40 research outputs found
Π€ΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ Π½ΠΎΡΡΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ Π»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ ΡΠ½ΠΈΠΌΠΊΠΎΠ² Π² ΠΌΠ½ΠΎΠ³ΠΎΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ Π±ΠΎΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠ΅
ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΡΠΏΠΎΡΠΎΠ± ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π·Π½ΠΎΡΠ°ΠΊΡΡΡΠ½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ²Π°Π·ΠΈΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ. ΠΡΡ
ΠΎΠ΄Π½ΡΠ΅ ΡΠ°Π·Π½ΠΎΡΠ°ΠΊΡΡΡΠ½ΡΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Π±ΠΎΡΡΠΎΠ²ΠΎΠΉ Π°ΠΏΠΏΠ°ΡΠ°ΡΡΡΠΎΠΉ ΠΌΠ½ΠΎΠ³ΠΎΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π½ΡΡ
Π»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, ΡΠΎΡΡΡΠΊΠΎΠ²ΡΠ²Π°ΡΡΡΡ Π² Π΅Π΄ΠΈΠ½ΡΠΉ ΡΠΎΡΡΠ°Π²Π½ΠΎΠΉ ΡΠ½ΠΈΠΌΠΎΠΊ ΠΈ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ Π²ΡΡΠΎΠΊΠΎΡΠΊΠΎΡΠΎΡΡΠ½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ²Π°Π·ΠΈΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ ΡΠ΅Π΄ΡΡΠΈΡΡΡΡΡΡ Π΄ΠΎ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
ΡΠ²Π΅ΡΠΎΠ² Ρ ΡΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΠ΅ΠΌ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΡΡ
Π³ΡΠ°Π½ΠΈΡ. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ²Π°Π·ΠΈΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΡΠ΅ΡΠΈΠΈ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΠΉ Ρ ΠΏΠΎΡΡΠ΅ΠΏΠ΅Π½Π½ΠΎ ΡΠ²Π΅Π»ΠΈΡΠΈΠ²Π°ΡΡΠ΅ΠΉΡΡ Π΄Π΅ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ Π·Π° ΡΡΠ΅Ρ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΡΠ»Π° ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ². ΠΡΠ° ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π²ΡΠ±ΡΠ°ΡΡ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΡΡΠΈΠ΅ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΡ ΠΏΠ°Ρ ΡΠΎΡΡΡΠΊΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈΠ· ΡΠ΅ΡΠΈΠΈ ΡΠ³Π΅Π½Π΅ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
.
ΠΠ° ΠΏΠ°ΡΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈΠ· Π²ΡΠ±ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΡ ΡΠΎΡΡΡΠΊΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ½ΠΈΠΌΠΊΠ° ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠΈΡΠΊ ΠΎΠΏΠΎΡΠ½ΡΡ
ΡΠΎΡΠ΅ΠΊ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ
ΠΊΠΎΠ½ΡΡΡΠΎΠ². ΠΠ»Ρ ΡΡΠΈΡ
ΡΠΎΡΠ΅ΠΊ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΡΡΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ ΠΏΠΎΡΠ»Π΅ Π΅Π³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΡΠ΅Π½ΠΊΠ° ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ. ΠΠ°ΠΊ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΎΠΏΠΎΡΠ½ΡΡ
ΡΠΎΡΠ΅ΠΊ ΠΊΠΎΠ½ΡΡΡΠ°, ΡΠ°ΠΊ ΠΈ ΡΠ°ΠΌΠΎ ΠΈΡΠΊΠΎΠΌΠΎΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠΎΡΠ½ΡΠ΅ΡΡΡ Π΄ΠΎ ΡΠ΅Ρ
