Segment Optimization and Data-Driven Thresholding for Knowledge-Based Landslide Detection by Object-Based Image Analysis

By Tapas Ranjan Martha, Norman Kerle, Cees J. van Westen, Victor G. Jetten, K. Vinod Kumar. 2011

304 citations recorded by the source corpus.

Subfields: Environmental Engineering, Management, Monitoring, Policy and Law, Media Technology, Remote Sensing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Gong Cheng
2. Guisong Xia
3. Junwei Han

In Management, Monitoring, Policy and Law

1. Peicheng Zhou
2. Gong Cheng
3. Junwei Han

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Peicheng Zhou
4. Guisong Xia
5. Lei Guo
6. Xuelong Li
7. Jocelyn Chanussot
8. Qi Wang

In Remote Sensing

1. Peicheng Zhou
2. Gong Cheng
3. Junwei Han
4. Lei Guo
5. Qi Wang
6. Jocelyn Chanussot
7. Shaoteng Liu
8. Guisong Xia

Citations in the corpus

• Scalable multi-class geospatial object detection in high-spatial-resolution remote sensing images (2014, methodology): “A number of recent works in literatures have proposed various methods for different object detection tasks [1-10].”
• Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, background): “Automatically selecting the most appropriate parameters for object extraction (Drǎgut et al., 2010; Martha et al., 2011; Smith, 2010), defining standard framework for object comparison for change detection, and the accuracy assessment for change detection results are among most prominent (Albrecht…”
• Strathprints Institutional Repository Shuhui and Ren, Jinchang (2015) Effective and Efficient Midlevel Visual Elements-oriented Land-use Classification Using Vhr Remote Sensing Images. Ieee Transactions on Geoscience and Remote Sensing, 53 (8). Pp. 4238-4249. Issn 0196-2892 Effective and Efficient M (unknown): “Land-use classification plays an important role for a wide range of applications, such as natural geological hazards detection [1] [2], general land-use/land-cover (LULC) determination[3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17], vegetation types mapping [18], environment…”
• A Survey on Object Detection in Optical Remote Sensing Images (2016, unknown): “…1997; Weidner and Förstner, 1995); channel (Tchoku et al., 1996); crops (Janssen and Middelkoop, 1992); drainage forest (Solberg, 1999); landslide (Martha et al., 2011); road (Hu et al., 2007; Leninisha and Vani, 2015; Maillard and Cavayas, 1989; Trinder and Wang, 1998; Wang and Newkirk, 1988;…”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “the last few decades, extensive researches on remote sensing image scene classification have been undertaken driven by its real-world applications, such as urban planning [5], [6], natural hazards detection [7]–[9], environment monitoring [10]–[12], vegetation mapping [13], [14], and geospatial obje”
• Scene Classification With Recurrent Attention of VHR Remote Sensing Images (2019, background): “increasingly available and bringing us the opportunity to try more research in military and civilian applications, such as natural disaster detection [3], [4], land-cover/land-use classification [5], [6] geographic space object detection [7], [8], geographic image retrieval [9], [10], urban planning”