Effects of lossy compression on remote sensing image classification of forest areas - Zabala, a; Pons, X. (International Journal of Applied Earth Observation and Geoinformation, 2011)
Lossy compression is being increasingly used in remote sensing; however, its effects on classiﬁcation have scarcely been studied. This paper studies the implications of JPEG (JPG) and JPEG 2000 (J2K) lossy compression for image classiﬁcation of forests in Mediterranean areas. Results explore the impact of the compression on the images themselves as well as on the obtained classiﬁcation. The results indicate that classiﬁcations made with previously compressed radiometrically corrected images and topoclimatic variables are not negatively affected by compression, even at quite high compression ratios. Indeed, JPG compression can be applied to images at a compression ratio (CR, ratio between the size of the original ﬁle and the size of the compressed ﬁle) of 10:1 or even 20:1 (for both JPG and J2K). Nevertheless, the fragmentation of the study area must be taken into account: in less fragmented zones, high CR are possible for both JPG and J2K, but in fragmented zones, JPG is not advisable, and when J2K is used, only a medium CR is recommended (3.33:1 to 5:1). Taking into account that J2K produces fewer artefacts at higher CR, the study not only contributes with optimum CR recommendations, but also found that the J2K compression standard (ISO 15444-1) is better than the JPG (ISO 10918-1) when applied to image classiﬁcation. Although J2K is computationally more expensive, this is no longer a critical issue with current computer technology.
The following values have no corresponding Zotero field: publisher: Elsevier B.V.
Keywords: Forestry management; Image classiﬁcation; Image compression; JPEG; JPEG 2000

Integrating hyperspectral imagery at different scales to estimate component surface temperatures - Archer, N. A. L.; Jones, H. G. (International Journal of Remote Sensing, 2006)
Different methods for classifying land cover and extracting temperatures of surface components from hyperspectral images at different scales were compared using airborne imagery (Reflective Optics System Imaging Spectrometer (ROSIS) at 1.2 m spatial resolution and Digital Airborne Imaging Spectrometer (DAIS 7915) at 3.3 m spatial resolution) for a ?montado/dehesa? landscape in the Alentejo, Portugal. For calibration purposes, surface temperatures and stomatal conductance of component vegetation types were also measured at ground level. Manual classification was compared with a range of automated classification methods to determine the most accurate method for the study area. The ?scale? for each cover type was characterized by analysing the frequency distribution of contiguous pixels of each cover type at 1.2 m. Temperatures of different surface components were estimated using different combinations of 1.2 m and 3.3 m data (using spectral angle mapper classification) as well as linear spectral unmixing and disaggregation approaches for extracting thermal information at sub?pixel resolution. The relative advantages of the different methods are discussed and a recommended strategy for integrating hyperspectral imagery at different scales to extract component surface temperatures in montado/dehesa?type systems is proposed.
doi: 10.1080/01431160500396485doi: 10.1080/01431160500396485The following values have no corresponding Zotero field: publisher: Taylor & Francis
Keywords: Image classiﬁcation; Land cover classification (voyant); Spatial resolution; Surface temperature