ΠΏΠΎΡ, ΠΏΠΎΠΊΠ° ΠΎΡΠ΅Π½ΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π΅ Π±ΡΠ΄Π΅Ρ ΠΏΡΠΈΠ΅ΠΌΠ»Π΅ΠΌΠΎΠΉ. ΠΠΈΠ΄ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄Π±ΠΈΡΠ°Π΅ΡΡΡ ΠΏΠΎ ΡΠ΅Π΄ΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΠΏΠΎ ΡΠ²Π΅ΡΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ, Π° Π·Π°ΡΠ΅ΠΌ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΡΡΡ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ. ΠΡΠΎΡ ΠΏΡΠΎΡΠ΅ΡΡ ΠΏΠΎΠ²ΡΠΎΡΡΠ΅ΡΡΡ Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ Π±ΠΎΠ»ΡΡΠ΅ΠΉ Π΄Π΅ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ Π² ΡΠΎΠΌ ΡΠ»ΡΡΠ°Π΅, Π΅ΡΠ»ΠΈ ΠΎΡΠ΅Π½ΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΡΠΈΠ΅ΠΌΠ»Π΅ΠΌΠΎΠΉ. Π¦Π΅Π»ΡΡ Π½Π°ΡΡΠΎΡΡΠ΅Π³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠΏΠΎΡΠΎΠ±Π°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π³ΠΎ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ ΠΈΠ· ΡΠ°Π·Π½ΠΎΡΠΎΡΠΌΠ°ΡΠ½ΡΡ
ΠΈ ΡΠ°Π·Π½ΠΎΡΠΎΠ΄Π½ΡΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ².
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠΏΠΎΡΠΎΠ±Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΠ΅ΡΠ²Π°Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ Π΅Π΄ΠΈΠ½ΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΈΠ· ΠΏΠ°ΡΡ ΡΠΎΡΡΡΠΊΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΡΡ
ΠΎΠ΄Π½ΡΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ² Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠΌ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ, ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ Π²ΡΠ΄Π΅Π»ΠΈΡΡ ΠΎΠ΄ΠΈΠ½Π°ΠΊΠΎΠ²ΡΠ΅ ΠΎΠ±Π»Π°ΡΡΠΈ Π½Π° Π΅Π³ΠΎ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ°ΡΡΡΡ
. ΠΡΠΎΡΠ°Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΠΌ ΡΠΎΡΠΊΠ°ΠΌ ΠΊΠΎΠ½ΡΡΡΠ° Π½Π° ΠΎΠ±ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΠΏΠ°ΡΠ΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ², ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΡΡΡ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ Π΄Π»Ρ ΠΈΡ
ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ.
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊΠ°ΠΊ ΠΏΠΎ ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΡΠΌ (ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ) ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ, ΡΠ°ΠΊ ΠΈ ΠΏΠΎ ΡΠ°Π·Π½ΠΎΡΠΎΠ΄Π½ΡΠΌ (ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΌ ΠΈ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ) ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ. ΠΡΠ»ΠΈΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΡΡΠΎΠΉ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ ΡΠΏΠΎΡΠΎΠ±Π° ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΈΡΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ
Π€ΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ Π½ΠΎΡΡΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ Π»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ ΡΠ½ΠΈΠΌΠΊΠΎΠ² Π² ΠΌΠ½ΠΎΠ³ΠΎΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π½ΠΎΠΉ Π±ΠΎΡΡΠΎΠ²ΠΎΠΉ ΡΠΈΡΡΠ΅ΠΌΠ΅
The paper proposes a method for fusioning multi-angle images implementing the algorithm for quasi-optimal clustering of pixels to the original images of the land surface. The original multi-angle images formed by the onboard equipment of multi-positional location systems are docked into a single composite image and, using a high-speed algorithm for quasi-optimal pixel clustering, are reduced to several colors while maintaining characteristic boundaries. A feature of the algorithm of quasi-optimal pixel clustering is the generation of a series of partitions with gradually increasing detail due to a variable number of clusters. This feature allows you to choose an appropriate partition of a pair of docked images from the generated series.
