Unsupervised Change Detection for Very High-spatial Resolution Satellite Imagery by Using Object-based IR-MAD Algorithm

Very high spatial resolution commercial satellite imagery can inform observation, mapping, and documentation of micro-topographic transitions across large tundra regions. The bridging of fine-scale field studies with pan-Arctic system assessments has until now been constrained by a lack of overlap in spatial resolution and geographical coverage. This likely introduced biases in climate impacts on, and feedback from the Arctic region to the global climate system. The central objective of this exploratory study is to develop an object-based image analysis workflow to automatically extract ice-wedge polygon troughs from very high spatial resolution commercial satellite imagery. We employed a systematic experiment to understand the degree of interoperability of knowledge-based workflows across distinct tundra vegetation units—sedge tundra and tussock tundra—focusing on the same semantic class. In our multi-scale trough modelling workflow, we coupled mathematical morphological filtering with a segmentation process to enhance the quality of image object candidates and classification accuracies. Employment of the master ruleset on sedge tundra reported classification accuracies of correctness of 0.99, completeness of 0.87, and F1 score of 0.92. When the master ruleset was applied to tussock tundra without any adaptations, classification accuracies remained promising while reporting correctness of 0.87, completeness of 0.77, and an F1 score of 0.81. Overall, results suggest that the object-based image analysis-based trough modelling workflow exhibits substantial interoperability across the terrain while producing promising classification accuracies. From an Arctic earth science perspective, the mapped troughs combined with the ArcticDEM can allow hydrological assessments of lateral connectivity of the rapidly changing Arctic tundra landscape, and repeated mapping can allow us to track fine-scale changes across large regions and that has potentially major implications on larger riverine systems.

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Cropland Extraction from Very High Spatial Resolution Satellite Imagery by Object-Based Classification Using Improved Mean Shift and One-Class Support Vector Machines

Sensor Letters ◽

10.1166/sl.2011.1361 ◽

2011 ◽

Vol 9 (3) ◽

pp. 997-1005 ◽

Cited By ~ 6

Author(s):

Jing Shen ◽

Jiping Liu ◽

Xiangguo Lin ◽

Rong Zhao ◽

Shenghua Xu

Keyword(s):

Support Vector Machines ◽

Spatial Resolution ◽

Satellite Imagery ◽

High Spatial Resolution ◽

Mean Shift ◽

Support Vector ◽

Object Based ◽

Vector Machines ◽

Very High Spatial Resolution ◽

Very High

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Pulse Coupled Neural Networks for Automatic Urban Change Detection at Very High Spatial Resolution

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-642-10268-4_109 ◽

2009 ◽

pp. 929-942 ◽

Cited By ~ 2

Author(s):

Fabio Pacifici ◽

William J. Emery

Keyword(s):

Neural Networks ◽

Change Detection ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Urban Change ◽

Coupled Neural Networks ◽

Very High Spatial Resolution ◽

Pulse Coupled Neural Networks ◽

Very High

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Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2009.10.002 ◽

2010 ◽

Vol 65 (1) ◽

pp. 143-153 ◽

Cited By ~ 105

Author(s):

Mourad Bouziani ◽

Kalifa Goïta ◽

Dong-Chen He

Keyword(s):

Change Detection ◽

Urban Environment ◽

Prior Knowledge ◽

Spatial Resolution ◽

High Spatial Resolution ◽

Very High Spatial Resolution ◽

Very High

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Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

Remote Sensing ◽

10.3390/rs12183092 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3092 ◽

Cited By ~ 1

Author(s):

Mathieu Varin ◽

Bilel Chalghaf ◽

Gilles Joanisse

Keyword(s):

Spatial Resolution ◽

Tree Species ◽

High Spatial Resolution ◽

Individual Species ◽

Global Approach ◽

Conifer Species ◽

Object Based ◽

Very High Spatial Resolution ◽

Very High ◽

Tree Type

Species identification in Quebec, Canada, is usually performed with photo-interpretation at the stand level, and often results in a lack of precision which affects forest management. Very high spatial resolution imagery, such as WorldView-3 and Light Detection and Ranging have the potential to overcome this issue. The main objective of this study is to map 11 tree species at the tree level using an object-based approach. For modeling, 240 variables were derived from WorldView-3 with pixel-based and arithmetic feature calculation techniques. A global approach (11 species) was compared to a hierarchical approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were compared: support vector machine, classification and regression tree, random forest (RF), k-nearest neighbors, and linear discriminant analysis. Each model was assessed using 16-band or first 8-band derived variables, with the results indicating higher precision for the RF technique. Higher accuracies were found using 16-band instead of 8-band derived variables for the global approach (overall accuracy (OA): 75% vs. 71%, Kappa index of agreement (KIA): 0.72 vs. 0.67) and tree type level (OA: 99% vs. 97%, KIA: 0.97 vs. 0.95). For broadleaf individual species, higher accuracy was found using first 8-band derived variables (OA: 70% vs. 68%, KIA: 0.63 vs. 0.60). No distinction was found for individual conifer species (OA: 94%, KIA: 0.93). This paper demonstrates that a hierarchical classification approach gives better results for conifer species and that using an 8-band WorldView-3 instead of a 16-band is sufficient.

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