17 research outputs found
Estimation of the Handwritten Text Skew Based on Binary Moments
Binary moments represent one of the methods for the text skew estimation in binary images. It has been used widely for the skew identification of the printed text. However, the handwritten text consists of text objects, which are characterized with different skews. Hence, the method should be adapted for the handwritten text. This is achieved with the image splitting into separate text objects made by the bounding boxes. Obtained text objects represent the isolated binary objects. The application of the moment-based method to each binary object evaluates their local text skews. Due to the accuracy, estimated skew data can be used as an input to the algorithms for the text line segmentation
Semantic segmentation of airborne laser scanning point clouds using machine learning methods
Π’Π΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ° Π»Π°ΡΠ΅ΡΡΠΊΠΎΠ³ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ° (Π΅Π½Π³Π». Light Detection and Ranging β LiDAR) ΠΏΠΎΠΊΠ°Π·Π°Π»Π° ΡΠ΅ ΠΊΠ°ΠΎ Π²Π΅ΠΎΠΌΠ° ΡΡΠΏΠ΅ΡΠ½Π° Π·Π° Π±ΡΠ·ΠΎ ΠΏΡΠΈΠΊΡΠΏΡΠ°ΡΠ΅ ΠΌΠ°ΡΠΎΠ²Π½Π΅ ΠΊΠΎΠ»ΠΈΡΠΈΠ½Π΅ ΠΏΡΠΎΡΡΠΎΡΠ½ΠΈΡ
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΎ ΡΠΎΠΏΠΎΠ³ΡΠ°ΡΠΈΡΠΈ ΡΠΈΠ·ΠΈΡΠΊΠ΅ ΠΏΠΎΠ²ΡΡΠΈ ΠΠ΅ΠΌΡΠ΅. Π‘Π΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ° ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎΠ³ Π»Π°ΡΠ΅ΡΡΠΊΠΈΠΌ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ΅ΠΌ ΠΈΠ· Π²Π°Π·Π΄ΡΡ
Π° (Π΅Π½Π³Π». Airborne Laser Scanning β ALS) ΠΊΠΎΡΠ° ΡΠ΅ ΡΠ°ΠΊΠΎΡΠ΅ Π½Π°Π·ΠΈΠ²Π° ΠΈ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡaΠΊΠ°, ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠΎ ΠΎΠ·Π½Π°ΡΠ°Π²Π°ΡΠ΅ ΠΊΠ°ΠΎ ΠΈ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ° ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ°, ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° Π²Π΅Π»ΠΈΠΊΠΈ ΠΈΠ·Π°Π·ΠΎΠ² Π·Π±ΠΎΠ³ ΡΡΡΡΠΊΡΡΡΠ΅ ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° ΠΈ ΡΠΈΠΏΠΎΠ²Π° ΠΊΠ»Π°ΡΠ° ΠΊΠΎΡΠ΅ ΡΠ΅ ΠΌΠΎΠ³Ρ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠΎΠ²Π°ΡΠΈ Ρ ΡΠΎΠΌ ΠΏΡΠΎΡΡΠΎΡΡ. ΠΠ°ΡΠΈΠ½ΡΠΊΠΎ ΡΡΠ΅ΡΠ΅, ΡΠ° Π΄ΡΡΠ³Π΅ ΡΡΡΠ°Π½Π΅, ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° ΠΌΠΎΡΠ°Π½ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠΊΠΈ Π°ΠΏΠ°ΡΠ°Ρ ΠΊΠΎΡΠΈ ΡΠ΅ ΠΌΠΎΠΆΠ΅ ΠΈΡΠΊΠΎΡΠΈΡΡΠΈΡΠΈ Π·Π° ΡΠ°Π·Π»ΠΈΡΠΈΡΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅ ΡΠΊΡΡΡΡΡΡΡΠΈ ΠΈ Π½Π°Π²Π΅Π΄Π΅Π½Ρ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ. Π£ ΠΎΠ²ΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΡΡ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΠ°Π½Π΅ ΠΌΠ΅ΡΠΎΠ΄Π΅ ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ° ΠΊΠΎΡΠΈΠΌ ΡΠ΅ Π΄ΠΎΠ±ΠΈΡΠ°ΡΡ Π½Π°ΡΠ±ΠΎΡΠΈ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ΅ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ΅ ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ°, ΠΏΠΎΠ³ΠΎΡoΠ²ΠΎ ΡΠ° ΡΠ»ΠΎΠΆΠ΅Π½ΠΈΠΌ Π°Π½ΡΠ°ΠΌΠ±Π» ΠΌΠΎΠ΄Π΅Π»ΠΈΠΌΠ° ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ° ΠΊΠΎΠ½ΡΡΡΡΠΈΡΠ°Π½ΠΈΠΌ ΡΠ»Π°Π³Π°ΡΠ΅ΠΌ Π²ΠΈΡΠ΅ ΠΎΡΠ½ΠΎΠ²Π½ΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π° ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ°. Π£ ΠΎΠ²ΠΎΠΌ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΡ ΡΠ΅ Π²ΡΡΠ΅Π½ΠΎ ΠΈ Π±Π°Π»Π°Π½ΡΠΈΡΠ°ΡΠ΅ ΡΠΊΡΠΏΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΡΠΈΠ½ΡΠ΅ΡΠΈΡΠΊΠΈΠΌ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΠ΅ΠΌ ΡΠ°ΡΠ°ΠΊΠ° ΠΊΠΎΡΠ΅ ΠΏΡΠΈΠΏΠ°Π΄Π°ΡΡ ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΈΠΌ ΠΊΠ»Π°ΡΠ°ΠΌΠ° Π΄ΠΎΠΊ ΡΡ ΡΠ°ΡΠΊΠ΅ ΠΊΠΎΡΠ΅ ΠΏΡΠΈΠΏΠ°Π΄Π°ΡΡ Π²Π΅ΡΠΈΠ½ΡΠΊΠΈΠΌ ΠΊΠ»Π°ΡΠ°ΠΌΠ° Π·Π½Π°ΡΠ½ΠΎ ΡΠ΅Π΄ΡΠΊΠΎΠ²Π°Π½Π΅. ΠΡΡΠ΅Π½e ΡΡ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠΈΠΏΠ° ΠΏΡΠ΅ΡΡΠ°Π³Π΅ ΡΠ°ΡΠ°ΠΊΠ° ΡΡΡΠ΅Π΄ΡΡΠ²Π° ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΡΡΠΈΡΠ°ΡΠ° Π²Π΅Π»ΠΈΡΠΈΠ½Π΅ ΠΏΠΎΠ»ΡΠΏΡΠ΅ΡΠ½ΠΈΠΊΠ° ΠΏΡΠ΅ΡΡΠ°Π³Π΅, Π° ΠΈΡΠΏΠΈΡΠ°Π½Π° ΡΠ΅ ΠΈ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ Π²ΠΈΡΠ΅ΡΠ°Π·ΠΌΠ΅ΡΠ½ΠΎΠ³ (Π΅Π½Π³Π». multiscale) ΠΏΡΠΈΡΡΡΠΏΠ° ΠΏΡΠ΅ΡΡΠ°Π³Π΅ Ρ ΡΠΈΡΡ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΠ° Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡΡΠΊΠΈΡ
Π°ΡΡΠΈΠ±ΡΡΠ° (ΠΊΠ°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°) ΡΠ°ΡΠ°ΠΊΠ°. ΠΠ΄ΡΠ΅ΡΠ΅Π½ ΡΠ΅ Π²Π΅Π»ΠΈΠΊΠΈ Π±ΡΠΎΡ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
Π°ΡΡΠΈΠ±ΡΡΠ° ΡΠ°ΡΠ°ΠΊΠ° ΠΈ ΠΈΠ·Π²ΡΡΠ΅Π½Π° ΡΠ΅Π»Π΅ΠΊΡΠΈΡΠ° ΠΎΠ½ΠΈΡ
Π½Π°ΡΠ·Π½Π°ΡΠ°ΡΠ½ΠΈΡΠΈΡ
Π·Π° ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΡ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ°. ΠΠ·Π²ΠΎΡΠ΅Π½Π° ΡΠ΅ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ° ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ΠΌ Π΄Π΅ΡΠ΅Ρ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄Π° ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ°. ΠΠ°ΡΠ²ΠΈΡΠ° ΡΠΊΡΠΏΠ½Π° ΡΠ°ΡΠ½ΠΎΡΡ (Π΅Π½Π³Π». Overall Accuracy β OA) ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ΅ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ΅ ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎΠ³ Π»Π°ΡΠ΅ΡΡΠΊΠΈΠΌ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ΅ΠΌ ΠΈΠ· Π²Π°Π·Π΄ΡΡ
Π° Π±ΠΈΠ»Π° ΡΠ΅ 83.5% Π·Π° ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΠΎΡΠΏΠΎΡΠ½ΠΈΡ
Π²Π΅ΠΊΡΠΎΡΠ° (Π΅Π½Π³Π». Support Vector Machine) ΠΏΡΠΈΠΌΠ΅ΡΠ΅Π½Ρ Π½Π° ISPRS ΡΠ΅ΡΡ ΠΏΠΎΠ΄Π°ΡΠΊΠ΅, Π΄ΠΎΠΊ ΡΠ΅ Π½Π°Π΄ GRSS ΡΠ΅ΡΡ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° Π»Π°ΡΠ΅ΡΡΠΊΠΎΠ³ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ° ΠΏΠΎΡΡΠΈΠ³Π½ΡΡΠ° ΡΠΊΡΠΏΠ½Π° ΡΠ°ΡΠ½ΠΎΡΡ ΠΎΠ΄ 93.6% ΠΊΠ°Π΄Π° ΡΠ΅ ΠΊΠΎΡΠΈΡΡΠΈ ΡΠ»ΠΎΠΆΠ΅Π½ΠΈ Π°Π½ΡΠ°ΠΌΠ±Π» ΠΌΠΎΠ΄Π΅Π» Π±Π°Π·ΠΈΡΠ°Π½ Π½Π° Π½Π°ΠΈΠ²Π½ΠΎΠΌ ΠΠ°ΡΠ΅ΡΡ (Π΅Π½Π³Π». Naive Bayes) ΠΈ ΡΠ»Π°Π³Π°ΡΡ ΠΌΠΎΠ΄Π΅Π»Π°: ΡΠ»ΡΡΠ°ΡΠ½Π΅ ΡΡΠΌΠ΅ (Π΅Π½Π³Π». Random Forest), Π³ΡΠ°Π΄ΠΈΡΠ΅Π½ΡΠ½ΠΎΠ³ ΠΏΠΎΡΠ°ΡΠ°Π²Π°ΡΠ° (Π΅Π½Π³Π». Gradient Boosting) ΠΈ Π»ΠΎΠ³ΠΈΡΡΠΈΡΠΊΠ΅ ΡΠ΅Π³ΡΠ΅ΡΠΈΡΠ΅ (Π΅Π½Π³Π». Logistic Regression).