The search for reference points of the isolated contours is performed on a pair of images from the selected partition of the docked image. A functional transformation is determined for these points. And after it has been applied to the original images, the degree of correlation of the fused image is estimated. Both the position of the reference points of the contour and the desired functional transformation itself are refined until the evaluation of the fusion quality is acceptable. The type of functional transformation is selected according to the images reduced in color, which later is applied to the original images. This process is repeated for clustered images with greater detail in the event that the assessment of the fusion quality is not acceptable. The purpose of present study is to develop a method that allows synthesizing fused image of the land surface from heteromorphic and heterogeneous images.
The paper presents the following features of the fusing method. The first feature is the processing of a single composite image from a pair of docked source images by the pixel clustering algorithm, what makes it possible to isolate the same areas in its different parts in a similar way. The second feature consists in determining the functional transformation by the isolated reference points of the contour on the processed pair of clustered images, which is later applied to the original images to combine them.
The paper presents the results on the synthesis of a fused image both from homogeneous (optical) images and from heterogeneous (radar and optical) images. A distinctive feature of the developed method is to improve the quality of synthesis, increase the accuracy and information content of the final fused image of the land surface.
ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ ΡΠΏΠΎΡΠΎΠ± ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠ°Π·Π½ΠΎΡΠ°ΠΊΡΡΡΠ½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ²Π°Π·ΠΈΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ. ΠΡΡ
ΠΎΠ΄Π½ΡΠ΅ ΡΠ°Π·Π½ΠΎΡΠ°ΠΊΡΡΡΠ½ΡΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Π±ΠΎΡΡΠΎΠ²ΠΎΠΉ Π°ΠΏΠΏΠ°ΡΠ°ΡΡΡΠΎΠΉ ΠΌΠ½ΠΎΠ³ΠΎΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π½ΡΡ
Π»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, ΡΠΎΡΡΡΠΊΠΎΠ²ΡΠ²Π°ΡΡΡΡ Π² Π΅Π΄ΠΈΠ½ΡΠΉ ΡΠΎΡΡΠ°Π²Π½ΠΎΠΉ ΡΠ½ΠΈΠΌΠΎΠΊ ΠΈ ΠΏΡΠΈ ΠΏΠΎΠΌΠΎΡΠΈ Π²ΡΡΠΎΠΊΠΎΡΠΊΠΎΡΠΎΡΡΠ½ΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ²Π°Π·ΠΈΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ ΡΠ΅Π΄ΡΡΠΈΡΡΡΡΡΡ Π΄ΠΎ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ
ΡΠ²Π΅ΡΠΎΠ² Ρ ΡΠΎΡ
ΡΠ°Π½Π΅Π½ΠΈΠ΅ΠΌ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠ½ΡΡ
Π³ΡΠ°Π½ΠΈΡ. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠ²Π°Π·ΠΈΠΎΠΏΡΠΈΠΌΠ°Π»ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠΈ ΡΠ΅ΡΠΈΠΈ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΠΉ Ρ ΠΏΠΎΡΡΠ΅ΠΏΠ΅Π½Π½ΠΎ ΡΠ²Π΅Π»ΠΈΡΠΈΠ²Π°ΡΡΠ΅ΠΉΡΡ Π΄Π΅ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ Π·Π° ΡΡΠ΅Ρ ΠΏΠ΅ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΡΠ»Π° ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ². ΠΡΠ° ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ Π²ΡΠ±ΡΠ°ΡΡ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ΡΡΠΈΠ΅ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΡ ΠΏΠ°Ρ ΡΠΎΡΡΡΠΊΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈΠ· ΡΠ΅ΡΠΈΠΈ ΡΠ³Π΅Π½Π΅ΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
.
