195 research outputs found
Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements
This book is a reprint of the Special Issue entitled "Statistical and Machine Learning Models for Remote Sensing Data Mining - Recent Advancements" that was published in Remote Sensing, MDPI. It provides insights into both core technical challenges and some selected critical applications of satellite remote sensing image analytics
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Ocean remote sensing techniques and applications: a review (Part II)
As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version
ΠΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠ°Π·Π»ΠΈΠ²ΠΎΠ² Π½Π΅ΡΡΠΈ Π½Π° Π²ΠΎΠ΄Π½ΡΡ ΠΏΠΎΠ²Π΅ΡΡ Π½ΠΎΡΡΡΡ ΠΏΠΎ Π°ΡΡΠΎΡΠΎΡΠΎΡΠ½ΠΈΠΌΠΊΠ°ΠΌ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ
Π ΡΡΠ°ΡΡΠ΅ ΡΠ΅ΡΠ°Π΅ΡΡΡ Π·Π°Π΄Π°ΡΠ° Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΠ²ΠΎΠ² Π½Π΅ΡΡΠΈ Π½Π° Π²ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡΡ
ΡΠ΅ΠΊ, ΠΌΠΎΡΠ΅ΠΉ ΠΈ ΠΎΠΊΠ΅Π°Π½ΠΎΠ² ΠΏΠΎ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΠΌ Π°ΡΡΠΎΡΠΎΡΠΎΡΠ½ΠΈΠΌΠΊΠ°ΠΌ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π³Π»ΡΠ±ΠΎΠΊΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡΡ Π΄Π°Π½Π½ΠΎΠΉ Π·Π°Π΄Π°ΡΠΈ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π°Π»ΠΈΡΠΈΠ΅ Π²ΠΈΠ·ΡΠ°Π»ΡΠ½ΠΎ ΠΏΠΎΡ
ΠΎΠΆΠΈΡ
Π½Π° ΡΠ°Π·Π»ΠΈΠ²Ρ Π½Π΅ΡΡΠΈ ΠΎΠ±Π»Π°ΡΡΠ΅ΠΉ Π½Π° Π²ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡΡ
, Π²ΡΠ·Π²Π°Π½Π½ΡΡ
ΡΠ²Π΅ΡΠ΅Π½ΠΈΠ΅ΠΌ Π²ΠΎΠ΄ΠΎΡΠΎΡΠ»Π΅ΠΉ, Π²Π΅ΡΠ΅ΡΡΠ², Π½Π΅ ΠΏΡΠΈΠ½ΠΎΡΡΡΠΈΡ
ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΡΠ΅ΡΠ± (Π½Π°ΠΏΡΠΈΠΌΠ΅Ρ, ΠΏΠ°Π»ΡΠΌΠΎΠ²ΠΎΠ΅ ΠΌΠ°ΡΠ»ΠΎ), Π±Π»ΠΈΠΊΠΎΠ² ΠΏΡΠΈ ΡΡΠ΅ΠΌΠΊΠ΅ ΠΈΠ»ΠΈ ΠΏΡΠΈΡΠΎΠ΄Π½ΡΡ
ΡΠ²Π»Π΅Π½ΠΈΠΉ (ΡΠ°ΠΊ Π½Π°Π·ΡΠ²Π°Π΅ΠΌΡΠ΅ Β«Π΄Π²ΠΎΠΉΠ½ΠΈΠΊΠΈΒ»). ΠΠ½ΠΎΠ³ΠΈΠ΅ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ Π² Π΄Π°Π½Π½ΠΎΠΉ ΠΎΠ±Π»Π°ΡΡΠΈ ΠΎΡΠ½ΠΎΠ²Π°Π½Ρ Π½Π° Π°Π½Π°Π»ΠΈΠ·Π΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, ΠΏΠΎΠ»ΡΡΠ΅Π½Π½ΡΡ
ΠΎΡ ΡΠ°Π΄Π°ΡΠΎΠ² Ρ ΡΠΈΠ½ΡΠ΅Π·ΠΈΡΠΎΠ²Π°Π½Π½ΠΎΠΉ Π°ΠΏΠ΅ΡΡΡΡΠΎΠΉ (Synthetic Aperture Radar (SAR) images), ΠΊΠΎΡΠΎΡΡΠ΅ Π½Π΅ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡ ΡΠΎΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΈ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ. ΠΠΎΡΠ»Π΅Π΄ΡΡΡΠ°Ρ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡ ΡΠΏΠΎΡΠΎΠ±ΡΡΠ²ΡΠ΅Ρ ΡΠΎΠΊΡΠ°ΡΠ΅Π½ΠΈΡ ΡΠΊΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΈ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ΅ΡΠ±Π°, Π° ΠΌΠΎΠ½ΠΈΡΠΎΡΠΈΠ½Π³ ΡΠ°Π·ΠΌΠ΅ΡΠΎΠ² ΠΏΠ»ΠΎΡΠ°Π΄ΠΈ Π½Π΅ΡΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΡΠ½Π° ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΡΡΡ Π΄Π»Ρ ΠΏΡΠΈΠ½ΡΡΠΈΡ Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠΈΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠΉ ΠΏΠΎ ΡΡΡΡΠ°Π½Π΅Π½ΠΈΡ ΠΏΠΎΡΠ»Π΅Π΄ΡΡΠ²ΠΈΠΉ. ΠΡΠ΅Π΄Π»Π°Π³Π°Π΅ΡΡΡ Π½ΠΎΠ²ΡΠΉ ΠΏΠΎΠ΄Ρ