Π£ Π½Π΅ΠΊΠΈΠΌ ΠΏΡΠΈΠΌΠ΅Π½Π°ΠΌΠ°, ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎΠ³ Π»Π°ΡΠ΅ΡΡΠΊΠΈΠΌ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ΅ΠΌ ΠΈΠ· Π²Π°Π·Π΄ΡΡ
Π° ΠΏΠΎΠ΄ΡΠ°Π·ΡΠΌΠ΅Π²Π° ΠΈ ΠΈΠ·Π΄Π²Π°ΡΠ°ΡΠ΅ ΠΎΠ±ΡΠ΅ΠΊΠ°ΡΠ° ΠΎΠ΄ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° (ΠΊΡΠΎΠ²Π° Π·Π³ΡΠ°Π΄Π΅, ΠΊΡΠΎΡΡΠ΅ Π΄ΡΠ²Π΅ΡΠ° ΠΈ ΡΠ»ΠΈΡΠ½ΠΎ). Π£ ΠΎΠΊΠ²ΠΈΡΡ ΠΎΠ²ΠΎΠ³ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ° ΠΎΠ±ΡΠ°ΡΠ΅Π½Π° ΡΠ΅ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° ΠΏΠΎΡΡΠΌΡΠ΅Π½ΠΎΠ³ ΠΏΠΎΠ΄ΡΡΡΡΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎΠ³ Π»Π°ΡΠ΅ΡΡΠΊΠΈΠΌ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ΅ΠΌ ΡΠ° ΡΠΈΡΠ΅ΠΌ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠ΅ ΠΏΠΎΡΠ΅Π΄ΠΈΠ½Π°ΡΠ½ΠΈΡ
ΡΡΠ°Π±Π°Π»Π°. ΠΠ°Π²Π΅Π΄Π΅Π½ΠΈ ΠΏΡΠΈΡΡΡΠΏ ΠΏΠΎΠ΄ΡΠ°Π·ΡΠΌΠ΅Π²Π° ΡΠΈΠ»ΡΡΠΈΡΠ°ΡΠ΅ Π»ΠΎΠΊΠ°Π»Π½ΠΈΡ
ΠΌΠ°ΠΊΡΠΈΠΌΡΠΌΠ° ΠΈ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ ΠΏΠΎΡΠ΅Π΄ΠΈΠ½Π°ΡΠ½ΠΈΡ
ΠΊΡΠΎΡΡΠΈ ΡΡΠ°Π±Π°Π»Π° Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π²ΠΈΡΠΈΠ½ΡΠΊΠΎΠ³ ΠΌΠΎΠ΄Π΅Π»Π° ΠΊΡΠΎΡΡΠΈ ΡΡΠ°Π±Π°Π»Π°. ΠΡΠ΅ΡΡ
ΠΎΠ΄Π½ΠΈ ΠΏΠΎΡΡΡΠΏΠ°ΠΊ ΡΠ΅ Π±ΠΈΡΠ°Π½ Π·Π° Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΡΠΈΠΏΠΎΠ²Π° Π°ΡΡΠΈΠ±ΡΡΠ° Π½Π° Π½ΠΈΠ²ΠΎΡ ΡΠ΅Π³ΠΌΠ΅Π½Π°ΡΠ° ΠΊΡΠΎΡΡΠΈ ΡΡΠ°Π±Π°Π»Π° ΠΊΠΎΡΠ΅ ΡΠ΅ ΠΊΠΎΡΠΈΡΡΠ΅ Π·Π° ΠΊΠ°ΡΠ½ΠΈΡΡ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΡ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠ° Π²ΡΡ
ΠΎΠ²Π° ΡΡΠ°Π±Π°Π»Π° Ρ ΠΈΡΠΏΡΠ°Π²Π½ΠΎ ΠΈ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎ Π΄Π΅ΡΠ΅ΠΊΡΠΎΠ²Π°Π½Π΅. ΠΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΡΠ΅ ΡΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ Π·Π° ΠΏΠΎΠ΄ΡΡΡΡΠ΅ ΠΌΠ΅ΡΠΎΠ²ΠΈΡΠ΅ ΡΡΠΌΠ΅, ΠΏΡΠ΅ΡΠ΅ΠΆΠ½ΠΎ Π»ΠΈΡΡΠ°ΡΡΠΊΠ΅,
VIII
ΡΠ»ΠΎΠΆΠ΅Π½Π΅ ΡΠΎΠΏΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ ΠΈ Π΄ΠΈΠΌΠ΅Π½Π·ΠΈΡΠ° 0.6 km Γ 4 km. ΠΡΠΏΠΈΡΠΈΠ²Π°Π½Π΅ ΡΡ ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΠ΅ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅ Π·Π° ΠΏΠ΅Ρ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ°: ΡΠ»ΡΡΠ°ΡΠ½e ΡΡΠΌe, Π΅ΠΊΡΡΡΠ΅ΠΌΠ½ΠΎ Π³ΡΠ°Π΄ΠΈΡΠ΅Π½ΡΠ½ΠΎ ΠΏΠΎΡΠ°ΡΠ°Π²Π°ΡΠ΅ (Π΅Π½Π³Π». Extreme Gradient Boosting), Π²Π΅ΡΡΠ°ΡΠΊΠ΅ Π½Π΅ΡΡΠΎΠ½ΡΠΊΠ΅ ΠΌΡΠ΅ΠΆΠ΅ (Π΅Π½Π³Π». Artificial Neural Network), ΠΌΠ΅ΡΠΎΠ΄Π΅ ΠΏΠΎΡΠΏΠΎΡΠ½ΠΈΡ
Π²Π΅ΠΊΡΠΎΡΠ° ΠΈ ΠΌΠ΅ΡΠΎΠ΄Π΅ Π»ΠΎΠ³ΠΈΡΡΠΈΡΠΊΠ΅ ΡΠ΅Π³ΡΠ΅ΡΠΈΡΠ΅. ΠΠ²Π΄Π΅ ΡΠ΅ ΡΠ°ΠΊΠΎΡΠ΅ Π²ΡΡΠ΅Π½ΠΎ Π±Π°Π»Π°Π½ΡΠΈΡΠ°ΡΠ΅ ΠΊΠ»Π°ΡΠ° ΡΠΊΡΠΏΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° Ρ ΡΠΈΡΡ ΠΏΠΎΡΡΠΈΠ·Π°ΡΠ° Π±ΠΎΡΠΈΡ
ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΠΈ Ρ Π²ΠΈΠ΄Ρ ΡΠ°ΡΠ½ΠΎΡΡΠΈ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅. ΠΠΎΠ½Π°ΡΠ½Π° ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ° ΡΠ΅ ΠΈΠ·Π²ΡΡΠ΅Π½Π° ΡΠ° ΠΌΠΎΠ΄Π΅Π»ΠΎΠΌ ΡΠ»ΡΡΠ°ΡΠ½Π΅ ΡΡΠΌΠ΅ ΡΠ° ΠΊΠΎΡΠΈΠΌ ΡΠ΅ Π΄ΠΎΠ±ΠΈΡΠ°ΡΡ Π½Π°ΡΠ±ΠΎΡΠ΅ ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΠ΅ Ρ ΠΏΠΎΠ³Π»Π΅Π΄Ρ ΡΠ°ΡΠ½ΠΎΡΡΠΈ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅. Π£ΠΊΡΠΏΠ½Π° ΡΠ°ΡΠ½ΠΎΡΡ (OA) ΠΈ ΠΊΠ°ΠΏΠ° ΠΊΠΎΠ΅ΡΠΈΡΠΈΡΠ΅Π½Ρ ΡΠ»Π°Π³Π°ΡΠ° (ΞΊ) Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΈ Π΄Π΅ΡΠ΅ΡΠΎΡΡΡΡΠΊΠΎΠΌ ΡΠ½Π°ΠΊΡΡΠ½ΠΎΠΌ Π²Π°Π»ΠΈΠ΄Π°ΡΠΈΡΠΎΠΌ Π½Π°Π΄ ΡΡΠ΅Π½ΠΈΠ½Π³ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° ΠΈΠ·Π½ΠΎΡΠΈΠ»ΠΈ ΡΡ 90.4% ΠΈ 0.808. ΠΡΠΈΠΌΠ΅Π½ΠΎΠΌ ΠΈΡΡΡΠ΅Π½ΠΈΡΠ°Π½ΠΎΠ³ ΠΌΠΎΠ΄Π΅Π»Π° Π½Π° Π½Π΅Π·Π°Π²ΠΈΡΠ½ΠΎΠΌ ΡΠΊΡΠΏΡ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎ ΡΠ΅ Π΄Π° ΡΠ΅ OA = 89.0% ΠΈ ΞΊ = 0.757.