ΠΠ° ΠΏΠ°ΡΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈΠ· Π²ΡΠ±ΡΠ°Π½Π½ΠΎΠ³ΠΎ ΡΠ°Π·Π±ΠΈΠ΅Π½ΠΈΡ ΡΠΎΡΡΡΠΊΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΡΠ½ΠΈΠΌΠΊΠ° ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠΈΡΠΊ ΠΎΠΏΠΎΡΠ½ΡΡ
ΡΠΎΡΠ΅ΠΊ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΡ
ΠΊΠΎΠ½ΡΡΡΠΎΠ². ΠΠ»Ρ ΡΡΠΈΡ
ΡΠΎΡΠ΅ΠΊ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ΅ΡΡΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΈ ΠΏΠΎΡΠ»Π΅ Π΅Π³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ΅ΡΡΡ ΠΎΡΠ΅Π½ΠΊΠ° ΡΡΠ΅ΠΏΠ΅Π½ΠΈ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΠΈ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ. ΠΠ°ΠΊ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅ ΠΎΠΏΠΎΡΠ½ΡΡ
ΡΠΎΡΠ΅ΠΊ ΠΊΠΎΠ½ΡΡΡΠ°, ΡΠ°ΠΊ ΠΈ ΡΠ°ΠΌΠΎ ΠΈΡΠΊΠΎΠΌΠΎΠ΅ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ΅ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΡΡΠΎΡΠ½ΡΠ΅ΡΡΡ Π΄ΠΎ ΡΠ΅Ρ
ΠΏΠΎΡ, ΠΏΠΎΠΊΠ° ΠΎΡΠ΅Π½ΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π΅ Π±ΡΠ΄Π΅Ρ ΠΏΡΠΈΠ΅ΠΌΠ»Π΅ΠΌΠΎΠΉ. ΠΠΈΠ΄ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎΠ΄Π±ΠΈΡΠ°Π΅ΡΡΡ ΠΏΠΎ ΡΠ΅Π΄ΡΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠΌ ΠΏΠΎ ΡΠ²Π΅ΡΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ, Π° Π·Π°ΡΠ΅ΠΌ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΡΡΡ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ. ΠΡΠΎΡ ΠΏΡΠΎΡΠ΅ΡΡ ΠΏΠΎΠ²ΡΠΎΡΡΠ΅ΡΡΡ Π΄Π»Ρ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ Π±ΠΎΠ»ΡΡΠ΅ΠΉ Π΄Π΅ΡΠ°Π»ΠΈΠ·Π°ΡΠΈΠ΅ΠΉ Π² ΡΠΎΠΌ ΡΠ»ΡΡΠ°Π΅, Π΅ΡΠ»ΠΈ ΠΎΡΠ΅Π½ΠΊΠ° ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π½Π΅ ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΡΠΈΠ΅ΠΌΠ»Π΅ΠΌΠΎΠΉ. Π¦Π΅Π»ΡΡ Π½Π°ΡΡΠΎΡΡΠ΅Π³ΠΎ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠΏΠΎΡΠΎΠ±Π°, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠ΅Π³ΠΎ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°ΡΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ ΠΈΠ· ΡΠ°Π·Π½ΠΎΡΠΎΡΠΌΠ°ΡΠ½ΡΡ
ΠΈ ΡΠ°Π·Π½ΠΎΡΠΎΠ΄Π½ΡΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ².
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ»Π΅Π΄ΡΡΡΠΈΠ΅ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΠΏΠΎΡΠΎΠ±Π° ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ. ΠΠ΅ΡΠ²Π°Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ Π΅Π΄ΠΈΠ½ΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΈΠ· ΠΏΠ°ΡΡ ΡΠΎΡΡΡΠΊΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΡΡ
ΠΎΠ΄Π½ΡΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ² Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠΌ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·Π°ΡΠΈΠΈ ΠΏΠΈΠΊΡΠ΅Π»Π΅ΠΉ, ΡΡΠΎ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ Π²ΡΠ΄Π΅Π»ΠΈΡΡ ΠΎΠ΄ΠΈΠ½Π°ΠΊΠΎΠ²ΡΠ΅ ΠΎΠ±Π»Π°ΡΡΠΈ Π½Π° Π΅Π³ΠΎ ΡΠ°Π·Π»ΠΈΡΠ½ΡΡ
ΡΠ°ΡΡΡΡ
. ΠΡΠΎΡΠ°Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡ Π·Π°ΠΊΠ»ΡΡΠ°Π΅ΡΡΡ Π² ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΠ΅ΠΎΠ±ΡΠ°Π·ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎ Π²ΡΠ΄Π΅Π»Π΅Π½Π½ΡΠΌ ΡΠΎΡΠΊΠ°ΠΌ ΠΊΠΎΠ½ΡΡΡΠ° Π½Π° ΠΎΠ±ΡΠ°Π±ΠΎΡΠ°Π½Π½ΠΎΠΉ ΠΏΠ°ΡΠ΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π½Π½ΡΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ², ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΡΠ΅ΡΡΡ ΠΊ ΠΈΡΡ
ΠΎΠ΄Π½ΡΠΌ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡΠΌ Π΄Π»Ρ ΠΈΡ
ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ.
