LAKE ICE DETECTION IN LOW-RESOLUTION OPTICAL SATELLITE IMAGES

Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250&thinsp;m&ndash;1000&thinsp;m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters.

Download Full-text

Ice Monitoring in Swiss Lakes from Optical Satellites and Webcams Using Machine Learning

Remote Sensing ◽

10.3390/rs12213555 ◽

2020 ◽

Vol 12 (21) ◽

pp. 3555

Author(s):

Manu Tom ◽

Rajanie Prabha ◽

Tianyu Wu ◽

Emmanuel Baltsavias ◽

Laura Leal-Taixé ◽

...

Keyword(s):

Machine Learning ◽

Satellite Images ◽

Global Climate ◽

Semantic Segmentation ◽

Lake Ice ◽

Climate Indicators ◽

Multi Temporal ◽

Spatio Temporal ◽

Set Up ◽

Optical Satellite Images

Continuous observation of climate indicators, such as trends in lake freezing, is important to understand the dynamics of the local and global climate system. Consequently, lake ice has been included among the Essential Climate Variables (ECVs) of the Global Climate Observing System (GCOS), and there is a need to set up operational monitoring capabilities. Multi-temporal satellite images and publicly available webcam streams are among the viable data sources capable of monitoring lake ice. In this work we investigate machine learning-based image analysis as a tool to determine the spatio-temporal extent of ice on Swiss Alpine lakes as well as the ice-on and ice-off dates, from both multispectral optical satellite images (VIIRS and MODIS) and RGB webcam images. We model lake ice monitoring as a pixel-wise semantic segmentation problem, i.e., each pixel on the lake surface is classified to obtain a spatially explicit map of ice cover. We show experimentally that the proposed system produces consistently good results when tested on data from multiple winters and lakes. Our satellite-based method obtains mean Intersection-over-Union (mIoU) scores > 93%, for both sensors. It also generalises well across lakes and winters with mIoU scores > 78% and >80% respectively. On average, our webcam approach achieves mIoU values of ≈87% and generalisation scores of ≈71% and ≈69% across different cameras and winters respectively. Additionally, we generate and make available a new benchmark dataset of webcam images (Photi-LakeIce) which includes data from two winters and three cameras.

Download Full-text

Ship Detection in Optical Satellite Images via Directional Bounding Boxes Based on Ship Center and Orientation Prediction

Remote Sensing ◽

10.3390/rs11182173 ◽

2019 ◽

Vol 11 (18) ◽

pp. 2173 ◽

Cited By ~ 4

Author(s):

Jinlei Ma ◽

Zhiqiang Zhou ◽

Bo Wang ◽

Hua Zong ◽

Fei Wu

Keyword(s):

Remote Sensing ◽

Satellite Images ◽

Detection Method ◽

Semantic Segmentation ◽

Detection Methods ◽

Optical Remote Sensing ◽

Ship Detection ◽

Bounding Boxes ◽

Typical Object ◽

Optical Satellite Images

To accurately detect ships of arbitrary orientation in optical remote sensing images, we propose a two-stage CNN-based ship-detection method based on the ship center and orientation prediction. Center region prediction network and ship orientation classification network are constructed to generate rotated region proposals, and then we can predict rotated bounding boxes from rotated region proposals to locate arbitrary-oriented ships more accurately. The two networks share the same deconvolutional layers to perform semantic segmentation for the prediction of center regions and orientations of ships, respectively. They can provide the potential center points of the ships helping to determine the more confident locations of the region proposals, as well as the ship orientation information, which is beneficial to the more reliable predetermination of rotated region proposals. Classification and regression are then performed for the final ship localization. Compared with other typical object detection methods for natural images and ship-detection methods, our method can more accurately detect multiple ships in the high-resolution remote sensing image, irrespective of the ship orientations and a situation in which the ships are docked very closely. Experiments have demonstrated the promising improvement of ship-detection performance.

Download Full-text

Identifying permafrost slope disturbance using multi-temporal optical satellite images and change detection techniques

Cold Regions Science and Technology ◽

10.1016/j.coldregions.2012.12.008 ◽

2013 ◽

Vol 88 ◽

pp. 37-49 ◽

Cited By ~ 24

Author(s):

Ashley C.A. Rudy ◽

Scott F. Lamoureux ◽

Paul Treitz ◽

Adam Collingwood

Keyword(s):

Change Detection ◽

Satellite Images ◽

Detection Techniques ◽

Multi Temporal ◽

Optical Satellite Images

Download Full-text

LAKE ICE DETECTION FROM SENTINEL-1 SAR WITH DEEP LEARNING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-3-2020-409-2020 ◽

2020 ◽

Vol V-3-2020 ◽

pp. 409-416 ◽

Cited By ~ 3

Author(s):

M. Tom ◽

R. Aguilar ◽

P. Imhof ◽

S. Leinss ◽

E. Baltsavias ◽

...