ΠΠ° ΠΊΡΠ°ΡΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ΅ Π΄Π°ΡΠ΅ ΡΡ ΡΠΌΠ΅ΡΠ½ΠΈΡΠ΅ Π·Π° Π΄Π°ΡΠ΅ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΠΈ ΡΠ°Π·Π²ΠΎΡ.Light Detection and Ranging β LiDAR technology has proven to be very successful for rapid collection of massive amounts of spatial data on the topography of the Earth's physical surface. Semantic segmentation of an Airborne Laser Scanning (ALS) point cloud, also called point cloud classification or semantic labeling as well as semantic point cloud classification is a major challenge due to the structure of the point cloud, as well as the types of classes that can be identified in that space. Machine learning (ML), on the other hand, represents a powerful mathematical tool that can be used for a variety of applications, including mentioned procedure. In this dissertation, ML methods are analyzed in the terms of achieving the best results for semantic segmentation of point cloud, especially with stacked ensemble ML models constructed by combining several fundamental ML methods. The ALS dataset was also balanced in such a way that points belonging to minority classes are synthetically generated while points belonging to the major classes are highly reduced. An analysis of the search type of neighborhood points and the sizes of the search radius were performed, and the possibility of using a multiβscale search approach in order to generate the geometric characteristics of the points. A large number of different features (attributes) of the points was determined and the selection of the features that are most significant for the semantic segmentation of the point cloud was carried out. Semantic segmentation of ALS point clouds was performed by using ten different ML methods. The highest overall accuracy of the semantic segmentation of the ALS point cloud was 83.5% for the support vector machine method predicted on the ISPRS test data, while the overall accuracy of 93.6% was achieved on the GRSS test ALS data when using the stacked ensemble model of naive Bayesian stacking of several ML models (Random Forest, Gradient Boosting and Logistic Regression).
In some applications, segmentation also implies extraction of the objects of interest (the building roof, the tree crown, etc.). Within this research, the segmentation of the ALS point cloud of a forested area was analyzed with the aim of Individual Tree Detection (ITD). The mentioned approach involves Local Maxima (LM) filtering and segmentation of individual tree canopies by using the Canopy Height Model (CHM). Previous procedure is important for generation of different segmentβlevel type of features that are used for later classification of treetops into correctly and incorrectly detected ones. The study was conducted for a mixed temperate forest, predominantly deciduous, with complex topography and area size of 0.6 km Γ 4 km. Classification model training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR). Here, the classes of the dataset were also balanced in order to achieve better performance in the terms of classification accuracy. The final classification was performed with the random forest model, which gives the best performance in terms of classification accuracy. The Overall Accuracy (OA) and the kappa coefficient of agreement (ΞΊ) obtained from tenβfold cross validation for the training data were 90.4%
X
and 0.808, respectively. The application of the trained model on the independent set of data, resulted in OA = 89.0% and ΞΊ = 0.757.
At the end of the dissertation, guidelines for further research and development are given
Examining the impact of Ground Control point quantity on the geometric accuracy of UAV photogrammetric products formed using Structure-from-Motion approach