ΠΎΠ΄ ΠΊ Π²Π΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΎΠΏΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ½ΠΈΠΌΠΊΠΎΠ² ΠΊΠ°ΠΊ Π·Π°Π΄Π°ΡΠΈ Π±ΠΈΠ½Π°ΡΠ½ΠΎΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΈΠ°ΠΌΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ, ΠΊΠΎΠ³Π΄Π° ΡΡΠ°Π³ΠΌΠ΅Π½Ρ ΠΈΡΡ
ΠΎΠ΄Π½ΠΎΠ³ΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΡ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡΠ°ΡΠ½ΠΎ ΡΡΠ°Π²Π½ΠΈΠ²Π°Π΅ΡΡΡ Ρ ΡΠ΅ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΠ²Π½ΡΠΌΠΈ ΠΏΡΠΈΠΌΠ΅ΡΠ°ΠΌΠΈ ΠΈΠ· ΠΊΠ»Π°ΡΡΠ° Π½Π΅ΡΡΡΠ½ΡΡ
ΠΏΡΡΠ΅Π½ Π½Π° Π²ΠΎΠ΄Π½ΡΡ
ΠΏΠΎΠ²Π΅ΡΡ
Π½ΠΎΡΡΡΡ
. ΠΡΠ½ΠΎΠ²ΠΎΠΉ ΡΠΈΠ°ΠΌΡΠΊΠΎΠΉ ΡΠ΅ΡΠΈ ΡΠ»ΡΠΆΠΈΡ ΠΎΠ±Π»Π΅Π³ΡΠ΅Π½Π½Π°Ρ ΡΠ΅ΡΡ VGG16. ΠΡΠΈ ΠΏΡΠ΅Π²ΡΡΠ΅Π½ΠΈΠΈ ΠΏΠΎΡΠΎΠ³ΠΎΠ²ΠΎΠ³ΠΎ Π·Π½Π°ΡΠ΅Π½ΠΈΡ Π²ΡΡ
ΠΎΠ΄Π½ΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΠΏΡΠΈΠ½ΠΈΠΌΠ°Π΅ΡΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ ΠΎ Π½Π°Π»ΠΈΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΠ²Π° Π½Π΅ΡΡΠΈ. ΠΠ»Ρ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΡΠ΅ΡΠΈ Π±ΡΠ» ΡΠΎΠ±ΡΠ°Π½ ΠΈ ΡΠ°Π·ΠΌΠ΅ΡΠ΅Π½ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΡΠΉ Π½Π°Π±ΠΎΡ Π΄Π°Π½Π½ΡΡ
ΠΈΠ· ΠΎΡΠΊΡΡΡΡΡ
ΠΈΠ½ΡΠ΅ΡΠ½Π΅Ρ-ΡΠ΅ΡΡΡΡΠΎΠ². Π‘ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠΎΠΉ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π΅ΡΠ±Π°Π»Π°Π½ΡΠΈΡΠΎΠ²Π°Π½Π½ΠΎΡΡΡ Π²ΡΠ±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΠΊΠ»Π°ΡΡΠ°ΠΌ, ΡΡΠΎ ΠΏΠΎΡΡΠ΅Π±ΠΎΠ²Π°Π»ΠΎ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π°ΡΠ³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ, ΠΎΡΠ½ΠΎΠ²Π°Π½Π½ΡΡ
Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ Π½Π° Π³Π΅ΠΎΠΌΠ΅ΡΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈ ΡΠ²Π΅ΡΠΎΠ²ΡΡ
ΠΌΠ°Π½ΠΈΠΏΡΠ»ΡΡΠΈΡΡ
, Π½ΠΎ ΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π³Π΅Π½Π΅ΡΠ°ΡΠΈΠ²Π½ΠΎΠΉ ΡΠΎΡΡΡΠ·Π°ΡΠ΅Π»ΡΠ½ΠΎΠΉ ΡΠ΅ΡΠΈ (Generative Adversarial Network, GAN). ΠΠΊΡΠΏΠ΅ΡΠΈΠΌΠ΅Π½ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»ΠΈ, ΡΡΠΎ ΡΠΎΡΠ½ΠΎΡΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΡΠ°Π·Π»ΠΈΠ²ΠΎΠ² Π½Π΅ΡΡΠΈ ΠΈ Β«Π΄Π²ΠΎΠΉΠ½ΠΈΠΊΠΎΠ²Β» Π½Π° ΡΠ΅ΡΡΠΎΠ²ΠΎΠΉ Π²ΡΠ±ΠΎΡΠΊΠ΅ Π΄ΠΎΡΡΠΈΠ³Π°Π΅Ρ Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ 0,91 ΠΈ 0,834 ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ. ΠΠ°Π»Π΅Π΅ ΡΠ΅ΡΠ°Π΅ΡΡΡ Π΄ΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½Π°Ρ Π·Π°Π΄Π°ΡΠ° ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ Π½Π΅ΡΡΡΠ½ΠΎΠ³ΠΎ ΠΏΡΡΠ½Π° Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ ΡΠ²Π΅ΡΡΠΎΡΠ½ΡΡ
