Automatic cloud masking of Earth observation images is one of the first required steps in optical remote sensing data processing since the operational use and product generation from satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions and the scarcity of labeled data force us to cast cloud screening as an unsupervised change detection problem in the temporal domain. We introduce a cloud screening method based on detecting abrupt changes along the time dimension. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes will be mainly due to the presence of clouds. The method estimates the background surface changes using the information in the time series. In particular, we propose linear and nonlinear least squares regression algorithms that minimize both the prediction and the estimation error simultaneously. Then, significant differences in the image of interest with respect to the estimated background are identified as clouds. The use of kernel methods allows the generalization of the algorithm to account for higher-order (nonlinear) feature relations. After the proposed cloud masking and cloud removal, cloud-free time series at high spatial resolution can be used to obtain a better monitoring of land cover dynamics and to generate more elaborated products. The method is tested in a dataset with 5-day revisit time series from SPOT-4 at high resolution and with Landsat-8 time series. Experimental results show that the proposed method yields more accurate cloud masks when confronted with state-of-the-art approaches typically used in operational settings. In addition, the algorithm has been implemented in the Google Earth Engine platform, which allows us to access the full Landsat-8 catalog and work in a parallel distributed platform to extend its applicability to a global planetary scale.
An automatic cloud masking is one of the first required processing steps since the operational use of satellite image time series might be hampered by undetected clouds. The high temporal revisit of current and forthcoming missions allows us to consider cloud screening as an unsupervised change detection problem in the temporal domain. Therefore, we propose a cloud screening method based on detecting abrupt changes in the temporal domain. The main assumption is that image time series follow smooth variations over land (background) and abrupt changes in certain spectral and spatial features will be mainly due to the presence of clouds. The method estimates the background and common surface changes using the full information in the time series. In particular, we propose linear and nonlinear least squares regression algorithms that minimize both the prediction and estimation error simultaneously. Then, significant differences in the image of interest with respect to the estimated background are identified as clouds. The use of kernel methods allows the generalization of the algorithm to account for higher-order (nonlinear) feature relations. After cloud detection, cloud-free time series at high spatial resolution can be used to obtain a better monitoring of the land cover dynamics and to generate more elaborated products. The proposed method is tested in a dataset with 5-day revisit time series from
SPOT-4 at high resolution and Landsat-8 time series.
Acousto-optic tunable filters (AOTFs) can be used as spectral filters in multispectral imaging applications. Acousto-optic crystals diffract a single wavelength from a broadband light beam, depending on the applied radio frequency signal. However, experimental measurements show that the actual performance is far from the expected behavior. We present an experimental characterization of several commercial off-the-shelf AOTFs for the implementation of multispectral imaging instruments. The diffraction performance of three bare crystals is compared, while a fourth AOTF crystal is mounted on the optical path of a multispectral imager to evaluate its performance. The experiments show that the behavior of all the analyzed AOTFs differs from the theoretical expectations and presents uneven diffraction efficiency, a Gaussian dependency with the applied power, and a strong nonlinear relationship with the driving signal frequency. The different behavior of each AOTF in terms of all the analyzed parameters shows the necessity for an in-depth characterization of the AOTF performance once mounted on a multispectral imaging device if quantitative measurements are required. Finally, several recommendations for use are derived from these experimental results.
Acousto-optic tunable filters (AOTFs) can be used as spectral filters for the implementation of multispectral imaging systems. However, obtaining quality images is challenging. In this work, we propose several improvements that enable the use of these systems in quantitative spectroscopic imaging applications. The improvements are based on three pillars: 1. a finer spectral bandpass shaping by dynamically optimizing the radio frequency (rf) driving signal, 2. an extensive calibration process, and 3. careful image preprocessing that uses calibration data to correct some well known AOTF issues in imaging applications. A novel multispectral imaging instrument is built using commercial off-the-shelf components. The instrument includes an Isomet (Springfield, New Jersey) AOTF working in the visible and near-infrared range, and a new concept of rf generator based on a high-speed digital-to-analog converter that allows the generation of multiband signals. The ancillary control software performs the main part of the image optimization process: an initial calibration, a dynamic adjustment of the rf driving signal power and exposure time, and finally the radiometric preprocessing of the acquired multispectral images. Finally, some results of the instrument performance are presented that show the achieved spectral and spatial resolution on different imaging scenarios.