The positional and vertical accuracy of UAV aerial photogrammetry products generated using the Structure from Motion (SfM) approach depends on various factors, such as flight plan parameters, camera quality, camera calibration, the SfM algorithm used, and the georeferencing process. The influence of the quantity of Ground Control Points (GCPs) on the geometric quality of generated models and the stability of camera calibration parameters assessed through self-calibration in the block-aerotriangulation process was investigated in this study. Three software systems were used to process the collected UAV photogrammetry images: Pix4D Mapper, Agisoft Metashape, and Trimble Inpho UASMaster. Standard statistical quality assessments were employed to assess the accuracy of the block-aerotriangulation. The research findings indicate that augmenting the quantity of GCPs enhances model reliability and decreases the RMSE values of vertical deviation on the control points. The RMSE values of vertical deviation on the check points for all three used software systems converged to approximately twice the value of the average spatial resolution. Additionally, the RMSE values of positional deviation on check points converged to the value of the average spatial resolution
Refinement of Individual Tree Detection Results Obtained from Airborne Laser Scanning Data for a Mixed Natural Forest
Numerous semi- and fully-automatic algorithms have been developed for individual tree detection from airborne laser-scanning data, but different rates of falsely detected treetops also accompany their results. In this paper, we proposed an approach that includes a machine learning-based refinement step to reduce the number of falsely detected treetops. The approach involves the local maxima filtering and segmentation of the canopy height model to extract different segment-level features used for the classification of treetop candidates. The study was conducted in a mixed temperate forest, predominantly deciduous, with a complex topography and an area size of 0.6 km Γ 4 km. The classification modelβs training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting, Artificial Neural Network, the Support Vector Machine, and Logistic Regression. The final classification model with optimal hyperparameters was adopted based on the best-performing classifier (RF). The overall accuracy (OA) and kappa coefficient (ΞΊ) obtained from the ten-fold cross validation for the training data were 90.4% and 0.808, respectively. The prediction of the test data resulted in an OA = 89.0% and a ΞΊ = 0.757. This indicates that the proposed method could be an adequate solution for the reduction of falsely detected treetops before tree crown segmentation, especially in deciduous forests
Estimation of the Fundamental Frequency of the Speech Signal Compressed by MP3 Algorithm
The paper analyzes the estimation of the fundamental frequency from the real speech signal which is obtained by recording the speaker in the real acoustic environment modeled by the MP3 method. The estimation was performed by the Picking-Peaks algorithm with implemented parametric cubic convolution (PCC) interpolation. The efficiency of PCC was tested for Catmull-Rom, Greville, and Greville two- parametric kernel. Depending on MSE, a window that gives optimal results was chosen
Semantic segmentation of airborne laser scanning point clouds using machine learning methods
Π’Π΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ° Π»Π°ΡΠ΅ΡΡΠΊΠΎΠ³ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ° (Π΅Π½Π³Π». Light Detection and Ranging β LiDAR) ΠΏΠΎΠΊΠ°Π·Π°Π»Π° ΡΠ΅ ΠΊΠ°ΠΎ Π²Π΅ΠΎΠΌΠ° ΡΡΠΏΠ΅ΡΠ½Π° Π·Π° Π±ΡΠ·ΠΎ ΠΏΡΠΈΠΊΡΠΏΡΠ°ΡΠ΅ ΠΌΠ°ΡΠΎΠ²Π½Π΅ ΠΊΠΎΠ»ΠΈΡΠΈΠ½Π΅ ΠΏΡΠΎΡΡΠΎΡΠ½ΠΈΡ
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΎ ΡΠΎΠΏΠΎΠ³ΡΠ°ΡΠΈΡΠΈ ΡΠΈΠ·ΠΈΡΠΊΠ΅ ΠΏΠΎΠ²ΡΡΠΈ ΠΠ΅ΠΌΡΠ΅. Π‘Π΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ° ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎΠ³ Π»Π°ΡΠ΅ΡΡΠΊΠΈΠΌ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ΅ΠΌ ΠΈΠ· Π²Π°Π·Π΄ΡΡ