Keyword(s):

Global Climate ◽

Semantic Segmentation ◽

Limiting Factor ◽

Lake Ice ◽

Convolutional Network ◽

Important Indicator ◽

Proposed Model ◽

Spatio Temporal ◽

Ice Detection ◽

Validation Tests

Abstract. Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN).We report results on two winters (2016–17 and 2017–18) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters.

Download Full-text

A NOVEL SHIP DETECTION METHOD FOR LARGE-SCALE OPTICAL SATELLITE IMAGES BASED ON VISUAL LBP FEATURE AND VISUAL ATTENTION MODEL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-917-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 917-921

Author(s):

Sui Haigang ◽

Song Zhina

Keyword(s):

Visual Attention ◽

Large Scale ◽

Satellite Images ◽

Visual Features ◽

Support Vector ◽

Biologically Inspired ◽

Visual Attention Model ◽

Attention Model ◽

Ship Detection ◽

Optical Satellite Images

Reliably ship detection in optical satellite images has a wide application in both military and civil fields. However, this problem is very difficult in complex backgrounds, such as waves, clouds, and small islands. Aiming at these issues, this paper explores an automatic and robust model for ship detection in large-scale optical satellite images, which relies on detecting statistical signatures of ship targets, in terms of biologically-inspired visual features. This model first selects salient candidate regions across large-scale images by using a mechanism based on biologically-inspired visual features, combined with visual attention model with local binary pattern (CVLBP). Different from traditional studies, the proposed algorithm is high-speed and helpful to focus on the suspected ship areas avoiding the separation step of land and sea. Largearea images are cut into small image chips and analyzed in two complementary ways: Sparse saliency using visual attention model and detail signatures using LBP features, thus accordant with sparseness of ship distribution on images. Then these features are employed to classify each chip as containing ship targets or not, using a support vector machine (SVM). After getting the suspicious areas, there are still some false alarms such as microwaves and small ribbon clouds, thus simple shape and texture analysis are adopted to distinguish between ships and nonships in suspicious areas. Experimental results show the proposed method is insensitive to waves, clouds, illumination and ship size.

Download Full-text

Land use and land cover inference in large areas using multi-temporal optical satellite images

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS ◽

10.1109/igarss.2013.6723541 ◽

2013 ◽

Cited By ~ 2

Author(s):

Shutaro Hashimoto ◽

Takeo Tadono ◽

Masahiko Onosato ◽

Masahiro Hori

Keyword(s):

Land Use ◽

Land Cover ◽

Satellite Images ◽

Multi Temporal ◽

Optical Satellite Images

Download Full-text

A NOVEL SHIP DETECTION METHOD FOR LARGE-SCALE OPTICAL SATELLITE IMAGES BASED ON VISUAL LBP FEATURE AND VISUAL ATTENTION MODEL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b3-917-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 917-921 ◽

Cited By ~ 1

Author(s):

Sui Haigang ◽

Song Zhina

Keyword(s):

Visual Attention ◽

Large Scale ◽

Satellite Images ◽

Visual Features ◽

Support Vector ◽

Biologically Inspired ◽

Visual Attention Model ◽

Attention Model ◽

Ship Detection ◽

Optical Satellite Images

Download Full-text

AUTOMATIC URBAN ILLEGAL BUILDING DETECTION USING MULTI-TEMPORAL SATELLITE IMAGES AND GEOSPATIAL INFORMATION SYSTEMS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-1-w5-387-2015 ◽

2015 ◽

Vol XL-1-W5 ◽

pp. 387-393 ◽

Cited By ~ 5

Author(s):

N. Khalili Moghadam ◽

M. R. Delavar ◽

P. Hanachee

Keyword(s):

Change Detection ◽

Satellite Images ◽

Building Detection ◽

Existing Buildings ◽

Geospatial Information ◽

Processing Step ◽

Multi Temporal ◽

Under Construction ◽

Processing Techniques ◽

The City

With the unprecedented growth of urban population and urban development, we are faced with the growing trend of illegal building (IB) construction. Field visit, as the currently used method of IB detection, is time and man power consuming, in addition to its high cost. Therefore, an automatic IB detection is required. Acquiring multi-temporal satellite images and using image processing techniques for automatic change detection is one of the optimum methods which can be used in IB monitoring. In this research an automatic method of IB detection has been proposed. Two-temporal panchromatic satellite images of IRS-P5 of the study area in a part of Tehran, the city map and an updated spatial database of existing buildings were used to detect the suspected IBs. In the pre-processing step, the images were geometrically and radiometrically corrected. In the next step, the changed pixels were detected using K-means clustering technique because of its quickness and less user’s intervention required. Then, all the changed pixels of each building were identified and the change percentage of each building with the standard threshold of changes was compared to detect the buildings which are under construction. Finally, the IBs were detected by checking the municipality database. The unmatched constructed buildings with municipal database will be field checked to identify the IBs. The results show that out of 343 buildings appeared in the images; only 19 buildings were detected as under construction and three of them as unlicensed buildings. Furthermore, the overall accuracies of 83%, 79% and 75% were obtained for K-means change detection, detection of under construction buildings and IBs detection, respectively.

Download Full-text