Luca Cicala

Low-complexity compression of multispectral images based on classified transform coding

Compression of remote-sensing images can be necessary in various stages of the image life, and especially on-board a satellite before transmission to the ground station. Although on-board CPU power is quite limited, it is now possible to implement sophisticated real-time compression techniques, provided that complexity constraints are taken into account at design time. In this paper we consider the class-based multispectral image coder originally proposed in [Gelli and Poggi, Compression of multispectral images by spectral classification and transform coding, IEEE Trans. Image Process. (April 1999) 476-489 [5]] and modify it to allow its use in real time with limited hardware resources. Experiments carried out on several multispectral images show that the resulting unsupervised coder has a fully acceptable complexity, and a rate-distortion performance which is superior to that of the original supervised coder, and comparable to that of the best coders known in the literature.

Detection of environmental hazards through the feature-based fusion of optical and SAR data: a case study in southern Italy

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory.

SAR/multispectral image fusion for the detection of environmental hazards with a GIS

In this paper we propose a GIS-based methodology, using optical and SAR remote sensing data, together with more conventional sources, for the detection of small cattle breeding areas, potentially responsible of hazardous littering. This specific environmental problem is very relevant for the Caserta area, in southern Italy, where many small buffalo breeding farms exist which are not even known to the productive activity register, and are not easily monitored and surveyed. Experiments on a test area, with available specific ground truth, prove that the proposed systems is characterized by very large detection probability and negligible false alarm rate.

Sensor aided H.264 Video Encoder for UAV applications

This paper presents a new low-complexity H.264 encoder for Unmanned Aerial Vehicles (UAV) applications. Standard video coding systems currently employed in UAV applications do not rely on some peculiarities in terms of scene 3D model and correlation among successive frames. In particular, the observed scene is static, i.e. the camera movement is dominant, and it can often be well approximated with a plane. Moreover, camera position and orientation can be obtained from the navigation system. Therefore, correspondent points on two video frames are linked by a simple homography. The encoder employs a new motion estimation scheme which make use of the global motion information provided by the onboard navigation system. The homography is used in order to initialize the block matching algorithm allowing a more robust motion estimation and a smaller search window, and hence reducing the complexity. Experimental results show that the proposed scheme ouperforms standard H.264 in terms of PSNR and throughput. The results are relevant in low frame rate video coding, which is a typical scenario in UAV behind line-of-sight (BLOS) missions. Experiments open new drections in developing new sensor aided video coding standards.

Supervised segmentation of remote-sensing multitemporal images based on the tree-structured Markov random field model

We deal with the supervised segmentation of multi-temporal remote-sensing images following a statistical Bayesian approach. To take into account prior information on the class of images, like the correlation between neighboring pixels, as well as the available knowledge about the structure of the current image, we model the image as a tree-structured Markov random field. The data collected at two different dates are jointly processed as a single multi-component image, with the classes defined a priori based on ground truth information and grouped in changed and unchanged macro-classes. Experimental results in terms of classification accuracy prove the effectiveness of the proposed technique with respect to non-contextual methods, as well as to a disjoint approach. In addition, the classification tree allows for a direct interpretation of the result

REMOTE SENSING IN THE FIGHT AGAINST ENVIRONMENTAL CRIMES: THE CASE STUDY OF THE CATTLE-BREEDING FACILITIES IN SOUTHERN ITALY

Enforcement of environmental regulation is a persistent challenge and timely detection of the violations is key to holding the violators accountable. The use of remote sensing data is becoming an effective practice in the fight against environmental crimes. In this work, a novel and effective approach for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multispectral data together with geospatial analyses in the geographic information system (GIS) environment, is proposed. Experiments on data available for the area of Caserta (Southern Italy), show that the proposed technique provides very high detection capability, up to 90%, with a acceptable false alarm rate, becoming a useful tool in the hand of agencies engaged in the protection of territory.

A sensor aided H.264 encoder tested on aerial imagery for SFM

Standard video coding systems currently employed in UAV (Unmanned Aerial Vehicle) and aerial drone applications do not rely on some peculiarities in terms of scene 3D model and correlation among successive frames. In particular, the observed scene is static, i.e. the camera movement is dominant, and it can often be well approximated with a plane. Moreover, camera position and orientation can be obtained from the navigation system. Therefore, correspondent points on two video frames are linked by a simple homography. This paper presents novel results obtained by a low-complexity sensor aided H.264 encoder, recently developed at CIRA and yet tested on simulated data. The proposed encoder employs a new motion estimation scheme which make use of the global motion information provided by the onboard navigation system. The homography is used in order to initialize the block matching algorithm allowing a more robust motion estimation and a smaller search window, and hence reducing the complexity. The tests are made coding real aerial imagery, captured to be used for 3D scene reconstruction. The images are acquired by an high resolution camera mounted on a small drone, flying at low altitude.

An H.264 Sensor Aided Encoder for Aerial Video Sequences with In-the-Loop Metadata Enhancement

Unmanned Aerial Vehicles UAVs are often employed to capture high resolution images in order to perform image mosaicking and/or 3D reconstruction. Images are usually stored on-board or sent to the ground using still image or video data compression. Still image encoders are preferred when low frame rates are involved, because video coding systems are based on motion estimation and compensation algorithms which fail when the motion vectors are significantly long. The latter is the case of low frame rate videos, in which the overlapping between subsequent frames is very small. In this scenario, UAVs attitude and position metadata from the Inertial Navigation System INS can be employed to estimate global motion parameters without video analysis. However, a low complexity analysis can refine the motion field estimated using only the metadata. In this work, we propose to use this refinement step in order to improve the position and attitude estimation produced by the navigation system with the aim of maximizing the encoder performance. Experiments on both simulated and real world video sequences confirm the effectiveness of the proposed approach.

H.264 Sensor Aided Video Encoder for UAV BLOS Missions

This paper presents a new low-complexity H.264 encoder, based on x264 implementation, for Unmanned Aerial Vehicles (UAV) applications. The encoder employs a new motion estimation scheme which make use of the global motion information provided by the onboard navigation system. The results are relevant in low frame rate video coding, which is a typical scenario in UAV behind line-of-sight (BLOS) missions.

Estimated post-flood effects through free, full and open satellite remote sensing data to support civil protection

A flood is a relatively high flow of water that overtops the natural and artificial banks in any of the reaches of a stream. When banks are overtopped, water spreads over flood plain and generally causes problem for inhabitants, crops and vegetation. During extreme flood events, it is important to determine quickly the extent of flooding and land use under water. Flood maps can be applied to develop comprehensive relief effort immediately after flooding. There are varieties of issues and uncertainties involved in flood mapping. Remotely sensed data can be used to develop flood map in an efficient and effective way. In this paper different techniques of flood mapping using active and passive remote sensing systems are applied, so that a final flood inundation map has been prepared by combining all data within a Geographical Information System (GIS) environment. The flood inundation maps can be further used for quick identification of areas of potential flood hazard to minimize the flood losses.