Π½Π΅ΠΉΡΠΎΠ½Π½ΡΡ
ΡΠ΅ΡΠ΅ΠΉ (Π‘ΠΠ‘) ΡΠΈΠΏΠ° ΠΊΠΎΠ΄ΠΈΡΠΎΠ²ΡΠΈΠΊ-Π΄Π΅ΠΊΠΎΠ΄ΠΈΡΠΎΠ²ΡΠΈΠΊ. ΠΠ»Ρ ΡΠ΅Π³ΠΌΠ΅Π½ΡΠ°ΡΠΈΠΈ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»ΠΈΡΡ ΡΡΠΈ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ Π³Π»ΡΠ±ΠΎΠΊΠΈΡ
ΡΠ΅ΡΠ΅ΠΉ, Π° ΠΈΠΌΠ΅Π½Π½ΠΎ U-Net, SegNet ΠΈ Poly-YOLOv3. ΠΡΡΡΠΈΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π»Π° ΡΠ΅ΡΡ Poly-YOLOv3, Π΄ΠΎΡΡΠΈΠ³Π½ΡΠ² ΡΠΎΡΠ½ΠΎΡΡΠΈ 0,97 ΠΏΡΠΈ ΡΡΠ΅Π΄Π½Π΅ΠΌ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ½ΠΈΠΌΠΊΠ° 385 Ρ Π²Π΅Π±-ΡΠ΅ΡΠ²ΠΈΡΠΎΠΌ Google Colab. Π’Π°ΠΊΠΆΠ΅ Π±ΡΠ»Π° ΡΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½Π° Π±Π°Π·Π° Π΄Π°Π½Π½ΡΡ
Π΄Π»Ρ Ρ
ΡΠ°Π½Π΅Π½ΠΈΡ ΠΈΡΡ
ΠΎΠ΄Π½ΡΡ
ΠΈ Π²Π΅ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΡ
ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Ρ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ½ΡΠΌΠΈ ΠΎΠ±Π»Π°ΡΡΡΠΌΠΈ
Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images
This paper presents a system for the detection of ships and oil spills using side-looking airborne radar (SLAR) images. The proposed method employs a two-stage architecture composed of three pairs of convolutional neural networks (CNNs). Each pair of networks is trained to recognize a single class (ship, oil spill, and coast) by following two steps: a first network performs a coarse detection, and then, a second specialized CNN obtains the precise localization of the pixels belonging to each class. After classification, a postprocessing stage is performed by applying a morphological opening filter in order to eliminate small look-alikes, and removing those oil spills and ships that are surrounded by a minimum amount of coast. Data augmentation is performed to increase the number of samples, owing to the difficulty involved in obtaining a sufficient number of correctly labeled SLAR images. The proposed method is evaluated and compared to a single multiclass CNN architecture and to previous state-of-the-art methods using accuracy, precision, recall, F-measure, and intersection over union. The results show that the proposed method is efficient and competitive, and outperforms the approaches previously used for this task.This work was supported in part by the Spanish Governmentβs Ministry of Economy, Industry, and Competitiveness under Project RTC-2014-1863-8 and in part by Babcock MCS Spain under Project INAER4-14Y (IDI-20141234)
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