Π° (Π΅Π½Π³Π». Airborne Laser Scanning β ALS) ΠΊΠΎΡΠ° ΡΠ΅ ΡΠ°ΠΊΠΎΡΠ΅ Π½Π°Π·ΠΈΠ²Π° ΠΈ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡaΠΊΠ°, ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠΎ ΠΎΠ·Π½Π°ΡΠ°Π²Π°ΡΠ΅ ΠΊΠ°ΠΎ ΠΈ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ° ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ°, ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° Π²Π΅Π»ΠΈΠΊΠΈ ΠΈΠ·Π°Π·ΠΎΠ² Π·Π±ΠΎΠ³ ΡΡΡΡΠΊΡΡΡΠ΅ ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° ΠΈ ΡΠΈΠΏΠΎΠ²Π° ΠΊΠ»Π°ΡΠ° ΠΊΠΎΡΠ΅ ΡΠ΅ ΠΌΠΎΠ³Ρ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΠΊΠΎΠ²Π°ΡΠΈ Ρ ΡΠΎΠΌ ΠΏΡΠΎΡΡΠΎΡΡ. ΠΠ°ΡΠΈΠ½ΡΠΊΠΎ ΡΡΠ΅ΡΠ΅, ΡΠ° Π΄ΡΡΠ³Π΅ ΡΡΡΠ°Π½Π΅, ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° ΠΌΠΎΡΠ°Π½ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠΊΠΈ Π°ΠΏΠ°ΡΠ°Ρ ΠΊΠΎΡΠΈ ΡΠ΅ ΠΌΠΎΠΆΠ΅ ΠΈΡΠΊΠΎΡΠΈΡΡΠΈΡΠΈ Π·Π° ΡΠ°Π·Π»ΠΈΡΠΈΡΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅ ΡΠΊΡΡΡΡΡΡΡΠΈ ΠΈ Π½Π°Π²Π΅Π΄Π΅Π½Ρ ΠΏΡΠΎΡΠ΅Π΄ΡΡΡ. Π£ ΠΎΠ²ΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΡΡ Π°Π½Π°Π»ΠΈΠ·ΠΈΡΠ°Π½Π΅ ΠΌΠ΅ΡΠΎΠ΄Π΅ ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ° ΠΊΠΎΡΠΈΠΌ ΡΠ΅ Π΄ΠΎΠ±ΠΈΡΠ°ΡΡ Π½Π°ΡΠ±ΠΎΡΠΈ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ΅ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ΅ ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ°, ΠΏΠΎΠ³ΠΎΡoΠ²ΠΎ ΡΠ° ΡΠ»ΠΎΠΆΠ΅Π½ΠΈΠΌ Π°Π½ΡΠ°ΠΌΠ±Π» ΠΌΠΎΠ΄Π΅Π»ΠΈΠΌΠ° ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ° ΠΊΠΎΠ½ΡΡΡΡΠΈΡΠ°Π½ΠΈΠΌ ΡΠ»Π°Π³Π°ΡΠ΅ΠΌ Π²ΠΈΡΠ΅ ΠΎΡΠ½ΠΎΠ²Π½ΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π° ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ°. Π£ ΠΎΠ²ΠΎΠΌ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΡ ΡΠ΅ Π²ΡΡΠ΅Π½ΠΎ ΠΈ Π±Π°Π»Π°Π½ΡΠΈΡΠ°ΡΠ΅ ΡΠΊΡΠΏΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΡΠΈΠ½ΡΠ΅ΡΠΈΡΠΊΠΈΠΌ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΠ΅ΠΌ ΡΠ°ΡΠ°ΠΊΠ° ΠΊΠΎΡΠ΅ ΠΏΡΠΈΠΏΠ°Π΄Π°ΡΡ ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΈΠΌ ΠΊΠ»Π°ΡΠ°ΠΌΠ° Π΄ΠΎΠΊ ΡΡ ΡΠ°ΡΠΊΠ΅ ΠΊΠΎΡΠ΅ ΠΏΡΠΈΠΏΠ°Π΄Π°ΡΡ Π²Π΅ΡΠΈΠ½ΡΠΊΠΈΠΌ ΠΊΠ»Π°ΡΠ°ΠΌΠ° Π·Π½Π°ΡΠ½ΠΎ ΡΠ΅Π΄ΡΠΊΠΎΠ²Π°Π½Π΅. ΠΡΡΠ΅Π½e ΡΡ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠΈΠΏΠ° ΠΏΡΠ΅ΡΡΠ°Π³Π΅ ΡΠ°ΡΠ°ΠΊΠ° ΡΡΡΠ΅Π΄ΡΡΠ²Π° ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΡΡΠΈΡΠ°ΡΠ° Π²Π΅Π»ΠΈΡΠΈΠ½Π΅ ΠΏΠΎΠ»ΡΠΏΡΠ΅ΡΠ½ΠΈΠΊΠ° ΠΏΡΠ΅ΡΡΠ°Π³Π΅, Π° ΠΈΡΠΏΠΈΡΠ°Π½Π° ΡΠ΅ ΠΈ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ Π²ΠΈΡΠ΅ΡΠ°Π·ΠΌΠ΅ΡΠ½ΠΎΠ³ (Π΅Π½Π³Π». multiscale) ΠΏΡΠΈΡΡΡΠΏΠ° ΠΏΡΠ΅ΡΡΠ°Π³Π΅ Ρ ΡΠΈΡΡ Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΠ° Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡΡΠΊΠΈΡ
Π°ΡΡΠΈΠ±ΡΡΠ° (ΠΊΠ°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°) ΡΠ°ΡΠ°ΠΊΠ°. ΠΠ΄ΡΠ΅ΡΠ΅Π½ ΡΠ΅ Π²Π΅Π»ΠΈΠΊΠΈ Π±ΡΠΎΡ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
Π°ΡΡΠΈΠ±ΡΡΠ° ΡΠ°ΡΠ°ΠΊΠ° ΠΈ ΠΈΠ·Π²ΡΡΠ΅Π½Π° ΡΠ΅Π»Π΅ΠΊΡΠΈΡΠ° ΠΎΠ½ΠΈΡ
Π½Π°ΡΠ·Π½Π°ΡΠ°ΡΠ½ΠΈΡΠΈΡ
Π·Π° ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΡ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ°. ΠΠ·Π²ΠΎΡΠ΅Π½Π° ΡΠ΅ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ° ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ΠΌ Π΄Π΅ΡΠ΅Ρ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΠΌΠ΅ΡΠΎΠ΄Π° ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ°. ΠΠ°ΡΠ²ΠΈΡΠ° ΡΠΊΡΠΏΠ½Π° ΡΠ°ΡΠ½ΠΎΡΡ (Π΅Π½Π³Π». Overall Accuracy β OA) ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠΊΠ΅ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ΅ ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎΠ³ Π»Π°ΡΠ΅ΡΡΠΊΠΈΠΌ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ΅ΠΌ ΠΈΠ· Π²Π°Π·Π΄ΡΡ
Π° Π±ΠΈΠ»Π° ΡΠ΅ 83.5% Π·Π° ΠΌΠ΅ΡΠΎΠ΄Ρ ΠΏΠΎΡΠΏΠΎΡΠ½ΠΈΡ
Π²Π΅ΠΊΡΠΎΡΠ° (Π΅Π½Π³Π». Support Vector Machine) ΠΏΡΠΈΠΌΠ΅ΡΠ΅Π½Ρ Π½Π° ISPRS ΡΠ΅ΡΡ ΠΏΠΎΠ΄Π°ΡΠΊΠ΅, Π΄ΠΎΠΊ ΡΠ΅ Π½Π°Π΄ GRSS ΡΠ΅ΡΡ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° Π»Π°ΡΠ΅ΡΡΠΊΠΎΠ³ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ° ΠΏΠΎΡΡΠΈΠ³Π½ΡΡΠ° ΡΠΊΡΠΏΠ½Π° ΡΠ°ΡΠ½ΠΎΡΡ ΠΎΠ΄ 93.6% ΠΊΠ°Π΄Π° ΡΠ΅ ΠΊΠΎΡΠΈΡΡΠΈ ΡΠ»ΠΎΠΆΠ΅Π½ΠΈ Π°Π½ΡΠ°ΠΌΠ±Π» ΠΌΠΎΠ΄Π΅Π» Π±Π°Π·ΠΈΡΠ°Π½ Π½Π° Π½Π°ΠΈΠ²Π½ΠΎΠΌ ΠΠ°ΡΠ΅ΡΡ (Π΅Π½Π³Π». Naive Bayes) ΠΈ ΡΠ»Π°Π³Π°ΡΡ ΠΌΠΎΠ΄Π΅Π»Π°: ΡΠ»ΡΡΠ°ΡΠ½Π΅ ΡΡΠΌΠ΅ (Π΅Π½Π³Π». Random Forest), Π³ΡΠ°Π΄ΠΈΡΠ΅Π½ΡΠ½ΠΎΠ³ ΠΏΠΎΡΠ°ΡΠ°Π²Π°ΡΠ° (Π΅Π½Π³Π». Gradient Boosting) ΠΈ Π»ΠΎΠ³ΠΈΡΡΠΈΡΠΊΠ΅ ΡΠ΅Π³ΡΠ΅ΡΠΈΡΠ΅ (Π΅Π½Π³Π». Logistic Regression).
