Chew Wai Chang

Hyperspectral land cover classification of EO-1 Hyperion data by principal component analysis and pixel unmixing

In this paper, we attempt to perform land cover classification using hyperspectral data acquired by the EO-1 Hyperion instrument over two test sites in the tropical region: one in Singapore and the other one in coastal Jambi on the Sumatra island of Indonesia. Atmospheric correction on the hyperspectral imagery was first performed using a commercial package. Principal component decomposition was then performed and an unsupervised ISODATA classification was carried out on the dominant components to produce a land cover classification map for each test site. Classification using the pixel unmixing method as implemented in the ENVI package was also performed. The results of classification were compared with existing land cover maps.

Multiparameter retrieval of water optical properties from above-water remote-sensing reflectance using the simulated annealing algorithm.

Water-leaving radiance, measured just above the ocean surface, contains important information about near-surface or subsurface processes that occur on or below the deep ocean and coastal water. As such, retrieving seawater inherent optical properties (IOPs) is an important step to determining water type, subsurface light field, turbidity, pigment concentration, and sediment loading. However, the retrieval (or inversion) of seawater IOPs from just above water radiance measurements is a multiparameter nonlinear problem that is difficult to solve by conventional optimization methods. The applicability of the simulated annealing algorithm (SA) is explored as a nonlinear global optimizer to solve this multiparameter retrieval problem. The SA algorithm is combined with widely known semianalytical relations for seawaters IOPs to parameter invert these properties from simulated and measured water-leaving reflectance spectra. Furthermore, given the versatility of the SA algorithm, the scheme is extended to retrieve water depth from input reflectance data. Extensive tests and comparisons with in situ and simulated data sets compiled by the International Ocean-Color Coordinating Group are presented. Field data include reflectance spectra acquired with a handheld GER 1500 spectroradiometer and absorption measurements, performed with the AC-9 instrument on waters around Singapores nearby islands.

Retrieval of inherent optical properties from Landsat ETM+ data: possibilities and limitations

Many studies have been carried out to retrieve water quality measures like concentrations of suspended matter and chlorophyll from Landsat TM images. Such studies typically use extensive in-situ measurements and empirical data fitting (usually correlations) to retrieve concentrations of the water constituent under investigation. One disadvantage of such empirically derived algorithms is that they tend to be applicable only to the water constituent, the particular location and water type investigated. In addition, the atmospheric effects are either not taken into account or a uniform atmosphere is assumed. On the other hand, the backscattering and absorption coefficients, have been shown to determine the water color and hence, the measured reflectance. We report on our attempt to retrieve these inherent optical properties from Landsat ETM+ data. We show how we can correct for the atmospheric effects using Band 4. Using current ocean color models, we retrieved the backscattering and absorption coefficients from Bands 1, 2 and 3 for the coastal waters around Singapore. The derived backscattering and absorption coefficients are within the range of values retrieved using Hyperion data and those from in-situ measurements. The advantage of this method is that minimal empirical data are needed and so it can be easily applied to other similar data sets. However, the precision of the retrieved coefficients is necessarily coarse.

Estimating water optical properties, water depth and bottom albedo using high resolution satellite imagery for coastal habitat mapping

The recently launched Worldview-2 satellite has 8 high resolution (2-m) spectral bands in the visible to near-infrared region. The additional spectral bands provide an opportunity to test the spectral matching algorithm for retrieving the water depth, bottom albedo and intrinsic optical properties of coastal sea water. Our test area is an intertidal zone and reefs near an off-shore island of Singapore consisting of seagrass, corals and mangrove habitats. Examples of applying the spectral matching method to submerged coral reefs are shown. The retrieved water depth and bottom albedo are utilized together with the reflectance spectra of various habitat classes for better classification and mapping of the coastal habitats.

Detecting submerged aquatic vegetation with 8-band WorldView-2 satellite images

In this paper, we investigate the effects of water turbidity and water depth on the task of detecting submerged aquatic vegetation, such as seagrass, using WorldView-2 satellite images. We computed the reflectance spectra of submerged aquatic vegetation using a shallow water reflectance model for various water depth, water turbidity and benthic vegetation cover fraction on sandy substrate. The computed spectra were added with noise and used in a inverse modeling algorithm for retrieving the water depth, optical properties and water column corrected sea bottom reflectance. For low turbidity water (up to 25 NTU), the retrieved water column corrected NDVI values correlate very well with the vegetation cover fraction.

Atmospheric correction of IKONOS with cloud and shadow image features

In this paper, we present a method for atmospheric correction that uses the cloud and shadow image features. An iterative scheme is formulated to computes the ratio of diffuse to direct irradiance as well as the path radiance using the radiance detected over the open water and shadow pixels. These parameters are then used to compute the cloud and water reflectance. We implemented this method on IKONOS images which are known to have inferior signal to noise ratio compared to sensors specially designed for ocean color measurements. The corrected image reflectance over water pixels are compared to field measurements.

Image fusion of Hyperion and Ikonos imagery

We attempted a method of fusing a coarse resolution hyperspectral image and a high spatial resolution image with a few spectral bands to produce a high resolution hyperspectral image, and to extract the spectra of the end-members. The method is based on the linear spectral mixing model and an iterative maximum-likelihood algorithm is used to invert the mixing equation. The effects of noise and misregistration error are investigated. Misregistration seems to be a main factor determining the accuracy of the final products.

Angular variation of parameters relating particulate scattering and remote-sensing reflectance

A set of optimized parameters to compute the underwater remote sensing reflectance from the inherent optical properties covering a wide range of geometries was derived from Hydrolight simulations. The optimized values reduced the errors by 30 % at very steep zenith angles (40°-50°) as compared to published values that were derived for zenith angles of 10°.

Object-oriented land use cover classification of Landsat 8 OLI images in Sumatra

An object oriented approach was developed to derive land use/land cover classification maps over the island of Sumatra, Indonesia. The approach was developed to be used for Landsat 8 data over Sumatra. The accuracy assessment of the classified image used for development is about 90% while the test data yielded 80 %. The method will be applied to the data from Landsat 8 over the Sumatra to generate a 30 m resolution land cover classification map.

Effects of the suspended particle size distribution on water remote-sensing reflectance

The concentration of total suspended sediments (TSS) has often been empirically related to the above-surface remote-sensing reflectance, Rrs(λ). However, since a wide range of particle types exists in natural waters, a given TSS can often be the result of different particle size distributions (PSD). Directly relating TSS to Rrs(λ) disregards the effects of the PSD and may introduce inaccuracies in estimating TSS from Rrs(λ) measurements. This paper describes how the effects of the PSD on Rrs(λ) can be quantified for waters of various TSS, through numerical simulations. Results indicate that depending on the water types under investigation, these inaccuracies may be significant, exceeding 30% in the 500-600nm range in cleaner waters.