AN OBJECT-BASED WORKFLOW DEVELOPED TO EXTRACT AQUACULTURE PONDS FROM AIRBORNE LIDAR DATA: A TEST CASE IN CENTRAL VISAYAS, PHILIPPINES

Traditional remote sensing approach for mapping aquaculture ponds typically involves the use of aerial photography and high resolution images. The current study demonstrates the use of object-based image processing and analyses of LiDAR-data-generated derivative images with 1-meter resolution, namely: CHM (canopy height model) layer, DSM (digital surface model) layer, DTM (digital terrain model) layer, Hillshade layer, Intensity layer, NumRet (number of returns) layer, and Slope layer. A Canny edge detection algorithm was also performed on the Hillshade layer in order to create a new image (Canny layer) with more defined edges. These derivative images were then used as input layers to perform a multi-resolution segmentation algorithm best fit to delineate the aquaculture ponds. In order to extract the aquaculture pond feature, three major classes were identified for classification, including land, vegetation and water. Classification was first performed by using assign class algorithm to classify Flat Surfaces to segments with mean Slope values of 10 or lower. Out of these Flat Surfaces, assign class algorithm was then performed to determine Water feature by using a threshold value of 63.5. The segments identified as Water were then merged together to form larger bodies of water which comprises the aquaculture ponds. The present study shows that LiDAR data coupled with object-based classification can be an effective approach for mapping coastal aquaculture ponds. The workflow currently presented can be used as a model to map other areas in the Philippines where aquaculture ponds exist.

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A comparison of pixel-based decision tree and object-based Support Vector Machine methods for land-cover classification based on aerial images and airborne lidar data

International Journal of Remote Sensing ◽

10.1080/01431161.2017.1371864 ◽

2017 ◽

Vol 38 (23) ◽

pp. 7176-7195 ◽

Cited By ~ 12

Author(s):

Qiong Wu ◽

Ruofei Zhong ◽

Wenji Zhao ◽

Han Fu ◽

Kai Song

Keyword(s):

Support Vector Machine ◽

Land Cover ◽

Decision Tree ◽

Land Cover Classification ◽

Airborne Lidar ◽

Aerial Images ◽

Support Vector ◽

Lidar Data ◽

Object Based ◽

Airborne Lidar Data

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An Object-Based Method for Urban Land Cover Classification Using Airborne Lidar Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2014.2332337 ◽

2014 ◽

Vol 7 (10) ◽

pp. 4243-4254 ◽

Cited By ~ 21

Author(s):

Ziyue Chen ◽

Bingbo Gao

Keyword(s):

Land Cover ◽

Land Cover Classification ◽

Urban Land ◽

Airborne Lidar ◽

Lidar Data ◽

Urban Land Cover ◽

Object Based ◽

Airborne Lidar Data

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Feature assessment in object-based forest classification using airborne LiDAR data and high spatial resolution satellite imagery

2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA) ◽

10.1109/eorsa.2014.6927920 ◽

2014 ◽

Author(s):

Zhenyu Zhang ◽

Xiaoye Liu ◽

Wendy Wright

Keyword(s):

Spatial Resolution ◽

Satellite Imagery ◽

High Spatial Resolution ◽

Airborne Lidar ◽

Lidar Data ◽

Forest Classification ◽

Object Based ◽

Airborne Lidar Data

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Automatic Vehicle Extraction from Airborne LiDAR Data Using an Object-Based Point Cloud Analysis Method

Remote Sensing ◽

10.3390/rs6098405 ◽

2014 ◽

Vol 6 (9) ◽

pp. 8405-8423 ◽

Cited By ~ 19

Author(s):

Jixian Zhang ◽

Minyan Duan ◽

Qin Yan ◽

Xiangguo Lin

Keyword(s):

Point Cloud ◽

Airborne Lidar ◽

Lidar Data ◽

Analysis Method ◽

Object Based ◽

Airborne Lidar Data ◽

Point Cloud Analysis ◽

Cloud Analysis

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Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

Remote Sensing ◽

10.3390/rs12111702 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1702 ◽

Cited By ~ 2

Author(s):

Thanh Huy Nguyen ◽

Sylvie Daniel ◽

Didier Guériot ◽

Christophe Sintès ◽

Jean-Marc Le Caillec

Keyword(s):