Π£ Π½Π΅ΠΊΠΈΠΌ ΠΏΡΠΈΠΌΠ΅Π½Π°ΠΌΠ°, ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎΠ³ Π»Π°ΡΠ΅ΡΡΠΊΠΈΠΌ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ΅ΠΌ ΠΈΠ· Π²Π°Π·Π΄ΡΡ
Π° ΠΏΠΎΠ΄ΡΠ°Π·ΡΠΌΠ΅Π²Π° ΠΈ ΠΈΠ·Π΄Π²Π°ΡΠ°ΡΠ΅ ΠΎΠ±ΡΠ΅ΠΊΠ°ΡΠ° ΠΎΠ΄ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΠ° (ΠΊΡΠΎΠ²Π° Π·Π³ΡΠ°Π΄Π΅, ΠΊΡΠΎΡΡΠ΅ Π΄ΡΠ²Π΅ΡΠ° ΠΈ ΡΠ»ΠΈΡΠ½ΠΎ). Π£ ΠΎΠΊΠ²ΠΈΡΡ ΠΎΠ²ΠΎΠ³ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ° ΠΎΠ±ΡΠ°ΡΠ΅Π½Π° ΡΠ΅ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΠ° ΠΎΠ±Π»Π°ΠΊΠ° ΡΠ°ΡΠ°ΠΊΠ° ΠΏΠΎΡΡΠΌΡΠ΅Π½ΠΎΠ³ ΠΏΠΎΠ΄ΡΡΡΡΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎΠ³ Π»Π°ΡΠ΅ΡΡΠΊΠΈΠΌ ΡΠΊΠ΅Π½ΠΈΡΠ°ΡΠ΅ΠΌ ΡΠ° ΡΠΈΡΠ΅ΠΌ Π΄Π΅ΡΠ΅ΠΊΡΠΈΡΠ΅ ΠΏΠΎΡΠ΅Π΄ΠΈΠ½Π°ΡΠ½ΠΈΡ
ΡΡΠ°Π±Π°Π»Π°. ΠΠ°Π²Π΅Π΄Π΅Π½ΠΈ ΠΏΡΠΈΡΡΡΠΏ ΠΏΠΎΠ΄ΡΠ°Π·ΡΠΌΠ΅Π²Π° ΡΠΈΠ»ΡΡΠΈΡΠ°ΡΠ΅ Π»ΠΎΠΊΠ°Π»Π½ΠΈΡ
ΠΌΠ°ΠΊΡΠΈΠΌΡΠΌΠ° ΠΈ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΡΡ ΠΏΠΎΡΠ΅Π΄ΠΈΠ½Π°ΡΠ½ΠΈΡ
ΠΊΡΠΎΡΡΠΈ ΡΡΠ°Π±Π°Π»Π° Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ Π²ΠΈΡΠΈΠ½ΡΠΊΠΎΠ³ ΠΌΠΎΠ΄Π΅Π»Π° ΠΊΡΠΎΡΡΠΈ ΡΡΠ°Π±Π°Π»Π°. ΠΡΠ΅ΡΡ
ΠΎΠ΄Π½ΠΈ ΠΏΠΎΡΡΡΠΏΠ°ΠΊ ΡΠ΅ Π±ΠΈΡΠ°Π½ Π·Π° Π³Π΅Π½Π΅ΡΠΈΡΠ°ΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡΠΈΡ
ΡΠΈΠΏΠΎΠ²Π° Π°ΡΡΠΈΠ±ΡΡΠ° Π½Π° Π½ΠΈΠ²ΠΎΡ ΡΠ΅Π³ΠΌΠ΅Π½Π°ΡΠ° ΠΊΡΠΎΡΡΠΈ ΡΡΠ°Π±Π°Π»Π° ΠΊΠΎΡΠ΅ ΡΠ΅ ΠΊΠΎΡΠΈΡΡΠ΅ Π·Π° ΠΊΠ°ΡΠ½ΠΈΡΡ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΡ ΠΊΠ°Π½Π΄ΠΈΠ΄Π°ΡΠ° Π²ΡΡ
ΠΎΠ²Π° ΡΡΠ°Π±Π°Π»Π° Ρ ΠΈΡΠΏΡΠ°Π²Π½ΠΎ ΠΈ ΠΏΠΎΠ³ΡΠ΅ΡΠ½ΠΎ Π΄Π΅ΡΠ΅ΠΊΡΠΎΠ²Π°Π½Π΅. ΠΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΡΠ΅ ΡΠΏΡΠΎΠ²Π΅Π΄Π΅Π½ΠΎ Π·Π° ΠΏΠΎΠ΄ΡΡΡΡΠ΅ ΠΌΠ΅ΡΠΎΠ²ΠΈΡΠ΅ ΡΡΠΌΠ΅, ΠΏΡΠ΅ΡΠ΅ΠΆΠ½ΠΎ Π»ΠΈΡΡΠ°ΡΡΠΊΠ΅,
VIII
ΡΠ»ΠΎΠΆΠ΅Π½Π΅ ΡΠΎΠΏΠΎΠ³ΡΠ°ΡΠΈΡΠ΅ ΠΈ Π΄ΠΈΠΌΠ΅Π½Π·ΠΈΡΠ° 0.6 km Γ 4 km. ΠΡΠΏΠΈΡΠΈΠ²Π°Π½Π΅ ΡΡ ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΠ΅ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅ Π·Π° ΠΏΠ΅Ρ ΠΌΠ΅ΡΠΎΠ΄Π° ΠΌΠ°ΡΠΈΠ½ΡΠΊΠΎΠ³ ΡΡΠ΅ΡΠ°: ΡΠ»ΡΡΠ°ΡΠ½e ΡΡΠΌe, Π΅ΠΊΡΡΡΠ΅ΠΌΠ½ΠΎ Π³ΡΠ°Π΄ΠΈΡΠ΅Π½ΡΠ½ΠΎ ΠΏΠΎΡΠ°ΡΠ°Π²Π°ΡΠ΅ (Π΅Π½Π³Π». Extreme Gradient Boosting), Π²Π΅ΡΡΠ°ΡΠΊΠ΅ Π½Π΅ΡΡΠΎΠ½ΡΠΊΠ΅ ΠΌΡΠ΅ΠΆΠ΅ (Π΅Π½Π³Π». Artificial Neural Network), ΠΌΠ΅ΡΠΎΠ΄Π΅ ΠΏΠΎΡΠΏΠΎΡΠ½ΠΈΡ
Π²Π΅ΠΊΡΠΎΡΠ° ΠΈ ΠΌΠ΅ΡΠΎΠ΄Π΅ Π»ΠΎΠ³ΠΈΡΡΠΈΡΠΊΠ΅ ΡΠ΅Π³ΡΠ΅ΡΠΈΡΠ΅. ΠΠ²Π΄Π΅ ΡΠ΅ ΡΠ°ΠΊΠΎΡΠ΅ Π²ΡΡΠ΅Π½ΠΎ Π±Π°Π»Π°Π½ΡΠΈΡΠ°ΡΠ΅ ΠΊΠ»Π°ΡΠ° ΡΠΊΡΠΏΠ° ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° Ρ ΡΠΈΡΡ ΠΏΠΎΡΡΠΈΠ·Π°ΡΠ° Π±ΠΎΡΠΈΡ
ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΠΈ Ρ Π²ΠΈΠ΄Ρ ΡΠ°ΡΠ½ΠΎΡΡΠΈ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅. ΠΠΎΠ½Π°ΡΠ½Π° ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ° ΡΠ΅ ΠΈΠ·Π²ΡΡΠ΅Π½Π° ΡΠ° ΠΌΠΎΠ΄Π΅Π»ΠΎΠΌ ΡΠ»ΡΡΠ°ΡΠ½Π΅ ΡΡΠΌΠ΅ ΡΠ° ΠΊΠΎΡΠΈΠΌ ΡΠ΅ Π΄ΠΎΠ±ΠΈΡΠ°ΡΡ Π½Π°ΡΠ±ΠΎΡΠ΅ ΠΏΠ΅ΡΡΠΎΡΠΌΠ°Π½ΡΠ΅ Ρ ΠΏΠΎΠ³Π»Π΅Π΄Ρ ΡΠ°ΡΠ½ΠΎΡΡΠΈ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅. Π£ΠΊΡΠΏΠ½Π° ΡΠ°ΡΠ½ΠΎΡΡ (OA) ΠΈ ΠΊΠ°ΠΏΠ° ΠΊΠΎΠ΅ΡΠΈΡΠΈΡΠ΅Π½Ρ ΡΠ»Π°Π³Π°ΡΠ° (ΞΊ) Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΈ Π΄Π΅ΡΠ΅ΡΠΎΡΡΡΡΠΊΠΎΠΌ ΡΠ½Π°ΠΊΡΡΠ½ΠΎΠΌ Π²Π°Π»ΠΈΠ΄Π°ΡΠΈΡΠΎΠΌ Π½Π°Π΄ ΡΡΠ΅Π½ΠΈΠ½Π³ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° ΠΈΠ·Π½ΠΎΡΠΈΠ»ΠΈ ΡΡ 90.4% ΠΈ 0.808. ΠΡΠΈΠΌΠ΅Π½ΠΎΠΌ ΠΈΡΡΡΠ΅Π½ΠΈΡΠ°Π½ΠΎΠ³ ΠΌΠΎΠ΄Π΅Π»Π° Π½Π° Π½Π΅Π·Π°Π²ΠΈΡΠ½ΠΎΠΌ ΡΠΊΡΠΏΡ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΎ ΡΠ΅ Π΄Π° ΡΠ΅ OA = 89.0% ΠΈ ΞΊ = 0.757.
ΠΠ° ΠΊΡΠ°ΡΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ΅ Π΄Π°ΡΠ΅ ΡΡ ΡΠΌΠ΅ΡΠ½ΠΈΡΠ΅ Π·Π° Π΄Π°ΡΠ΅ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΠΈ ΡΠ°Π·Π²ΠΎΡ.Light Detection and Ranging β LiDAR technology has proven to be very successful for rapid collection of massive amounts of spatial data on the topography of the Earth's physical surface. Semantic segmentation of an Airborne Laser Scanning (ALS) point cloud, also called point cloud classification or semantic labeling as well as semantic point cloud classification is a major challenge due to the structure of the point cloud, as well as the types of classes that can be identified in that space. Machine learning (ML), on the other hand, represents a powerful mathematical tool that can be used for a variety of applications, including mentioned procedure. In this dissertation, ML methods are analyzed in the terms of achieving the best results for