Π ΡΠ°Π±ΠΎΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΊΠ°ΠΊ ΠΏΠΎ ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΡΠΌ (ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ) ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ, ΡΠ°ΠΊ ΠΈ ΠΏΠΎ ΡΠ°Π·Π½ΠΎΡΠΎΠ΄Π½ΡΠΌ (ΡΠ°Π΄ΠΈΠΎΠ»ΠΎΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠΌ ΠΈ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ) ΡΠ½ΠΈΠΌΠΊΠ°ΠΌ. ΠΡΠ»ΠΈΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΡΡΠΎΠΉ ΠΏΡΠ΅Π΄Π»Π°Π³Π°Π΅ΠΌΠΎΠ³ΠΎ ΡΠΏΠΎΡΠΎΠ±Π° ΡΠ²Π»ΡΠ΅ΡΡΡ ΡΠ»ΡΡΡΠ΅Π½ΠΈΠ΅ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΠΎΡΡΠΈ ΠΈΡΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ Π·Π΅ΠΌΠ½ΠΎΠΉ ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΠΈ
Remote sensing methods for biodiversity monitoring with emphasis on vegetation height estimation and habitat classification
Biodiversity is a principal factor for ecosystem stability and functioning, and the need for its protection has been identified as imperative globally. Remote sensing can contribute to timely and accurate monitoring of various elements related to biodiversity, but knowledge gap with user communities hinders its widespread operational use. This study advances biodiversity monitoring through earth observation data by initially identifying, reviewing, and proposing state-of-the-art remote sensing methods which can be used for the extraction of a number of widely adopted indicators of global biodiversity assessment. Then, a cost and resource effective approach is proposed for vegetation height estimation, using satellite imagery from very high resolution passive sensors. A number of texture features are extracted, based on local variance, entropy, and local binary patterns, and processed through several data processing, dimensionality reduction, and classification techniques. The approach manages to discriminate six vegetation height categories, useful for ecological studies, with accuracies over 90%. Thus, it offers an effective approach for landscape analysis, and habitat and land use monitoring, extending previous approaches as far as the range of height and vegetation species, synergies of multi-date imagery, data processing, and resource economy are regarded. Finally, two approaches are introduced to advance the state of the art in habitat classification using remote sensing data and pre-existing land cover information. The first proposes a methodology to express land cover information as numerical features and a supervised classification framework, automating the previous labour- and time-consuming rule-based approach used as reference. The second advances the state of the art incorporating DempsterβShafer evidential theory and fuzzy sets, and proves successful in handling uncertainties from missing data or vague rules and offering wide user defined parameterization potential. Both approaches outperform the reference study in classification accuracy, proving promising for biodiversity monitoring, ecosystem preservation, and sustainability management tasks.Open Acces
Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends
This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application's objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models' principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems
Terrain classification using machine learning algorithms in a multi-temporal approach A QGIS plug-in implementation
Land cover and land use (LCLU) maps are essential for the successful administration
of a nationβs topography, however, conventional on-site data gathering methods are costly
and time-consuming. By contrast, remote sensing data can be used to generate up-to-date
maps regularly with the help of machine learning algorithms, in turn, allowing for the
assessment of a regionβs dynamics throughout time.
