Very High Resolution Multiangle Urban Classification Analysis

By Nathan Longbotham, Chuck Chaapel, Laurence Bleiler, Christopher Padwick, William J. Emery, Fabio Pacifici. 2012

171 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, 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 Atmospheric Science

1. Devis Tuia
2. Gustau Camps-Valls
3. Gong Cheng
4. Junwei Han
5. Luis Gómez-Chova
6. Qian Du
7. Lei Guo
8. Saurabh Prasad

In Global and Planetary Change

1. Gustau Camps-Valls
2. Gong Cheng
3. Junwei Han

In Media Technology

1. Gong Cheng
2. Junwei Han
3. Guisong Xia
4. Devis Tuia
5. Lei Guo
6. Jocelyn Chanussot
7. Gustau Camps-Valls
8. Jon Atli Benediktsson

In Remote Sensing

1. Gong Cheng
2. Junwei Han
3. Lei Guo
4. Gustau Camps-Valls
5. Luis Gómez-Chova
6. Qian Du
7. Gabriele Moser
8. Devis Tuia

Citations in the corpus

• Multimodal Classification of Remote Sensing Images: A Review and Future Directions (2015, background): “The first one is to consider the angular images as different views of the same objects, and exploit differences in the angular sequence as additional features [56] that convey information about, for example, the lateral sides of the objects (strong off-nadir angles), roof materials (nadir view) [57]”
• Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest (2014, background): “As indicated by various studies (e.g., [51]–[56]), Random Forests have emerged as a powerful approach with performance similar to boosting [57], [58], SVMs [16], [59], [60], and Neural Networks [61]–[63].”
• Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities (2020, background): “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 object detection [15]–[22].”
• 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): “Most previous works [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] mainly focus on classifying pixels or objects (or rather, grouping of local homogeneous pixels) into their thematic class by extracting low-level image features (e.g. texture feature [9] [10], color feature [8], spatial and…”
• Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM (2014, background): “VHR images have the advantage of providing pixels with meter or even submeter geometrical resolution (ground sample distance) and permit to observe fine objects in urban environments, such as details on buildings or cars, with enhanced precision in their spatial description [1]–[5].”
• Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters (2018, unknown): “Remote sensing images with very high resolution (VHR) are widely used in many applications including land cover mapping and monitoring [1], multi-angle urban classification analysis [2], automatic road detection [3], as well as the identification of tree species in forest management [4].”