semantic segmentation of point cloud, especially with stacked ensemble ML models constructed by combining several fundamental ML methods. The ALS dataset was also balanced in such a way that points belonging to minority classes are synthetically generated while points belonging to the major classes are highly reduced. An analysis of the search type of neighborhood points and the sizes of the search radius were performed, and the possibility of using a multiβscale search approach in order to generate the geometric characteristics of the points. A large number of different features (attributes) of the points was determined and the selection of the features that are most significant for the semantic segmentation of the point cloud was carried out. Semantic segmentation of ALS point clouds was performed by using ten different ML methods. The highest overall accuracy of the semantic segmentation of the ALS point cloud was 83.5% for the support vector machine method predicted on the ISPRS test data, while the overall accuracy of 93.6% was achieved on the GRSS test ALS data when using the stacked ensemble model of naive Bayesian stacking of several ML models (Random Forest, Gradient Boosting and Logistic Regression).
In some applications, segmentation also implies extraction of the objects of interest (the building roof, the tree crown, etc.). Within this research, the segmentation of the ALS point cloud of a forested area was analyzed with the aim of Individual Tree Detection (ITD). The mentioned approach involves Local Maxima (LM) filtering and segmentation of individual tree canopies by using the Canopy Height Model (CHM). Previous procedure is important for generation of different segmentβlevel type of features that are used for later classification of treetops into correctly and incorrectly detected ones. The study was conducted for a mixed temperate forest, predominantly deciduous, with complex topography and area size of 0.6 km Γ 4 km. Classification model training was performed by five machine learning approaches: Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR). Here, the classes of the dataset were also balanced in order to achieve better performance in the terms of classification accuracy. The final classification was performed with the random forest model, which gives the best performance in terms of classification accuracy. The Overall Accuracy (OA) and the kappa coefficient of agreement (ΞΊ) obtained from tenβfold cross validation for the training data were 90.4%
X
and 0.808, respectively. The application of the trained model on the independent set of data, resulted in OA = 89.0% and ΞΊ = 0.757.
At the end of the dissertation, guidelines for further research and development are given
Estimation of the Fundamental Frequency of the Speech Signal Compressed by MP3 Algorithm
The paper analyzes the estimation of the fundamental frequency from the real speech signal which is obtained by recording the speaker in the real acoustic environment modeled by the MP3 method. The estimation was performed by the Picking-Peaks algorithm with implemented parametric cubic convolution (PCC) interpolation. The efficiency of PCC was tested for Catmull-Rom, Greville, and Greville two- parametric kernel. Depending on MSE, a window that gives optimal results was chosen
An approach to the language discrimination in different scripts using adjacent local binary pattern
<p>The paper proposes a language discrimination method of documents. First, each letter is encoded with the certain script type according to its status in baseline area. Such a cipher text is subjected to a feature extraction process. Accordingly, the local binary pattern as well as its expanded version called adjacent local binary pattern are extracted. Because of the difference in the language characteristics, the above analysis shows significant diversity. This type of diversity is a key aspect in the decision-making differentiation of the languages. Proposed method is tested on an example of documents. The experiments give encouraging results.</p