To study vegetation from space, both high spatial resolution and high temporal frequency images are needed. However, a satellite sensor, for technological reasons, cannot provide such images. But merge several kinds of images coming from several sensors enables to overgo this problem. In this article, we propose a fusion method based on pyramid algorithms and on morphological filtering to create synthesis images having both high spatial resolution and high temporal frequency. This process is validated by high resolution reference images. The study uses the ADAM database provided with courtesy of Centre National d'Etude Spatiale (CNES - French Space Agency).
The spatial resolution of remotely sensed imaging devices becomes higher and higher. Since the launch of QuickBird, during the year 2001, panchromatic images of 66 cm are acquired. As those improvements have been performed very quickly, methods for an automatic processing of those images are not yet available. The improvement of the spatial resolution enables the detection of new kind of objects. For example, instead of detecting forests, trees are. New applications, notably an accurate survey of the environment during exceptional events (flooding, fire, ...) are conceivable. However, the areas which used to be homogeneous within a 10-meter resolution, are then heterogeneous. Consequently, commonly used methods, such as classification for example, are less efficient. It is urging to propose techniques for an automatic exploitation of this kind of images. In this paper, we propose to add, before the commonly used processes a pre-processing to simplify an image by the diminution of the heterogeneity within regions corresponding to a unique entity, while keeping the borders. For so doing, we compare several filters, linear and non linear. In particular, we use a morphological pyramid based filtering. An example is shown on a QuickBird image acquired over Berne, and comparison of all the filters is done.
The segmentation process of satellite imagery becomes currently a significant step in remote sensing with the arrival of very high spatial resolution images. Indeed, the arrival of these images enables a new capability to study a range of non-observable objects until now. Using high-resolution imagery should make it possible to detect man made features (such as buildings and roads) or detailed components of vegetation (such as trees or heterogeneous woodlands) in an easier way than conventional data. In this paper, we present a brief review of segmentation techniques, the principal advances in earth observation technology, and the evolution of the high-resolution technology. Also, we present a self-adapting method of segmentation of very high-resolution satellite images. This approach is based on a description of the image using graphs of adjacency and morphological processing to obtain suitable and significant computed components by the growth of regions. Finally we present some examples of the segmentation and the feature extraction done in some high-resolution images.
Nowadays, terrestrial dynamics study is more and more often performed with the help of satellite sensors. Usually, vegetation cover surveys are performed with wide field of view sensors, because of their high temporal resolution. However, a high spatial resolution will be appreciable to distinguish each component in a landscape. We propose to create merged images combining both sensors: our fusion method is based on both theories of pyramid algorithms and mathematical morphology. Let call HR (resp. BR) the spatial resolution of the high resolution (resp. coarse) sensor image, for example SPOT 4 HRVIR and VEGETATION. The principle is : 1) To decompose the high resolution image into a low-frequency and several high-frequencies images (HFI). 2) To perform the inverse transform on the HFI images and the coarse resolution sensor data and produce the merged image. Consequently, from a temporal set of VEGETATION data and from a few HRVIR scenes, we are able to create 20m (or less) resolution synthesis data having the temporal repetitivity of the VEGETATION data set.