Smart Compression System for Remotely Sensed Images Based on Object-Oriented Image Segmentation

In this paper a method for joint segmentation and compression of remotely sensed images is described. The segmentation task, which is the main topic of this paper, is especially tailored for the identification of Objects Of Interest (OOIs), also called Foreground (FGND) Objects, placed over a non-interesting and homogeneous Background (BGND). These images, collected by satellites or high-altitude platforms, are of particular interest in scientific applications, such as space-borne image analysis, sea observation, regional public services for agriculture, hydrology, fire protection, and so forth. In the case presented here, a suitable compression scheme is then applied to each data stream outcoming from the segmentation block, depending upon its relevance, in order to obtain a selective lossless image compression. Of course, the same segmentation technique can also be a component of many other image processing schemes. An interesting feature of the suggested segmentation method is its versatility and reduced complexity, due to the implementation of the segmentation on the basis of a weighted graph, representing chromatic and morphological features of the regions into which the image is partitioned. The segmentation is based on a step-wise optimization performed with a data-driven decomposition of the image and it is achieved as a region-growing approach based upon the fusion of the best neighbor nodes in the graph. Another important aspect of the proposed technique is its robustness to the variation of represented subjects: neither hypothesis nor restrictions are formulated on the properties of OOIs, because the segmentation procedure identifies the BGND, by using its homogeneity characteristic. Therefore the method can be considered as almost application-independent. Practical applications of the suggested method shown in this paper will demonstrate its effectiveness. Moreover the improvement of Compression Ratio achievable with the proposed technique with respect to classical lossless image compression schemes will be shown on the basis of results obtained on a corpus of images.