The present dissertation will focus on the implementation of an automated land
use and land cover classifier based on remote sensing imagery provided by the mod ern sentinel-2 satellite constellation. The project, with Portugal at its focus, will expand
on previous approaches by utilizing temporal data as an input variable in order to harvest
the contextual information contained in the vegetation cycles.
The pursued solution investigated the implementation of a 9-class classifier plug-in
for an industry standard, open-source geographic information system. In the course of
the testing procedure, various processing techniques and machine learning algorithms
were evaluated in a multi-temporal approach. Resulting in a final overall accuracy of
65,9% across the targeted classes.Mapas de uso e ocupação do solo são cruciais para o entendimento e administração
da topografia de uma nação, no entanto, os métodos convencionais de aquisição local de
dados sΓ£o caros e demorados. Contrariamente, dados provenientes de mΓ©todos de senso riamento remoto podem ser utilizados para gerar regularmente mapas atualizados com
a ajuda de algoritmos de aprendizagem automÑtica. Permitindo, por sua vez, a avaliação
da dinΓ’mica de uma regiΓ£o ao longo do tempo.
Utilizando como base imagens de sensoriamento remoto fornecidas pela recente cons telação de satélites Sentinel-2, a presente dissertação concentra-se na implementação de
um classificador de mapas de uso e ocupação do solo automatizado. O projeto, com foco
em Portugal, irΓ‘ procurar expandir abordagens anteriores atravΓ©s do aproveitamento de
informação contextual contida nos ciclos vegetativos pela utilização de dados temporais
adicionais.
A solução adotada investigou a produção e implementação de um classificador geral
de 9 classes num plug-in de um sistema de informação geogrÑfico de código aberto.
Durante o processo de teste, diversas tΓ©cnicas de processamento e mΓΊltiplos algoritmos de
aprendizagem automΓ‘tica foram avaliados numa abordagem multi-temporal, culminando
num resultado final de precisΓ£o geral de 65,9% nas classes avaliadas
Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.
By definition of Wikipedia, βbig data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualizationβ.
Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) βbig dataβ into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases.
In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product.
Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery.
The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations
Remote Sensing of Natural Hazards
Each year, natural hazards such as earthquakes, cyclones, flooding, landslides, wildfires, avalanches, volcanic eruption, extreme temperatures, storm surges, drought, etc., result in widespread loss of life, livelihood, and critical infrastructure globally. With the unprecedented growth of the human population, largescale development activities, and changes to the natural environment, the frequency and intensity of extreme natural events and consequent impacts are expected to increase in the future.Technological interventions provide essential provisions for the prevention and mitigation of natural hazards. The data obtained through remote sensing systems with varied spatial, spectral, and temporal resolutions particularly provide prospects for furthering knowledge on spatiotemporal patterns and forecasting of natural hazards. The collection of data using earth observation systems has been valuable for alleviating the adverse effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision-support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.With these in mind, this book seeks original contributions to the advanced applications of remote sensing and geographic information systems (GIS) techniques in understanding various dimensions of natural hazards through new theory, data products, and robust approaches
A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery
Semantic segmentation (classification) of Earth Observation imagery is a
crucial task in remote sensing. This paper presents a comprehensive review of
technical factors to consider when designing neural networks for this purpose.
The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural
Networks (RNNs), Generative Adversarial Networks (GANs), and transformer
models, discussing prominent design patterns for these ANN families and their
implications for semantic segmentation. Common pre-processing techniques for
ensuring optimal data preparation are also covered. These include methods for
image normalization and chipping, as well as strategies for addressing data
imbalance in training samples, and techniques for overcoming limited data,
including augmentation techniques, transfer learning, and domain adaptation. By
encompassing both the technical aspects of neural network design and the
data-related considerations, this review provides researchers and practitioners
with a comprehensive and up-to-date understanding of the factors involved in
designing effective neural networks for semantic segmentation of Earth
Observation imagery.Comment: 145 pages with 32 figure