Identification of land cover types is one of the most critical activities in remote sensing. Nowadays, managing
land resources by using remote sensing techniques is becoming a common procedure to speed up the process while
reducing costs. However, data analysis procedures should satisfy the accuracy figures demanded by institutions
and governments for further administrative actions.
This paper presents a methodological scheme to update the citrus Geographical Information Systems (GIS)
of the Comunidad Valenciana autonomous region (Spain). The proposed approach introduces a multi-stage
automatic scheme to reduce visual photointerpretation and ground validation tasks. First, an object-oriented
feature extraction process is carried out for each cadastral parcel from very high spatial resolution (VHR) images
(0.5m) acquired in the visible and near infrared. Next, several automatic classifiers (decision trees, multilayer
perceptron, and support vector machines) are trained and combined to improve the final accuracy of the results.
The proposed strategy fulfills the high accuracy demanded by policy makers by means of combining automatic
classification methods with visual photointerpretation available resources. A level of confidence based on the
agreement between classifiers allows us an effective management by fixing the quantity of parcels to be reviewed.
The proposed methodology can be applied to similar problems and applications.
Chlorophyll fluorescence (Chf) emission allows estimating the photosynthetic activity of vegetation - a key parameter for the carbon cycle models - in a quite direct way. However, measuring Chf is difficult because it represents a small fraction of the radiance to be measured by the sensor. This paper analyzes the relationship between the solar induced Chf emission and the photosynthetically active radiation (PAR) in plants under water stress condition. The solar induced fluorescence emission is measured at leaf level by means of three different methodologies. Firstly, an active modulated light fluorometer gives the relative fluorescence yield. Secondly, a quantitative measurement of the Chf signal is derived from the leaf radiance by using the Fraunhofer Line-Discriminator (FLD) principle, which allows the measurement of Chf in the atmospheric absorption bands. Finally, the actual radiance spectrum of the leaf fluorescence emission is measured by a field spectroradiometer using a device that filters out the incident light in the Chf emission spectral range. The diurnal cycle of fluorescence emission has been measured for both healthy and stressed plants in natural and simulated conditions. The main achievements of this work have been: (1) successful radiometric spectral measurement of the solar induced fluorescence; (2) identification of fluorescence behavior under stress conditions; and (3) establishing a relationship between full spectral measurements with the signal provided by the FLD method. These results suggest the best time of the day to maximize signal levels while identifying vegetation stress status.
An imaging spectrometer covering the 400– to 1000–nm band is conceived and developed. The system is based on an acousto-optic tunable filter (AOTF) attached to a high-performance digital camera. The AOTF enables the selection of spectral bands with an rf signal in the range of 70 to 218 MHz. It includes a telecentric optical system that enhances system efficiency. Additionally, a smart choice of integration time reduces the dependence of the efficiency on the frequency. Calibration includes filter characterization and compensation of crystal nonconstant diffraction efficiency and spatial nonhomogeneity. The system is controlled by a PC application specifically developed for this purpose, providing wide versatility, while enabling transparent and intuitive management to nonexpert users. The spectrometer is validated by estimating the light absorption of leaves and their chlorophyll content.
Accurate and automatic detection of clouds in satellite scenes is a key issue for a wide range of remote sensing applications. With no accurate cloud masking, undetected clouds are one of the most significant source of error in both sea and land cover biophysical parameter retrieval. Sensors with spectral channels beyond 1 um have demonstrated good capabilities to perform cloud masking. This spectral range can not be exploited by recently developed hyperspectral sensors that work in the spectral range between 400- 1000 nm. However, one can take advantage of their high number of channels and spectral resolution to increase the cloud detection accuracy, and to describe properly the detected clouds (cloud type, height, subpixel coverage, could shadows, etc.) In this paper, we present a methodology for cloud detection that could be used by sensors working in the VNIR range. First, physically-inspired features are extracted (TOA reflectance and their spectral derivatives, atmospheric oxygen and water vapour absorptions, etc). Second, growing maps are built from cloud-like pixels to select regions which potentially could contain clouds. Then, an unsupervised clustering algorithm is applied in these regions using all extracted features. The obtained clusters are labeled into geo-physical classes taking into account the spectral signature of the cluster centers. Finally, an spectral unmixing algorithm is applied to the segmented image in order to obtain an abundance map of the cloud content in the cloud pixels. As a direct consequence of the detection scheme, the proposed system is capable to yield probabilistic outputs on cloud detected pixels in the image, rather than flags. Performance of the proposed algorithm is tested on six CHRIS/Proba Mode 1 images, which presents a spatial resolution of 32 m, 62 spectral bands with 6-20 nm bandwidth, and multiangularity.
