scholarly journals Optimizing the Segmentation of a High-Resolution Image by Using a Local Scale Parameter

2021 ◽  
Vol 87 (7) ◽  
pp. 503-511
Author(s):  
Lei Zhang ◽  
Hongchao Liu ◽  
Xiaosong Li ◽  
Xinyu Qian
Keyword(s):  
Land Cover ◽  
Scale Parameter ◽  
Single Layer ◽  
Remote Sensing Data ◽  
Scale Parameters ◽  
Object Based ◽  
Segmentation Approach ◽  
Optimal Segmentation ◽  
Segment Matching

Image segmentation is a critical procedure in object-based identification and classification of remote sensing data. However, optimal scale-parameter selection presents a challenge, given the presence of complex landscapes and uncertain feature changes. This study proposes a local optimal segmentation approach that considers both intersegment heterogeneity and intrasegment homogeneity, uses the standard deviation and local Moran's index to explore each optimal segment across different scale parameters, and combines the optimal segments into a single layer. The optimal segment is measured by using high-spatial-resolution images. Results show that our approach out-performs and generates less error than the global optimal segmentation approach. The variety of land cover types or intrasegment homogeneity leads to segment matching with the geo-objects on different scales. Local optimal segmentation demonstrates sensitivity to land cover discrepancy and provides good performance on cross-scale segmentation.

scholarly journals Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

2019 ◽  
Vol 12 (1) ◽  
pp. 96 ◽  
Author(s):  
James Brinkhoff ◽  
Justin Vardanega ◽  
Andrew J. Robson
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Satellite Image ◽  
Resource Planning ◽  
Support Vector ◽  
Perennial Crop ◽  
Perennial Crops ◽  
Object Based ◽  
Rbf Kernel

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

scholarly journals Exploration of Semantic Geo-Object Recognition Based on the Scale Parameter Optimization Method for Remote Sensing Images

2021 ◽  
Vol 10 (6) ◽  
pp. 420
Author(s):  
Jun Wang ◽  
Lili Jiang ◽  
Qingwen Qi ◽  
Yongji Wang
Keyword(s):  
Parameter Optimization ◽  
Scale Parameter ◽  
Human Perception ◽  
Optimization Methods ◽  
Optimization Method ◽  
Initiation Point ◽  
Scale Parameters ◽  
Object Based ◽  
Geographic Object ◽  
Multiresolution Segmentation

Image segmentation is of significance because it can provide objects that are the minimum analysis units for geographic object-based image analysis (GEOBIA). Most segmentation methods usually set parameters to identify geo-objects, and different parameter settings lead to different segmentation results; thus, parameter optimization is critical to obtain satisfactory segmentation results. Currently, many parameter optimization methods have been developed and successfully applied to the identification of single geo-objects. However, few studies have focused on the recognition of the union of different types of geo-objects (semantic geo-objects), such as a park. The recognition of semantic geo-objects is likely more crucial than that of single geo-objects because the former type of recognition is more correlated with the human perception. This paper proposes an approach to recognize semantic geo-objects. The key concept is that a single geo-object is the smallest component unit of a semantic geo-object, and semantic geo-objects are recognized by iteratively merging single geo-objects. Thus, the optimal scale of the semantic geo-objects is determined by iteratively recognizing the optimal scales of single geo-objects and using them as the initiation point of the reset scale parameter optimization interval. In this paper, we adopt the multiresolution segmentation (MRS) method to segment Gaofen-1 images and tested three scale parameter optimization methods to validate the proposed approach. The results show that the proposed approach can determine the scale parameters, which can produce semantic geo-objects.

scholarly journals Histogram Curve Matching Approaches for Object-based Image Classification of Land Cover and Land Use

2013 ◽  
Vol 79 (5) ◽  
pp. 433-440 ◽  
Author(s):  
Sory I. Toure ◽  
Douglas A. Stow ◽  
John R. Weeks ◽  
Sunil Kumar
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Image Classification ◽  
Curve Matching ◽  
Object Based

scholarly journals OBJECT-BASED ANALYSIS OF LIDAR GEOMETRIC FEATURES FOR VEGETATION DETECTION IN SHADED AREAS

2016 ◽  
Vol XLI-B1 ◽  
pp. 43-46
Author(s):  
Yu-Ching Lin ◽  
ChinSu Lin ◽  
Ming-Da Tsai ◽  
Chun-Lin Lin
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Surface Characteristics ◽  
Point Clouds ◽  
Unit Weight ◽  
Waveform Data ◽  
Remote Sensing Technique ◽  
Object Based

The extraction of land cover information from remote sensing data is a complex process. Spectral information has been widely utilized in classifying remote sensing images. However, shadows limit the use of multispectral images because they result in loss of spectral radiometric information. In addition, true reflectance may be underestimated in shaded areas. In land cover classification, shaded areas are often left unclassified or simply assigned as a shadow class. Vegetation indices from remote sensing measurement are radiation-based measurements computed through spectral combination. They indicate vegetation properties and play an important role in remote sensing of forests. Airborne light detection and ranging (LiDAR) technology is an active remote sensing technique that produces a true orthophoto at a single wavelength. This study investigated three types of geometric lidar features where NDVI values fail to represent meaningful forest information. The three features include echo width, normalized eigenvalue, and standard deviation of the unit weight observation of the plane adjustment, and they can be derived from waveform data and discrete point clouds. Various feature combinations were carried out to evaluate the compensation of the three lidar features to vegetation detection in shaded areas. Echo width was found to outperform the other two features. Furthermore, surface characteristics estimated by echo width were similar to that by normalized eigenvalues. Compared to the combination of only NDVI and mean height difference, those including one of the three features had a positive effect on the detection of vegetation class.

scholarly journals Classification of Land Cover by Using Aerial Photo At CV. Alaska Prima Coal, Cooling Village, Sanga-Sanga Sub-district, Kutai Kartanegara District, East Kalimantan Province

AGRIFOR ◽  
2018 ◽  
Vol 17 (1) ◽  
pp. 1
Author(s):  
Agus Sofyan
Keyword(s):  
Image Analysis ◽  
Land Cover ◽  
Aerial Photo ◽  
Recording Time ◽  
Object Based Image Analysis ◽  
Optical Sensitivity ◽  
East Kalimantan ◽  
Object Based ◽  
Open Land

Remote sensing can be done visually and digitally. one of the advantages of airborne photography data generated by drone (phantom-3) compared to satellite imagery with optical sensitivity is its ability to obtain cloud-free images and freedom of recording time and the displayed area shows clearly defined objects corresponding to land cover. characteristics. To limit the object-based area of this research method applied is Object Based Image Analysis (OBIA).This study aims to classify land cover using highly resolved aerial photography with the help of Object Based Image Analysis (OBIA) technique and calculate the accuracy and accuracy, land cover classification by using Objeck Based Image (OBIA) analysis through examination of field conditions.classifying land cover, the classification includes shrubs, young shrubs, plantations (oil palms), shrubs, mines, open land, roads and water bodies with Accuracy of Overcome 0.86.

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