Large Scale ◽

Active Contour Model ◽

Super Resolution ◽

Airborne Lidar ◽

Lidar Data ◽

Force Model ◽

Building Extraction ◽

Snake Model ◽

Object Based

Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since the mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. However, this task is still very challenging due to the complexity of building size, shape, and its surrounding environment. This complexity leads to a major obstacle for carrying out a reliable large-scale building extraction, since the involved prior information and assumptions on building such as shape, size, and color cannot be generalized over large areas. This paper presents an efficient snake model to overcome such a challenge, called Super-Resolution-based Snake Model (SRSM). The SRSM operates on high-resolution Light Detection and Ranging (LiDAR)-based elevation images—called z-images—generated by a super-resolution process applied to LiDAR data. The involved balloon force model is also improved to shrink or inflate adaptively, instead of inflating continuously. This method is applicable for a large scale such as city scale and even larger, while having a high level of automation and not requiring any prior knowledge nor training data from the urban scenes (hence unsupervised). It achieves high overall accuracy when tested on various datasets. For instance, the proposed SRSM yields an average area-based Quality of 86.57% and object-based Quality of 81.60% on the ISPRS Vaihingen benchmark datasets. Compared to other methods using this benchmark dataset, this level of accuracy is highly desirable even for a supervised method. Similarly desirable outcomes are obtained when carrying out the proposed SRSM on the whole City of Quebec (total area of 656 km2), yielding an area-based Quality of 62.37% and an object-based Quality of 63.21%.

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ROAD NETWORK MAPPING USING AIRBORNE LiDAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-707-2018 ◽

2018 ◽

Vol XLII-5 ◽

pp. 707-711 ◽

Cited By ~ 1

Author(s):

S. Upadhayay ◽

M. Yadav ◽

D. P. Singh

Keyword(s):

Road Network ◽

Spatial Databases ◽

Digital Terrain Model ◽

Airborne Lidar ◽

Surface Model ◽

Lidar Data ◽

Classification Approach ◽

Modified Maximum Likelihood ◽

Road Network Extraction ◽

Airborne Lidar Data

<p><strong>Abstract.</strong> The accurate, detailed and up-to-date road information is highly essential geo-spatial databases for transportation, smart city and other related applications. Thus, the main objective of this research is to develop an efficient algorithm for road network extraction from airborne LiDAR data using supervised classification approach. The proposed algorithm first classifies the input data into the road and non-road features using modified maximum likelihood classification approach. Then Digital Terrain Model (DTM) mask is generated by removing non-ground features from Digital Surface Model using hierarchical morphology and road candidate image if obtained. The parking lots are removed and road network is extracted successfully.</p>

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EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b7-747-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 747-751

Author(s):

N. Demir ◽

M. Kaynarca ◽

S. Oy

Keyword(s):

Standard Deviation ◽

Optical Sensors ◽

Land Surface ◽

Airborne Lidar ◽

Surface Model ◽

Lidar Data ◽

Sar Image ◽

Fuzzy Approach ◽

The Mean

Coastlines are important features for water resources, sea products, energy resources etc. Coastlines are changed dynamically, thus automated methods are necessary for analysing and detecting the changes along the coastlines. In this study, Sentinel-1 C band SAR image has been used to extract the coastline with fuzzy logic approach. The used SAR image has VH polarisation and 10x10m. spatial resolution, covers 57 sqkm area from the south-east of Puerto-Rico. Additionally, radiometric calibration is applied to reduce atmospheric and orbit error, and speckle filter is used to reduce the noise. Then the image is terrain-corrected using SRTM digital surface model. Classification of SAR image is a challenging task since SAR and optical sensors have very different properties. Even between different bands of the SAR sensors, the images look very different. So, the classification of SAR image is difficult with the traditional unsupervised methods. In this study, a fuzzy approach has been applied to distinguish the coastal pixels than the land surface pixels. The standard deviation and the mean, median values are calculated to use as parameters in fuzzy approach. The Mean-standard-deviation (MS) Large membership function is used because the large amounts of land and ocean pixels dominate the SAR image with large mean and standard deviation values. The pixel values are multiplied with 1000 to easify the calculations. The mean is calculated as 23 and the standard deviation is calculated as 12 for the whole image. The multiplier parameters are selected as a: 0.58, b: 0.05 to maximize the land surface membership. The result is evaluated using airborne LIDAR data, only for the areas where LIDAR dataset is available and secondly manually digitized coastline. The laser points which are below 0,5 m are classified as the ocean points. The 3D alpha-shapes algorithm is used to detect the coastline points from LIDAR data. Minimum distances are calculated between the LIDAR points of coastline with the extracted coastline. The statistics of the distances are calculated as following; the mean is 5.82m, standard deviation is 5.83m and the median value is 4.08 m. Secondly, the extracted coastline is also evaluated with manually created lines on SAR image. Both lines are converted to dense points with 1 m interval. Then the closest distances are calculated between the points from extracted coastline and manually created coastline. The mean is 5.23m, standard deviation is 4.52m. and the median value is 4.13m for the calculated distances. The evaluation values are within the accuracy of used SAR data for both quality assessment approaches.

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Hierarchical Geographic Object-Based Vegetation Type Extraction Based on Multi-Source Remote Sensing Data

Forests ◽

10.3390/f11121271 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1271

Author(s):

Xuegang Mao ◽

Yueqing Deng ◽

Liang Zhu ◽

Yao Yao

Keyword(s):

Remote Sensing ◽

Forest Type ◽

Vegetation Type ◽

Remote Sensing Data ◽

Airborne Lidar ◽

Lidar Data ◽

Sensing Data ◽

Object Based ◽

Four Levels ◽

Airborne Lidar Data

Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction.

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