In this communication, we evaluate the performance of the relevance vector machine (RVM) (Tipping,2000) for the estimation of biophysical parameters from remote sensing images. For illustration purposes, we focus on the estimation of chlorophyll concentrations from multispectral imagery, whose measurements are subject to high levels of uncertainty, both regarding the difficulties in ground-truth data acquisition, and when comparing in situ measurements against satellite-derived data. Moreover, acquired data are commonly affected by noise in the acquisition phase, and time mismatch between the acquired image and the recorded measurements, which is critical for instance for coastal water monitoring. In this context, robust and stable regressors that provide inverse models are desirable. Lately, the use of the support vector regressor (SVR) has produced good results to this end. However, the SVR has many deficiencies, which could be theoretically alleviated by the RVM. In this paper, performance of the RVM is benchmarked with SVR in terms of accuracy and bias of the estimations, sparseness of the solutions, distribution of the residuals, robustness to low number of training samples, and computational burden. In addition, some theoretical issues are discussed, such as the sensitivity to hyperparameters setting, kernel selection, and confidence intervals on the predictions. Results suggest that RVM offer an excellent compromise between accuracy and sparsity of the solution, and reveal itself as less sensitive to selection of the free parameters. Some disadvantages are also pointed, such as the unintuitive confidence intervals provided and the computational cost.
This paper presents a new portable instrument called Autonomous Tunable Filtering System (ATFS), developed for highly customisable imaging spectrometry in the VIS-NIR range. The ATFS instrument consists of an Acousto-Optic Tunable Filter (AOTF), an optical system, a Radio Frequency (RF) driver based on a Direct Digital Synthesiser (DDS) and control software. The ATFS can be attached to a variety of high-performance monochrome cameras. The system works as a spectral bandpass filter whose wavelength can be selected between 400nm and 1000nm and whose bandwidth can be adjusted between 4nm and 50nm. The filter can be tuned electronically at a very high speed and accuracy, thanks to the DDS versatility. The control software synchronises the camera with the RF generation and implements a smart auto-exposure algorithm that maximises the dynamic range of the instrument for each band. The software can take a set of spectral images sequentially and save them in ENVI® multispectral format or as multiple TIFF images. The system has been validated using a reference point spectrometer. An optional acquisition procedure has been developed, based on the acquisition of dark and white Spectralon® reference images, in order to use the system in applications involving quantitative (reflectance) measurements. Procedures have been established in order to fully calibrate the instrument. The system has been demonstrated in a real world application, which uses the ATFS to map the leaf chlorophyll content from multispectral reflectance images.
An imaging spectrometer covering the 400-1000 nm band has been conceived and developed. The system is based on an Acousto-Optic Tunable Filter (AOTF) attached to a high performance digital camera. The AOTF permits the selection of spectral bands with an RF signal in the range of 70-210 MHz. The range is covered using two transducers attached to a single crystal. Although the idea is not new it covers a broader spectrum than previous systems. It includes a telecentric optical system that enhances system efficiency, by ensuring that the chief ray of each light cone emerges out of this doublet parallel to the optical axes. Additionally, an smart choice of integration time reduces the dependence of the efficiency on the frequency. Calibration includes filter characterisation and compensation of crystal non-constant diffraction efficiency and spatial non-homogeneity. The system is controlled by a PC application, specifically developed for this purpose, providing wide versatility, while enabling transparent and intuitive management to typical users.