; (iii) The DN values received from shadowed regions decrease in the visible band from short to long wavelengths due to scattering; (iv) The shadow area NIR of vegetation category also shows a strong reflection; (v) Generally, vegetation indexes (NDVI) still have utility to classify the vegetation and non-vegetation in shadow area. The spectral data of high radiometric resolution images (ADS-40) is potential for the extract land cover information of shadow areas.

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 Regression Tree CNN for Estimation of Ground Sampling Distance Based on Floating-Point Representation

2019 ◽  
Vol 11 (19) ◽  
pp. 2276
Author(s):  
Jae-Hun Lee ◽  
Sanghoon Sull
Keyword(s):  
Remote Sensing ◽  
Regression Tree ◽  
Remote Sensing Image ◽  
Input Image ◽  
Google Earth ◽  
Aerial Image ◽  
Floating Point ◽  
Binomial Tree ◽  
Sampling Distance ◽  
Public Datasets

The estimation of ground sampling distance (GSD) from a remote sensing image enables measurement of the size of an object as well as more accurate segmentation in the image. In this paper, we propose a regression tree convolutional neural network (CNN) for estimating the value of GSD from an input image. The proposed regression tree CNN consists of a feature extraction CNN and a binomial tree layer. The proposed network first extracts features from an input image. Based on the extracted features, it predicts the GSD value that is represented by the floating-point number with the exponent and its mantissa. They are computed by coarse scale classification and finer scale regression, respectively, resulting in improved results. Experimental results with a Google Earth aerial image dataset and a mixed dataset consisting of eight remote sensing image public datasets with different GSDs show that the proposed network reduces the GSD prediction error rate by 25% compared to a baseline network that directly estimates the GSD.

scholarly journals STUDY ON REMOTE SENSING IMAGE CHARACTERISTICS OF ECOLOGICAL LAND: CASE STUDY OF ORIGINAL ECOLOGICAL LAND IN THE YELLOW RIVER DELTA

2018 ◽  
Vol XLII-3 ◽  
pp. 35-38
Author(s):  
G. Q. An
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Vegetation Index ◽  
Yellow River ◽  
Spectral Characteristics ◽  
Texture Features ◽  
Yellow River Delta ◽  
River Delta ◽  
The Yellow River ◽  
The Yellow River Delta

Takes the Yellow River Delta as an example, this paper studies the characteristics of remote sensing imagery with dominant ecological functional land use types, compares the advantages and disadvantages of different image in interpreting ecological land use, and uses research results to analyse the changing trend of ecological land in the study area in the past 30 years. The main methods include multi-period, different sensor images and different seasonal spectral curves, vegetation index, GIS and data analysis methods. The results show that the main ecological land in the Yellow River Delta included coastal beaches, saline-alkaline lands, and water bodies. These lands have relatively distinct spectral and texture features. The spectral features along the beach show characteristics of absorption in the green band and reflection in the red band. This feature is less affected by the acquisition year, season, and sensor type. Saline-alkali land due to the influence of some saline-alkaline-tolerant plants such as alkali tent, Tamarix and other vegetation, the spectral characteristics have a certain seasonal changes, winter and spring NDVI index is less than the summer and autumn vegetation index. The spectral characteristics of a water body generally decrease rapidly with increasing wavelength, and the reflectance in the red band increases with increasing sediment concentration. In conclusion, according to the spectral characteristics and image texture features of the ecological land in the Yellow River Delta, the accuracy of image interpretation of such ecological land can be improved.

scholarly journals Adaptive Filtering Remote Sensing Image Segmentation Network based on Attention Mechanism

2021 ◽  
Author(s):  
Cong zhong Wu ◽  
Hao Dong ◽  
Xuan jie Lin ◽  
Han tong Jiang ◽  
Li quan Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Adaptive Filtering ◽  
High Frequency ◽  
Remote Sensing Image ◽  
Aerial Image ◽  
Data Sets ◽  
Road Extraction ◽  
Image Labeling ◽  
Dilated Convolution ◽  
Segmentation Accuracy

It is difficult to segment small objects and the edge of the object because of larger-scale variation, larger intra-class variance of background and foreground-background imbalance in the remote sensing imagery. In convolutional neural networks, high frequency signals may degenerate into completely different ones after downsampling. We define this phenomenon as aliasing. Meanwhile, although dilated convolution can expand the receptive field of feature map, a much more complex background can cause serious alarms. To alleviate the above problems, we propose an attention-based mechanism adaptive filtered segmentation network. Experimental results on the Deepglobe Road Extraction dataset and Inria Aerial Image Labeling dataset showed that our method can effectively improve the segmentation accuracy. The F1 value on the two data sets reached 82.67% and 85.71% respectively.

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.

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