https://gispub4.epa.gov/APS/documents/MODIS_Change_Detection.10_06.pdf

Land-cover change detection using multi-temporal MODIS NDVI data

Saliency detection has been a hot topic in recent years, and many efforts have been devoted in this area. Unfortunately, the results of saliency detection can hardly be utilized in general applications. The primary reason, we think, is unspecific definition of salient objects, which makes that the previously published methods cannot extend to practical applications. To solve this problem, we claim that saliency should be defined in a context and the salient band selection in hyperspectral image (HSI) is introduced as an example. Unfortunately, the traditional salient band selection methods suffer from the problem of inappropriate measurement of band difference. To tackle this problem, we propose to eliminate the drawbacks of traditional salient band selection methods by manifold ranking. It puts the band vectors in the more accurate manifold space and treats the saliency problem from a novel ranking perspective, which is considered to be the main contributions of this paper. To justify the effectiveness of the proposed method, experiments are conducted on three HSIs, and our method is compared with the six existing competitors. Results show that the proposed method is very effective and can achieve the best performance among the competitors.

Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking

Non-stationary time series (TS) analysis has gained an explosive interest over the recent decades in different applied sciences. In fact, several decomposition methods were developed in order to extract various components (e.g., seasonal, trend and abrupt components) from the non-stationary TS, which allows for an improved interpretation of the temporal variability. The wavelet transform (WT) has been successfully applied over an extraordinary range of fields in order to decompose the non-stationary TS into time-frequency domain. For this reason, the WT method is briefly introduced and reviewed in this paper. In addition, this latter includes different research and applications of the WT to non-stationary TS in seven different applied sciences fields, namely the geo-sciences and geophysics, remote sensing in vegetation analysis, engineering, hydrology, finance, medicine, and other fields, such as ecology, renewable energy, chemistry and history. Finally, five challenges and future works, such as the selection of the type of wavelet, selection of the adequate mother wavelet, selection of the scale, the combination between wavelet transform and machine learning algorithm and the interpretation of the obtained components, are also discussed.

Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review

: This report discusses the use of modern 2-D spectral estimation algorithms for SAR imaging, and makes two principal contributions to the field of adaptive SAR imaging. First, it is a comprehensive comparison of 2-D spectral estimation methods for SAR imaging. It provides a synopsis of the algorithms available, discusses their relative merits for SAR imaging, and illustrates their performance on simulated and collected SAR imagery. The discussion of autoregressive linear predictive techniques (ARLP), including the Tufts Kumaresan variant, is somewhat more general than appears in most of the literature, in that it allows the prediction element to be varied throughout the subaperture. This generality leads to a theoretical link between ARLP and one of Pisarenko's methods. The report also provides a theoretical analysis that predicts the impact of the adaptive sidelobe reduction (ASR) algorithm on target to clutter ratio and provides insight into order and constraint selection. Second, this work develops multi-channel variants of three related algorithms, minimum variance method (MVM), reduced rank MVM (RRMVM), and ASR to estimate both reflectivity intensity and interferometric height from polarimetric displaced-aperture interferometric data. Examples illustrate that MVM and ASR both offer significant advantages over Fourier methods for estimating both scattering intensity and interferometric height, and allow empirical comparison of the accuracies of Fourier, MVM, and- ometric height estimates.

SAR Imaging via Modern 2-D Spectral Estimation Methods. Volume 1. Imaging Methods.

An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data

Temporal vegetation signatures (i.e., vegetation indices as functions of time) generated using the MODIS imagery poses many challenges, primarily due to signal-to-noise-related issues. This article...

Denoising and Wavelet-Based Feature Extraction of MODIS Multi-Temporal Vegetation Signatures

Temporal vegetation signatures (i.e., vegetation indices as functions of time) generated using the MODIS imagery poses many challenges, primarily due to signal to noise-related issues. This article describes the use of MODIS time-series data for the detection of specific tropical invasive species vegetation types. Due to challenges with the MODIS quality assurance data, a significant level of noise was present in the temporal signatures. This study investigated methods for denoising the vegetation temporal signatures, followed by a comparative analysis of three denois- ing methods to generate signatures for vegetation target detection. The analytical approach focused on the use of wavelet-based versus Fourier-based feature extrac- tion methods. Methods included the development of a novel wavelet-based feature extraction method that quantifies the shape of the fundamental in the temporal signa- tures.

