Deep Semantic Segmentation of Trees Using Multispectral Images

Ulku, Irem
Akagündüz, Erdem
Ghamisi, Pedram
Forests can be efficiently monitored by automatic semantic segmentation of trees using satellite and/or aerial images. Still, several challenges can make the problem difficult, including the varying spectral signature of different trees, lack of sufficient labelled data, and geometrical occlusions. In this article, we address the tree segmentation problem using multispectral imagery. While we carry out large-scale experiments on several deep learning architectures using various spectral input combinations, we also attempt to explore whether hand-crafted spectral vegetation indices can improve the performance of deep learning models in the segmentation of trees. Our experiments include benchmarking a variety of multispectral remote sensing image sets, deep semantic segmentation architectures, and various spectral bands as inputs, including a number of hand-crafted spectral vegetation indices. From our large-scale experiments, we draw several useful conclusions. One particularly important conclusion is that, with no additional computation burden, combining different categories of multispectral vegetation indices, such as NVDI, atmospherically resistant vegetation index, and soil-adjusted vegetation index, within a single three-channel input, and using the state-of-the-art semantic segmentation architectures, tree segmentation accuracy can be improved under certain conditions, compared to using high-resolution visible and/or near-infrared input.


Fuzzy spatial data cube construction and its use in association rule mining
Işık, Narin; Yazıcı, Adnan; Department of Computer Engineering (2005)
The popularity of spatial databases increases since the amount of the spatial data that need to be handled has increased by the use of digital maps, images from satellites, video cameras, medical equipment, sensor networks, etc. Spatial data are difficult to examine and extract interesting knowledge; hence, applications that assist decision-making about spatial data like weather forecasting, traffic supervision, mobile communication, etc. have been introduced. In this thesis, more natural and precise knowle...
Extending Correlation Filter-Based Visual Tracking by Tree-Structured Ensemble and Spatial Windowing
Gundogdu, Erhan; Özkan, Huseyin; Alatan, Abdullah Aydın (Institute of Electrical and Electronics Engineers (IEEE), 2017-11-01)
Correlation filters have been successfully used in visual tracking due to their modeling power and computational efficiency. However, the state-of-the-art correlation filter-based (CFB) tracking algorithms tend to quickly discard the previous poses of the target, since they consider only a single filter in their models. On the contrary, our approach is to register multiple CFB trackers for previous poses and exploit the registered knowledge when an appearance change occurs. To this end, we propose a novel t...
Automatic Mapping of Linearwoody Vegetation Features in Agricultural Landscapes
AKSOY, SELİM; AKÇAY, HÜSEYİN GÖKHAN; Cinbiş, Ramazan Gökberk; Wassenaar, Tom (2008-07-11)
Development of automatic methods for agricultural mapping and monitoring using remotely sensed imagery has been an important research problem. We describe algorithms that exploit the spectral, textural and object shape information using hierarchical feature extraction and decision making steps for automatic mapping of linear strips of woody vegetation in very high-resolution imagery. First, combinations of multispectral values and multi-scale Gabor and entropy texture features are used for training pixel le...
A multimodal approach for individual tracking of people and their belongings
Beyan, Çiğdem; Temizel, Alptekin (2015-04-01)
In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in...
Developing an integrated system for semi-automated segmentation of remotely sensed imagery
Kök, Emre Hamit; Türker, Mustafa; Department of Geodetic and Geographical Information Technologies (2005)
Classification of the agricultural fields using remote sensing images is one of the most popular methods used for crop mapping. Most recent classification techniques are based on per-field approach that works as assigning a crop label for each field. Commonly, the spatial vector data is used for the boundaries of the fields for applying the classification within them. However, crop variation within the fields is a very common problem. In this case, the existing field boundaries may be insufficient for perfo...
Citation Formats
I. Ulku, E. Akagündüz, and P. Ghamisi, “Deep Semantic Segmentation of Trees Using Multispectral Images,” IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, vol. 15, pp. 7589–7604, 2022, Accessed: 00, 2022. [Online]. Available: