An object-oriented daytime land-fog-detection approach based on the mean-shift and full lambda-schedule algorithms using EOS/MODIS data

Liu, Liangming; Wen, Xin; González, Albano; Tan, Debao; Du, Juan; Liang, Yonghui; Li, Wei; Fan, Ding; Sun, Kaimin; Dong, Pei; Xiang, Daxiang; Zhou, Zheng

doi:10.1080/01431161.2010.489067

Cited by 17 publications

(9 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image segmentation is the first step and a necessary prerequisite for GEOBIA, and accurate segmentation can improve the performance of many subsequent ecological applications, such as oil spill detection [13], cloud extraction [16], land-fog detection [48] and road detection [51]. The hybrid segmentation, which combines splitting with merging, is a new trend for obtaining good segments [5], because the method considers both the boundary information and spatial information between adjacent geo-objects [29].…”

Section: Discussionmentioning

confidence: 99%

“…To assess the effectiveness of our OHRH method, we first implemented a modified version of our merging method by only considering between-segment heterogeneity (i.e., OH method). Then, to compare both of our methods in this study to one of these other methods considering between-segment heterogeneity, the full lambda-schedule algorithm (FLSA) [34,48] was also implemented, for which the Euclidean distance is used to quantify the spectral distance between adjacent segments. Furthermore, in this paper, the same initial segments are used for all three methods.…”

Section: Region Merging Based On MC Considering Between-segment Hetermentioning

confidence: 99%

See 1 more Smart Citation

Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method

Wang

Meng

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Image segmentation is an important process and a prerequisite for object-based image analysis, but segmenting an image into meaningful geo-objects is a challenging problem. Recently, some scholars have focused on hybrid methods that employ initial segmentation and subsequent region merging since hybrid methods consider both boundary and spatial information. However, the existing merging criteria (MC) only consider the heterogeneity between adjacent segments to calculate the merging cost of adjacent segments, thus limiting the goodness-of-fit between segments and geo-objects because the homogeneity within segments and the heterogeneity between segments should be treated equally. To overcome this limitation, in this paper a hybrid remote-sensing image segmentation method is employed that considers the objective heterogeneity and relative homogeneity (OHRH) for MC during region merging. In this paper, the OHRH method is implemented in five different study areas and then compared to our region merging method using the objective heterogeneity (OH) method, as well as the full lambda-schedule algorithm (FLSA). The unsupervised evaluation indicated that the OHRH method was more accurate than the OH and FLSA methods, and the visual results showed that the OHRH method could distinguish both small and large geo-objects. The segments showed greater size changes than those of the other methods, demonstrating the superiority of considering within-and between-segment heterogeneity in the OHRH method.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Region Merging Based On MC Considering Between-segment Hetermentioning

confidence: 99%

Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method

Wang

Meng

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…By summarizing previous literature [15,[25][26][27][28][29][30][31][32] and references to pixel-based land use change detection methods, we can conclude that two approaches to obtain change information through the object-oriented technique generally exist. One is classifying first and then comparing the classification results, which is a technique known as detection after classification (DAC).…”

mentioning

confidence: 90%

When to Detect Changes in Object-Based Image Analysis: Before or after Classification?

Luo¹,

F²,

Jp³

2017

Preprint

View full text Add to dashboard Cite

This study compared two object-oriented land use change detection methods-detection after classification (DAC) and classification after detection (CAD) -based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.Environmental monitoring and natural resources management require the considerably urgent development of operational solutions that can extract tangible information from remote sensing data [1]. Satellite remote sensing has been applied in the mapping of land use since the 1970s, and nearly all types of sensors have been studied [2,[3][4][5]. Different types of sensors have different advantages;Landsat images are extensively used in obtaining free and large swath, especially in large scale [6][7][8][9].However, while the need for information continues to grow, only a small fraction of stored information in satellite data is often tapped. Transitioning magnanimity data into robust information has never been so important.Change detection, which is the process of identifying change in an area of an object or a phenomenon by observing it at different times, is an important way of obtaining information using the remote sensing technology [10]. The process uses multitemporal datasets to qualitatively analyze the temporal effects of objects or phenomena and quantify changes [11]. Land use/land cover change Preprints (www.preprints.org) | NOT PEER-REVIEWED |

show abstract

“…Based on the Environment for Visualizing Images (ENVI) software, the edge-based segmentation method is used to produce a preliminary segmentation image that is based on the Google Earth image. To avoid over-segmentation, a full lambda-schedule algorithm [35] is utilized to merge small segments in the textured areas of the preliminary segmentation image [36]. As an indicator of segments' integrated degree, a merge scale is applied to determine how similar the nearby small segments are in order to merge them into one new bigger segment.…”

Section: Image Segmentationmentioning

confidence: 99%

DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image

Huang

Chen

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Implying the prosperity and development of the city, impervious surface area (ISA) is playing an increasingly important role in ecological processes, microclimate, material and energy flows, and urban flood. The free sub-meter resolution Google Earth image, which is integrated by several high spatial resolution data, appears to have potential for high-resolution ISA extraction, where present study is rare and performances remain to be improved. Due to the high spatial and spectral variation of the urban environment as well as confusion between ISA and soil, the accurate delineating of ISA with traditional (direct) methods can be costly and time-consuming, which is in a word resource-intensive. However, this paper presents a novel indirect ISA extraction conceptual model and a new detecting and masking background land cover (DMBLC) approach that: uses a freely available, high-resolution dataset; requires a reduced set of training samples; and consists of relatively simple, common, and feasible image processing steps. The key characteristic of DMBLC is to detect the background of ISA (vegetation, soil, and water) accurately and obtain the ISA by masking the background. The approach relies on background detection to avoid the predicaments of direct ISA extraction. Water can be directly gained by water body vector data, in DMBLC; mixture tuned matched filtering (MTMF) is exploited to detect vegetation and soil, image segmentation is used to mitigate the spectral variation problem within the same land cover, and segment rectangularity reduces the confusion between ISA and soil. From experiments in a core area of Fuzhou, China, the DMBLC approach reached high performance and outperformed the powerful traditional support vector machines (SVM) method (overall accuracy of 94.45% and Kappa coefficient of 0.8885, compared to 86.44% and 0.7329, respectively). From the comparison of different levels of complexity within the inner processing steps, it is confirmed that the DMBLC approach is a powerful and flexible changed framework for indirect ISA extraction, which can be improved by using more advanced inner methods.

show abstract

An object-oriented daytime land-fog-detection approach based on the mean-shift and full lambda-schedule algorithms using EOS/MODIS data

Cited by 17 publications

References 9 publications

Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method

Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method

When to Detect Changes in Object-Based Image Analysis: Before or after Classification?

DMBLC: An Indirect Urban Impervious Surface Area Extraction Approach by Detecting and Masking Background Land Cover on Google Earth Image

Contact Info

Product

Resources

About