/pdf/denoising-and-wavelet-based-feature-extraction-of-modis-2ddsezuz6z.pdf

Denoising and wavelet-based feature extraction of MODIS multi-temporal vegetation signatures

In recent years, more intuitive understanding about the characteristics of higher dimensional space has influenced the development of subsequent data analysis and classification algorithms in the field of hyperspectral remote sensing Earlier data analysis and classification algorithms rely on processing high dimensional space as a whole to extract a lower dimensional feature space The major impediment on these techniques is the limited training data size, which does not confer with the large dimensionality of hyperspectral data Previous work has shown that statistically reliable parameter estimation can be performed on lower dimensional subspaces that are formed by decomposing the entire dimension into a set of subspaces (bases), based on certain discrimination criterion In this paper the authors present a classification technique that combines the feature level fusion capabilities of lower dimensional subspaces; with decision level fusion to improve the classification potential of hyperspectral data In order to reduce the impact of conflicting decisions by individual bases, a voting scheme called Qualified Majority Voting (QMV) is used in combining the decisions Each base is qualified to influence the final decision, based on its ability to predict the classes with respect to other bases This information can be derived from training data, analyst inputs or feed back from prior applications Unlike the traditional classification approaches, this technique not only utilizes the projected lower dimensional feature space, but also makes use of the reliability of the subspaces in classifying certain classes

Decision level fusion with best-bases for hyperspectral classification

In pursuit of increased reliability and improved signal levels, we employ a scanning approach that supports the high
frequency acquisition requirements of the system while utilizing a large aperture receiver. A continuously rotating
circular scanner was developed using direct inductance drives to rotate a Fresnel prism, acting as synchronized
transmitter-receiver optical element with a fixed incident angle to the sea surface. Circular scanning introduces
challenges in evaluating the system's coverage. By its nature, a circular scanning pattern introduces non-uniformity.
Unlike galvo-based scanners roll and pitch compensation is practically impossible for a large mechanism, therefore
careful planning is required to ensure continuous coverage under realistic operational conditions. Selection of optimal
operation parameters is discussed as well as different ways to evaluate surface coverage in different operation modes.

Utilizing circular scanning in the CZMIL system

CZMIL is an integrated lidar-imagery system and software suite designed for highly automated generation of physical and environmental information products for coastal zone mapping in the framework of the US Army Corps of Engineers (USACE) National Coastal Mapping Program (NCMP). This paper presents the results of CZMIL system validation in turbid water conditions along the Gulf Coast of Mississippi and in relatively clear water conditions in Florida in late spring 2012. Results of the USACE May-October 2012 mission in Green Bay, WI and Lake Erie are presented. The system performance tests show that CZMIL successfully achieved 7-8m depth in Mississippi with Kd =0.46m-1 (Kd is the diffuse attenuation coefficient) and up to 41m in Florida when Kd=0.11m-1. Bathymetric accuracy of CZMIL was measured by comparing CZMIL depths with multi-beam sonar data from Cat Island, MS and from off the coast of Fort. Lauderdale, FL. Validation demonstrated that CZMIL meets USACE specifications (two standard deviation, 2σ, ~30 cm). To measure topographic accuracy we made direct comparisons of CZMIL elevations to GPS-surveyed ground control points and vehicle-based lidar scans of topographic surfaces. Results confirmed that CZMIL meets the USACE topographic requirements (2σ, ~15 cm). Upon completion of the Green Bay and Lake Erie mission there were 89 flights with 2231 flightlines. The general hours of aircraft engine time (which doesn't include all transit/ferry flights) was 441 hours with 173 hours of time on survey flightlines. The 4.8 billion (!) laser shots and 38.6 billion digitized waveforms covered over 1025 miles of shoreline.

CZMIL (coastal zone mapping and imaging lidar): from first flights to first mission through system validation

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